NOAA langoliers eat another 1/3 of stations from GHCN database

Dallas Fort Worth airport is one of hundreds of GHCN reporting stations gone missing. GHCN stations are becoming an endangered species.

Updated frame from the movie "The Langoliers" 1995, - with apologies to Steven King's book

 

2010 Thermometer Langoliers Hit List

Guest post by E.M.Smith

Well, They Are At It Again

UPDATE 12 Feb 2010:  Dallas has been found in the Failed QA file.  Also, the “process” was discovered described in the ClimateGate emails by Phil Jones (see comments).  The final update date is ill defined at best and this list ought to be treated as a “missing in action” list until the end of the month as we wait to see if further updates are applied.  -E.M. Smith

Don’t know what to make of this list yet, other than it directly ‘gives the lie’ to the assertion that thermometer ‘drops’ were / are entirely an artifact of GHCN being a creation at a historical moment in time (i.e. made in 1990’s era so that’s why they drop out then in The Great Dying of Thermometers – which itself ignores The Lesser Dying in 2006).

It also shows that the excuse of things being dropped for not electronically reporting is pretty much a lie, too. I note that Dallas Fort Worth Airport is on this list and I’m pretty sure they have electronic reporting… From the NASA / GISS web site, as confirmation:

(*) Dallas-Fort W 32.9 N 97.0 W 425722590000 4,037,000 1947 – 2009

Note the end date of 2009.

And Strasbourg airport is on the list too, so it’s not just an America thing…

I’ve not examined this list for any patterns, nor re-done any of the prior “by latitude” and “by altitude” reports to see what the changes do to the world. For now, it’s just another “Dig Here” list. (Though a casual look at the altitude field shows a fair number of 1000m and 2000m stations died.)

Oh, and the list is also confirmation that the extraordinary hatred of thermometers shown by the managers of GHCN continues, unabated. Particular emphasis seems to have landed on Africa (already poorly covered) and Asia, with a modest effort to eradicate more of South America. By comparison, Europe is only slightly mauled…

[chiefio@Hummer data]$ wc -l 2009_uniq_station_list
1597 2009_uniq_station_list
[chiefio@Hummer data]$ wc -l 2010_uniq_station_list
1113 2010_uniq_station_list

So from 1597 we drop to 1113. That’s a drop of 30%.

Just shy of 1/3 of the stations, taken out back and shot this year.

But once you decided that you can just make up any missing data, then who needs to actually read the thermometers any more?

Ok, enough of my complaint. Here is the list. If anyone notices anything interesting about their part of the world, feel free to let us all know. Remember that the StationID (that first field) is structured as 1 digit of continent then 2 that tell the particular country, then 8 for the particular station and substation. So records that start with 1 are Africa, 2 Asia, 3 South America, 4 North America, 5 Pacific, 6 Europe, 7 Antarctica, 8 Ships at sea (a very few geographic spots with few records as a ship happens by and reports). I will break up the list into groups by continent, but notice that there are no “8″ stations on the list and only one from Antarctica. Oh, and there were 2 stations added, so I’ll list them here at the top:

Added Stations

11365563000 YAMOUSSOUKRO                     6.90   -5.35  213  168U   50HIxxLA-9A15TROP. SEASONAL  A
11365594000 SAN PEDRO                        4.75   -6.65   30    0R   -9FLFOCO 2A-9EQ. EVERGREEN   C

Antarctic Deletion:

70089642000 DUMONT D'URVI                  -66.67  140.02   43  150R   -9MVICCO 1x-9ANTARCTICA      A

The Requiem List

Africa

10160355000 SKIKDA                          36.93    6.95    7   18U  107HIxxCO 1x-9WARM DECIDUOUS  C
10160403000 GUELMA                          36.47    7.47  227  287S   47HIxxno-9x-9WARM CROPS      C
10160430000 MILIANA                         36.30    2.23  715 1167R   -9MVDEno-9x-9WARM DECIDUOUS  C
10160444000 BORDJ BOU ARR                   36.07    4.77  928 1051U   57MVxxno-9x-9WARM FOR./FIELD C
10160445000 SETIF                           36.18    5.42 1081 1060U  144MVxxno-9x-9WARM FOR./FIELD C
10160457000 MOSTAGANEM VI                   35.88    0.12  137  157U  102HIxxCO 4A 2WARM CROPS      B
10160468000 BATNA                           35.55    6.18 1052 1015U   85MVxxno-9x-9WARM DECIDUOUS  C
10160518000 BENI-SAF                        35.30   -1.35   68  103R   -9HIDECO 1x-9WARM CROPS      B
10160535000 DJELFA                          34.68    3.25 1144 1071U   51HIxxno-9x-9MED. GRAZING    C
10160536000 SAIDA                           34.87    0.15  750  802U   62MVxxno-9A 4MED. GRAZING    C
10160540000 EL KHEITER                      34.15    0.07 1000 1113R   -9FLDEno-9x-9WARM DECIDUOUS  B
10160549000 MECHERIA            ALGERIA     33.60   -0.30  176 1501R   -9MVDEno-9A-9MED. GRAZING    A
10160555000 TOUGGOURT                       33.12    6.13   85  106U   76FLxxno-9x-9HOT DESERT      B
10160560000 AIN SEFRA                       32.77   -0.60 1058 1811R   -9MVDEno-9x-9WARM GRASS/SHRUBA
10160602000 BENI ABBES                      30.13   -2.17  499  520R   -9HIDEno-9A-9SAND DESERT     C
10764910000 DOUALA OBS.                      4.00    9.73    9   12U  458FLxxCO 5A 1MARSH, SWAMP    C
10964655000 BRIA                             6.53   21.98  584  587S   25FLxxno-9A 1TROP. SAVANNA   A
11064700000 NDJAMENA                        12.13   15.03  295  294U  179FLxxno-9A 2WARM GRASS/SHRUBC
11161968000 ILES GLORIEUS                  -11.58   47.28    4    0R   -9FLxxCO 1A-9WATER           A
11161970000 ILE JUAN DE N                  -17.05   42.70   10    0R   -9FLxxCO 1A-9WATER           A
11161972000 ILE EUROPA                     -22.32   40.33   13    0R   -9FLxxCO 1A-9WATER           A
11264400000 POINTE-NOIRE                    -4.82   11.90   17    7U  142FLxxCO 4A 2WATER           B
11264401000 LOUBOMO                         -4.20   12.70  330  321S   20HIxxno-9A 1TROP. SAVANNA   C
11264402000 MOUYONDZI                       -3.98   13.92  512  321R   -9HIxxno-9x-9TROP. SAVANNA   A
11264405000 SIBITI                          -3.68   13.35  531  481R   -9HIxxno-9A-9TROP. SAVANNA   A
11264450000 BRAZZAVILLE /                   -4.25   15.25  316  303U  299FLxxno-9A 2WARM CROPS      C
11264454000 GAMBOMA                         -1.87   15.87  377  314R   -9FLxxno-9A-9TROP. SEASONAL  A
11365578000 ABIDJAN                          5.25   -3.93    8   26U  686FLxxCO 1A 5COASTAL EDGES   C
11365585000 ADIAKE                           5.30   -3.30   39   25R   -9FLxxCO 3x-9WARM FOR./FIELD A
11763331000 GONDAR                          12.53   37.43 1966 2002S   39MVxxno-9A10TROP. MONTANE   A
11763333000 COMBOLCHA                       11.08   39.72 1916 1465U   50MVxxno-9A10HIGHLAND SHRUB  B
11763402000 JIMMA                            7.67   36.83 1676 1776S   40MVxxno-9A 2TROP. MONTANE   B
11763403000 GORE                             8.17   35.55 1974 1760R   -9HIFOno-9A-9TROP. MONTANE   A
11763450000 ADDIS ABABA                      8.98   38.80 2324 2586U 1196MVxxno-9A 2WARM CROPS      C
11763471000 DIRE DAWA                        9.60   41.87 1146 1239U   64MVxxno-9A 2TROPICAL DRY FORC
12263612000 LODWAR                           3.12   35.62  515  431R   -9FLxxno-9A-9WARM GRASS/SHRUBA
12263740000 NAIROBI/KENYA                   -1.32   36.92 1624 1634U  509HIxxno-9A 2WARM FIELD WOODSC
12462002000 NALUT                           31.87   10.98  621  570S   24HIxxno-9x-9WARM GRASS/SHRUBC
12462007000 ZUARA                           32.88   12.08    3    5S   20FLxxCO 1x-9HIGHLAND SHRUB  C
12462008000 YEFREN                          32.08   12.55  691  575R   -9HIDEno-9x-9WARM GRASS/SHRUBB
12462010000 TRIPOLI             LIBYA       32.90   13.20   84    9U  550HIxxCO 3x-9MED. GRAZING    C
12462012000 EL KHOMS                        32.63   14.30   22   12S   17HIxxCO 2x-9WATER           C
12462016000 MISURATA                        32.42   15.05   32    5U  102FLxxCO 4x-9WARM GRASS/SHRUBC
12462019000 SIRTE                           31.20   16.58   14   16S   23FLxxCO 1x-9WATER           C
12462053000 BENINA                          32.10   20.27  132  152U  287HIxxCO20A16WARM CROPS      C
12462055000 AGEDABIA                        30.72   20.17    7    6U   53FLxxCO25x-9WARM GRASS/SHRUBB
12462056000 SHAHAT                          32.82   21.85  625  475S   17HIxxCO11x-9WARM CROPS      C
12462059000 DERNA                           32.78   22.58   26  138S   44HIxxCO 1x-9WATER           B
12462103000 GHADAMES                        30.13    9.50  347  273R   -9HIDEno-9x-9WARM GRASS/SHRUBC
12462124000 SEBHA                           27.02   14.45  432  424S   35FLxxno-9A 5SAND DESERT     C
12462131000 HON                             29.12   15.95  267  256R   -9HIDEno-9A-9HOT DESERT      A
12462161000 JALO                            29.03   21.57   60  109R   -9FLDEno-9x-9HOT DESERT      A
12462176000 GIARABUB                        29.75   24.53   -1   45R   -9FLDEno-9x-9HOT DESERT      B
12462271000 KUFRA                           24.22   23.30  436  406R   -9FLDEno-9A-9HOT DESERT      C
12667693000 CHILEKA                        -15.68   34.97  767  802U  222MVxxno-9A10TROPICAL DRY FORB
12761214000 KIDAL                           18.43    1.35  459  431R   -9HIDEno-9A-9SUCCULENT THORNSA
12761270000 KITA                            13.07   -9.47  334  381S   18HIxxno-9A 3WARM CROPS      A
12761285000 KENIEBA                         12.85  -11.23  132  306R   -9HIFOno-9x-9TROP. SAVANNA   A
12761297000 SIKASSO                         11.35   -5.68  375  398S   47HIxxno-9A 2WARM CROPS      B
12861421000 ATAR                            20.52  -13.07  224  261S   16FLxxno-9x-9HOT DESERT      B
12861437000 AKJOUJT                         19.75  -14.37  120  134R   -9FLDEno-9x-9HOT DESERT      A
12861461000 BOUTILIMIT                      17.53  -14.68   75   41R   -9FLDEno-9x-9WARM GRASS/SHRUBA
13167215000 PORTO AMELIA                   -13.00   40.50   50   13R   -9HIxxCO 2A-9WATER           A
13167217000 VILA CABRAL                    -13.30   35.30 1365 1359S   10HIxxno-9x-9WARM CROPS      A
13167237000 NAMPULA                        -15.10   39.28  441  435S   23HIxxno-9A 1TROP. SAVANNA   C
13167261000 TETE                           -16.18   33.58  150  187R   -9HIxxno-9x-9SUCCULENT THORNSA
13167283000 QUELIMANE                      -17.88   36.88   16    7S   11FLxxCO10x-9MARSH, SWAMP    B
13167297000 BEIRA                          -19.80   34.90   16    6S   46FLxxCO 2A 5MARSH, SWAMP    C
13167323000 INHAMBANE                      -23.87   35.38   15   22R   -9FLxxCO 2A-9WARM CROPS      B
13167341000 LOURENCO MARQUES/COUNTINHO     -25.90   32.60   44   18U  755FLxxCO 7A 4MARSH, SWAMP    C
13268312000 KEETMANSHOOP                   -26.53   18.12 1061  961S   10FLxxno-9x-9WARM GRASS/SHRUBA
13361090000 ZINDER                          13.78    8.98  453  445U   58FLxxno-9A 1SUCCULENT THORNSA
13761679000 KAOLACK                         14.13  -16.07    7    8U  107FLxxno-9x-9WARM CROPS      B
14168262000 PRETORIA                       -25.73   28.18 1322 1377U  573HIxxno-9x-9WARM CROPS      C
14168438000 KIMBERLEY                      -28.80   24.77 1200 1213U  105FLxxno-9A 3WARM GRASS/SHRUBC
14168588000 DURBAN (LOUIS                  -29.97   30.95   14   23U  975HIxxCO 2A 1WATER           C
14168842000 PORT ELIZABET                  -33.98   25.60   61   63U  414HIxxCO 4A 1WATER           C
14361997000 CROZET                         -46.43   51.87  143    0R   -9HIxxCO 1x-9WATER           A
14361998000 PORT-AUX-FRAN                  -49.35   70.25   30  173R   -9HIxxCO 1x-9WATER           A
14761901000 ST. HELENA IS.                 -16.00   -5.70  627    0R   -9HIxxCO 1x-9WATER           A
14862600000 WADI HALFA                      21.92   31.32  126  192R   -9FLDELA-9x-9WARM IRRIGATED  A
14862640000 ABU HAMED                       19.53   33.32  312  231R   -9FLDEno-9x-9HOT DESERT      A
14862641000 PORT SUDAN                      19.58   37.22    2   16U  133FLxxCO 1x-9COASTAL EDGES   C
14862650000 DONGOLA                         19.17   30.48  226  229R   -9FLDEno-9x-9WARM IRRIGATED  C
14862660000 KARIMA                          18.55   31.85  249  242S   13HIxxno-9x-9WARM IRRIGATED  C
14862680000 ATBARA                          17.70   33.97  345  288U   66FLxxno-9x-9WARM GRASS/SHRUBC
14862721000 KHARTOUM                        15.60   32.55  380  239U 1334FLxxno-9A 1WARM IRRIGATED  C
14862730000 KASSALA                         15.47   36.40  500  518U   99HIxxno-9x-9WARM GRASS/SHRUBC
14862733000 HALFA EL GEDI                   15.32   35.60  451  501R   -9FLDEno-9x-9WARM IRRIGATED  C
14862750000 ED DUEIM                        14.00   32.33  378  261S   27FLxxno-9A 1WARM GRASS/SHRUBB
14862751000 WAD MEDANI                      14.40   33.48  408  313U  107FLxxno-9x-9WARM GRASS/SHRUBC
14862752000 GEDAREF                         14.03   35.40  599  544U   92FLxxno-9x-9SUCCULENT THORNSC
14862760000 EL FASHER                       13.62   25.33  730  758U   52FLxxno-9A 1WARM GRASS/SHRUBB
14862762000 SENNAR                          13.55   33.62  418  415S   10FLxxLA-9x-9SUCCULENT THORNSC
14862771000 EL OBEID                        13.17   30.23  574  574U   90FLxxno-9A 1WARM GRASS/SHRUBC
14862772000 KOSTI                           13.17   32.67  381  367U   57FLxxno-9x-9WARM GRASS/SHRUBC
14862795000 ABU NA'AMA                      12.73   34.13  445  379R   -9FLDEno-9x-9WARM GRASS/SHRUBA
14862805000 DAMAZINE                        11.78   34.38  470  471R   -9FLxxLA-9A-9SUCCULENT THORNSA
14862810000 KADUGLI                         11.00   29.72  499  523S   18FLxxno-9x-9WARM GRASS/SHRUBC
14862880000 WAU                              7.70   28.02  438  433U   53FLxxno-9x-9TROP. SAVANNA   A
14963971000 MTWARA                         -10.27   40.18  113   74S   49HIxxCO 3x-9TROP. SEASONAL  B
15061701000 BATHURST/YUNDUM                 13.40  -16.70   26    6S   39FLxxCO 6A15COASTAL EDGES   B
15165351000 DAPAON                          10.87    0.25  330  279R   -9HIxxno-9A-9WARM GRASS/SHRUBA
15165352000 MANGO                           10.37    0.47  146  136R   -9FLxxno-9A-9WARM GRASS/SHRUBA
15165355000 NIAMTOUGOU                       9.77    1.10  343  340R   -9HIxxno-9A-9WARM GRASS/SHRUBA
15165357000 KARA                             9.55    1.17  341  308R   -9HIxxno-9x-9WARM GRASS/SHRUBB
15165361000 SOKODE                           8.98    1.15  387  419S   30HIxxno-9x-9TROPICAL DRY FORC
15165376000 ATAKPAME                         7.58    1.12  402  427R   -9HIxxno-9x-9TROP. SAVANNA   A
15165380000 TABLIGBO                         6.58    1.50   44   67R   -9FLxxno-9x-9WARM FOR./FIELD A
15165387000 LOME                             6.17    1.25   25   17U  148FLxxCO 5A 1WARM FOR./FIELD C
15567633000 MONGU                          -15.25   23.15 1053 1023R   -9FLxxno-9A-9TROPICAL DRY FORB
15567663000 KABWE                          -14.45   28.47 1207 1195U  144HIxxno-9x-9SUCCULENT THORNSC
15567743000 LIVINGSTONE                    -17.82   25.82  986  970U   72FLxxno-9A 2SUCCULENT THORNSA
15761996000 ILE NOUVELLE-AMSTERDAM         -37.80   77.50   28    0R   -9HIxxCO 1x-9WATER           A
15960010000 IZANA                           28.30  -16.50 2368 1591R   -9MTxxCO12x-9WATER           B
16367005000 DZAOUDZI/PAMA                  -12.80   45.28    7   36R   -9FLxxCO 1A-9WATER           B
16561980000 SAINT-DENIS/G                  -20.88   55.52   25  239U   80HIxxCO 1A 1WATER           C
16861976000 SERGE-FROLOW                   -15.88   54.52   13    0R   -9FLxxCO 1A-9WATER           A

Asia

20140948000 KABUL AIRPORT                   34.55   69.22 1791 2290U  534MVxxno-9A 2WARM FIELD WOODSA
20140990000 KANDAHAR AIRP                   31.50   65.85 1010 1008U  180HIxxno-9A15HOT DESERT      A
20550527000 HAILAR                          49.22  119.75  611  630U  120FLxxno-9A 1COOL FIELD/WOODSC
20550963000 TONGHE                          45.97  128.73  110  477S   20HIxxno-9x-9COOL CROPS      C
20551243000 KARAMAY                         45.60   84.85  428  354R   -9HIDEno-9x-9WARM GRASS/SHRUBC
20551644000 KUQA                            41.72   82.95 1100 1300U  103HIxxno-9x-9WARM GRASS/SHRUBC
20551656000 KORLA                           41.75   86.13  933 1189S   46FLxxno-9x-9SAND DESERT     C
20552267000 EJIN QI                         41.95  101.07  941 1220R   -9HIDEno-9x-9HOT DESERT      A
20552323000 MAZONG SHAN                     41.80   97.03 1770 1906R   -9HIDEno-9x-9HOT DESERT      A
20552418000 DUNHUANG                        40.15   94.68 1140 1066U   55FLxxno-9x-9COOL IRRIGATED  B
20552495000 BAYAN MOD                       40.75  104.50 1329 1220R   -9HIDEno-9x-9HOT DESERT      A
20552681000 MINQIN                          38.63  103.08 1367 1520R   -9FLDEno-9x-9SAND DESERT     B
20552866000 XINING                          36.62  101.77 2262 2376U  250MVxxno-9x-9WARM GRASS/SHRUBC
20553336000 HALIUT                          41.57  108.52 1290 1317R   -9HIDEno-9x-9HOT DESERT      B
20553845000 YAN AN                          36.60  109.50  959 1156R   -9HIxxno-9A-9WARM GRASS/SHRUBC
20554026000 JARUD QI                        44.57  120.90  266  300R   -9FLDEno-9x-9COOL CROPS      C
20554102000 XILIN HOT                       43.95  116.07  991 1079S   40FLxxno-9x-9COOL GRASS/SHRUBC
20554161000 CHANGCHUN                       43.90  125.22  238  283U 1500FLxxno-9x-9COOL FIELD/WOODSC
20554218000 CHIFENG                         42.27  118.97  572  648U   90HIxxno-9x-9COOL CROPS      C
20554662000 DALIAN                          38.90  121.63   97   39U 1480HIxxCO 2x-9WATER           C
20554823000 JINAN                           36.68  116.98   58   60U 1500HIxxno-9x-9WARM FOR./FIELD C
20555228000 SHIQUANHE                       32.50   80.08 4279 4597R   -9MVxxno-9x-9WARM DECIDUOUS  A
20555472000 XAINZA                          30.95   88.63 4670 5205R   -9MVxxno-9x-9TUNDRA          A
20555591000 LHASA                           29.67   91.13 3650 4813U  175MVxxno-9x-9SIBERIAN PARKS  C
20556004000 TUOTUOHE                        34.22   92.43 4535 4606R   -9MVxxno-9x-9TUNDRA          A
20556029000 YUSHU                           33.02   97.02 3682 5078U   80MVxxno-9x-9TUNDRA          A
20556046000 DARLAG                          33.75   99.65 3968 4017R   -9MVxxno-9x-9SIBERIAN PARKS  A
20556079000 RUO'ERGAI                       33.58  102.97 3441 3622R   -9MVxxno-9x-9WARM CROPS      A
20556106000 SOG XIAN                        31.88   93.78 4024 5015R   -9MVxxno-9x-9TUNDRA          A
20556444000 DEQEN                           28.50   98.90 3488 3349R   -9MVxxno-9x-9TUNDRA          A
20556964000 SIMAO                           22.77  100.98 1303 1336R   -9MVxxno-9x-9WARM DECIDUOUS  B
20557127000 HANZHONG                        33.07  107.03  509  611U  120MVxxno-9x-9WARM DECIDUOUS  C
20557494000 WUHAN                           30.62  114.13   23   60U 4250FLxxno-9x-9PADDYLANDS      C
20557516000 CHONGQING                       29.52  106.48  351  352U 3500HIxxno-9x-9PADDYLANDS      C
20557816000 GUIYANG                         26.58  106.72 1074 1289U 1500FLxxno-9x-9WARM GRASS/SHRUBC
20558027000 XUZHOU                          34.28  117.15   42   60U 1500HIxxno-9A 1WARM CROPS      C
20558238000 NANJING                         32.00  118.80   12  100U 2000HIxxno-9x-9PADDYLANDS      C
20558633000 QU XIAN                         28.97  118.87   71  303U   60MVxxno-9x-9WARM MIXED      C
20558666000 DACHEN DAO                      28.45  121.88   84    0R   -9HIxxCO 1x-9WATER           A
20558847000 FUZHOU                          26.08  119.28   85  199U  900HIxxCO30x-9PADDYLANDS      C
20559211000 BAISE                           23.90  106.60  242  268R   -9HIxxno-9x-9PADDYLANDS      C
20559948000 YAXIAN                          18.23  109.52    7   48R   -9HIxxCO 1A-9WATER           C
20559981000 XISHA DAO                       16.83  112.33    5    0R   -9FLxxCO 1x-9WATER           A
20647014000 CHUNGGANG                       41.78  126.88  332  543R   -9HIxxno-9x-9COOL MIXED      A
20647016000 HYESAN                          41.40  128.17  714  882S   20MVxxno-9x-9WARM FOR./FIELD C
20647025000 KIMCHAEK                        40.67  129.20   19  272U  150HIxxCO 1x-9WARM GRASS/SHRUBC
20647035000 SINUIJU                         40.10  124.38    7   30U  300HIxxno-9x-9WARM MIXED      C
20647055000 WONSAN                          39.18  127.43   36   29U  275HIxxCO 1x-9WARM GRASS/SHRUBB
20647058000 PYONGYANG                       39.03  125.78   38   22U 1250FLxxno-9x-9WARM CROPS      C
20647069000 HAEJU                           38.03  125.70   81   93U  140MVxxCO 1x-9WARM CROPS      C
20742071000 AMRITSAR                        31.63   74.87  234  217U  458FLxxno-9x-9WARM IRRIGATED  C
20742475000 ALLAHABAD/BAM                   25.45   81.73   98   90U  513FLxxno-9A 4WARM CROPS      C
20742587000 DALTONGANJ                      24.05   84.07  221  279S   43HIxxno-9x-9WARM CROPS      C
20840706000 TABRIZ                          38.08   46.28 1361 1479U  599MVxxno-9x-9WARM GRASS/SHRUBC
20840712000 ORUMIEH                         37.53   45.08 1312 1402U  164MVxxno-9x-9HOT DESERT      C
20840729000 ZANJAN                          36.68   48.48 1663 1752U  100MVxxno-9x-9HIGHLAND SHRUB  C
20840731000 GHAZVIN                         36.25   50.00 1278 1284U  139FLxxno-9x-9COOL FOR./FIELD C
20840738000 GORGAN                          36.82   54.47  155  280U   88MVxxno-9x-9WARM MIXED      C
20840745000 MASHHAD                         36.27   59.63  980 1035U  670MVxxno-9x-9HIGHLAND SHRUB  C
20840747000 SANANDAJ                        35.33   47.00 1373 1650U   96MVxxno-9x-9WARM GRASS/SHRUBC
20840754000 TEHRAN-MEHRAB                   35.68   51.35 1191 1230U 4496MVxxno-9A 1HIGHLAND SHRUB  C
20840757000 SEMNAN                          35.55   53.38 1171 1406S   31MVxxno-9x-9HIGHLAND SHRUB  C
20840766000 KRMANSHAH                       34.27   47.12 1322 1482U  291MVxxno-9x-9WARM CROPS      B
20840769000 ARAK                            34.10   49.40 1720 1941U  115MVxxno-9x-9WARM GRASS/SHRUBB
20840798000 SHAHRE-KORD                     32.33   50.85 1991 2436S   24MVxxno-9x-9WARM FIELD WOODSC
20840800000 ESFAHAN                         32.47   51.72 1550 1620U  672HIxxno-9A 1HOT DESERT      B
20840809000 BIRJAND                         32.87   59.20 1491 1528S   26MVxxno-9x-9WARM FIELD WOODSC
20840841000 KERMAN                          30.25   56.97 1754 2096U  140MVxxno-9A 5COOL GRASS/SHRUBC
20840848000 SHIRAZ                          29.53   52.58 1491 1909U  416MVxxno-9A 3HIGHLAND SHRUB  C
20840856000 ZAHEDAN                         29.47   60.88 1370 1580U   93HIxxno-9x-9WARM FIELD WOODSC
20840875000 BANDARABBASS                    27.22   56.37   10  149U   89FLxxCO 3A 3WARM GRASS/SHRUBC
21047582000 AKITA                           39.72  140.10   21   12U  261FLxxCO 3x-9PADDYLANDS      C
21128952000 KUSTANAI                        53.22   63.62  156  180U  165FLxxno-9x-9COOL CROPS      C
21135746000 ARALSKOE MORE                   46.78   61.65   62   33S   38FLxxLA-9x-9WARM GRASS/SHRUBC
21135925000 SAM                             45.40   56.12   82  117R   -9FLDEno-9x-9HOT DESERT      A
21136859000 PANFILOV                        44.17   80.07  645  953S   19MVxxno-9x-9WARM GRASS/SHRUBC
21138001000 FORT SEVCENKO                   44.55   50.25  -25    1S   12FLxxCO 1x-9WATER           B
21448930000 LUANG-PRABANG                   19.88  102.13  305  673R   -9MVxxno-9A-9TROP. SEASONAL  B
21544203000 RINCHINLHUMBE                   51.12   99.67 1583 1720R   -9MVxxno-9x-9SOUTH. TAIGA    C
21544207000 HATGAL                          50.43  100.15 1668 1816R   -9MVxxLA-9x-9SOUTH. TAIGA    A
21544213000 BARUUNTURUUN                    49.65   94.40 1232 1318R   -9MVDEno-9x-9TUNDRA          A
21544214000 UIGI                            48.93   89.93 1715 2256S   15MVxxno-9x-9HOT DESERT      A
21544215000 OMNO-GOBI                       49.02   91.72 1590 1903R   -9MVxxLA-9x-9COOL DESERT     A
21544218000 HOVD                            48.02   91.57 1405 1574S   25MVxxno-9x-9HOT DESERT      A
21544225000 TOSONTSENGEL                    48.73   98.28 1723 2062R   -9MVxxno-9x-9COOL DESERT     A
21544230000 TARIALAN                        49.57  102.00 1235 1317R   -9MVxxno-9x-9SOUTH. TAIGA    A
21544231000 MUREN                           49.57  100.17 1283 1659S   20MVxxno-9x-9COOL DESERT     A
21544232000 HUTAG                           49.38  102.70  938 1280R   -9MVxxno-9x-9COOL DESERT     A
21544237000 ERDENEMANDAL                    48.53  101.38 1510 1801R   -9MVxxno-9x-9COOL DESERT     A
21544239000 BULGAN                          48.80  103.55 1208 1484S   15HIxxno-9x-9COOL GRASS/SHRUBA
21544241000 BAYAN-GOL, SELENGE              48.90  106.10  807  914R   -9HIxxno-9x-9COOL GRASS/SHRUBA
21544259000 CHOIBALSAN                      48.08  114.55  747  910S   30FLxxno-9x-9COOL GRASS/SHRUBB
21544272000 ULIASTAI                        47.75   96.85 1759 2525S   15MVxxno-9x-9TUNDRA          A
21544277000 ALTAI                           46.40   96.25 2181 2716S   14MVxxno-9x-9WARM GRASS/SHRUBA
21544282000 TSETSERLEG                      47.45  101.47 1691 2290S   28HIxxno-9x-9COOL DESERT     A
21544284000 GAIUUT                          46.70  100.13 2126 2135S   10MVxxno-9x-9TUNDRA          A
21544285000 HUJIRT                          46.90  102.77 1662 1898R   -9HIDEno-9x-9COOL GRASS/SHRUBA
21544287000 BAYANHONGOR                     46.13  100.68 1859 1980S   10MVxxno-9x-9COOL DESERT     A
21544288000 ARVAIHEER                       46.27  102.78 1813 1819S   12HIxxno-9x-9COOL GRASS/SHRUBA
21544292000 DAUUNMOD, CENTRAL               47.80  106.80 -999 1520S   12HIxxno-9x-9COOL GRASS/SHRUBA
21544294000 MAANTI                          47.30  107.48 1430 1520R   -9FLDEno-9x-9COOL GRASS/SHRUBA
21544298000 CHOIR                           46.45  108.22 1286 1520R   -9FLDEno-9x-9COOL GRASS/SHRUBA
21544302000 BAYAN-OVOO                      47.78  112.12  926  996R   -9FLDEno-9x-9COOL GRASS/SHRUBA
21544304000 UNDERKHAAN                      47.32  110.63 1033 1220S   14FLxxno-9x-9COOL GRASS/SHRUBC
21544305000 BARUUN-URT                      46.68  113.28  981  910S   12FLxxno-9x-9COOL GRASS/SHRUBA
21544336000 SAIKHAN-OVOO                    45.45  103.90 1316 1370R   -9FLDEno-9x-9WARM GRASS/SHRUBA
21544341000 MANDALGOVI                      45.77  106.28 1393 1228S   10FLxxno-9x-9WARM GRASS/SHRUBC
21544347000 TSOGT-OVOO                      44.42  105.32 1298 1271R   -9FLDEno-9x-9WARM GRASS/SHRUBA
21544352000 BAYANDELGER                     45.73  112.37 1101 1064R   -9FLDEno-9x-9COOL GRASS/SHRUBA
21544354000 Sainshand                       44.90  110.10 -999  926S   14FLxxno-9x-9WARM GRASS/SHRUBA
21544358000 ZAMYN-UUD                       43.73  111.90  964  929R   -9FLDEno-9x-9COOL GRASS/SHRUBB
21544373000 DALANZADGAD                     43.58  104.42 1465 1556S   10MVxxno-9A 1WARM GRASS/SHRUBA
21744454000 KATHMANDU AIR                   27.70   85.37 1337 1538U  354MVxxno-9A 2WARM FIELD WOODSC
21941560000 PARACHINAR                      33.87   70.08 1726 2198R   -9MVxxno-9A-9COOL GRASS/SHRUBA
22041170000 DOHA INTERNAT                   25.25   51.57   10   10U  250FLxxCO 3A 1WATER           C
22223711000 TROICKO-PECER                   62.70   56.20  139  106R   -9FLxxno-9A-9MAIN TAIGA      C
22223921000 IVDEL'                          60.68   60.45   95  190S   15HIxxno-9x-9BOGS, BOG WOODS C
22225744000 KAMENSKOE                       62.43  166.08   10  256R   -9MVxxno-9x-9WOODED TUNDRA   A
22228138000 BISER                           58.52   58.85  463  388R   -9MTxxno-9x-9COOL MIXED      B
22228434000 KRASNOUFIMSK                    56.65   57.78  206  240S   40HIxxno-9x-9COOL GRASS/SHRUBC
22228552000 SADRINSK                        56.07   63.65   89  121U   82FLxxno-9x-9COOL CROPS      C
22229807000 IRTYSSK                         53.35   75.45   94  120R   -9FLxxno-9x-9COOL IRRIGATED  C
22232411000 ICA                             55.58  155.58    6    3R   -9FLxxCO 1x-9SIBERIAN PARKS  A
22340356000 TURAIF                          31.68   38.73  852  816R   -9FLDEno-9A-9HOT DESERT      C
22340405000 GASSIM                          26.30   43.77  650  582U   70FLxxno-9A15SAND DESERT     C
22340438000 RIYADH                          24.72   46.73  620  696U 1380FLxxno-9A 1WARM IRRIGATED  C
22340439000 YENBO                           24.02   38.22   11   71S   25HIxxCO 7A 3HOT DESERT      C
22443424000 PUTTALAM                         8.03   79.83    2    3S   18FLxxCO 1x-9WATER           B
22443466000 COLOMBO                          6.90   79.87    7    9U  852FLxxCO 1x-9WATER           C
22443473000 NUWARA ELIYA                     6.97   80.77 1880 1543S   16MVxxno-9x-9WARM FOR./FIELD B
22443497000 HAMBANTOTA                       6.12   81.13   20   42S   11FLxxCO 1x-9WARM GRASS/SHRUBA
22848462000 ARANYAPRATHET                   13.70  102.58   49   61R   -9HIFOno-9x-9TROPICAL DRY FORA
23041196000 SHARJAH INTER                   25.33   55.52   33   30U  266FLxxCO10A 5WARM GRASS/SHRUBC
23248820000 HA NOI                          21.02  105.80    6   45U 2571FLxxno-9x-9PADDYLANDS      C
23248826000 PHU LIEN                        20.80  106.63  119   60U 1279FLxxCO14x-9PADDYLANDS      B
23248855000 DA NANG                         16.03  108.18    7  233U  492MVxxCO 2A 1WATER           B
23248877000 NHA TRANG                       12.25  109.20   10   20U  216MVxxCO 1A 1WATER           C

South America

30187934000 RIO GRANDE B.                  -53.80  -67.75   22   32S   13FLxxCO 3A 2WATER           B
30382397000 FORTALEZA                       -3.77  -38.60   26    0U  648FLxxCO 1x-9WARM CROPS      C
30382578000 TERESINA                        -5.08  -42.82   74  112U  339FLxxno-9A 1WARM CROPS      C
30382861000 CONCEICAO DO                    -8.25  -49.28  157  157R   -9HIxxno-9A-9WARM GRASS/SHRUBC
30383096000 ARACAJU                        -10.92  -37.05    5   29U  288FLxxCO 2x-9WARM FOR./FIELD C
30383229000 SALVADOR                       -13.02  -38.52   51    3U 1496HIxxCO 2x-9WATER           C
30383361000 CUIABA                         -15.55  -56.12  151  170U  167HIxxno-9x-9MARSH, SWAMP    C
30383552000 CORUMBA                        -19.08  -57.50  130  171U   66FLxxno-9x-9TROP. SAVANNA   A
30383618000 TRES LAGOAS                    -20.78  -51.70  313  353S   45HIxxLA-9x-9WARM FIELD WOODSC
30383702000 PONTA PORA                     -22.53  -55.73  650  629S   20HIxxno-9A 2TROP. SEASONAL  C
30489056000 CENTRO MET.AN                  -62.42  -58.88   10    0R   -9HIICCO 1x-9ANTARCTICA      A
30886033000 BAHIA NEGRA                    -20.22  -58.17   96   86R   -9FLMAno-9A-9SEMIARID WOODS  A
30886065000 PRATS-GIL                      -22.70  -61.50  220  224R   -9FLxxno-9A-9SUCCULENT THORNSA
30886068000 MARISCAL                       -22.02  -60.60  181  189R   -9FLxxno-9A-9SUCCULENT THORNSC
30886086000 PUERTO CASADO                  -22.28  -57.87   87   80R   -9FLMAno-9A-9TROPICAL DRY FORA
30886097000 PEDRO JUAN CA                  -22.58  -55.65  662  631S   20HIxxno-9A 2TROP. SEASONAL  A
30886134000 CONCEPCION                     -23.42  -57.30   74   85S   19FLxxno-9x-9TROPICAL DRY FORA
30886218000 ASUNCION/AERO                  -25.27  -57.63  101   90U  388FLxxno-9A 5TROPICAL DRY FORC
30886233000 SAN JUAN BAUTISTA/MISIONES     -25.80  -56.30  155  233R   -9HIFOno-9x-9TROP. SEASONAL  A
30886255000 PILAR                          -26.85  -58.32   56   60S   15FLxxno-9A 1MARSH, SWAMP    B
30886260000 SAN JUAN BAUT                  -26.67  -57.15  126   90R   -9HIxxno-9x-9TROPICAL DRY FORB
30886297000 ENCARNACION                    -27.32  -55.83   91   91S   23FLxxno-9A 6WARM FIELD WOODSC
30984370000 TUMBES                          -3.55  -80.40   27   35S   48FLxxCO 5A 5WARM GRASS/SHRUBA
30984377000 IQUITOS                         -3.75  -73.25  126   90R   -9FLFOno-9x-9EQ. EVERGREEN   A
30984401000 PIURA                           -5.18  -80.60   55   67U  186HIxxno-9A 1HOT DESERT      C
30984452000 CHICLAYO                        -6.78  -79.83   34   43U  280FLxxCO15A 1WARM IRRIGATED  C
30984455000 TARAPOTO                        -6.45  -76.38  282  995R   -9HIFOno-9A-9EQ. EVERGREEN   B
30984501000 TRUJILLO                        -8.10  -79.03   30  231U  355MVxxCO 6x-9WATER           C
30984515000 PUCALLPA                        -8.42  -74.60  149  180U   92HIxxLA-9A 3EQ. EVERGREEN   B
30984628000 LIMA-CALLAO/A                  -12.00  -77.12   13   20U  376MVxxCO 2A 1WATER           C
30984686000 CUZCO                          -13.55  -71.98 3249 3693U  181MVxxno-9x-9TUNDRA          B
30984691000 PISCO                          -13.75  -76.28    7    5U   53FLxxCO 1A 1WATER           B
30984735000 JULIACA                        -15.48  -70.15 3827 3833U   78MVxxno-9A 1COOL CROPS      C
30984782000 TACNA                          -18.07  -70.30  469  385U   93MVxxCO30A 2HOT DESERT      B
31281225000 ZANDERIJ                         5.45  -55.20   15   30R   -9FLxxno-9A-9COOL CROPS      B
31386330000 ARTIGAS                        -30.38  -56.50  120  140S   29FLxxno-9A 2WARM GRASS/SHRUBB
31386350000 RIVERA                         -30.88  -55.53  241  254U   50FLxxno-9x-9WARM GRASS/SHRUBC
31386360000 SALTO                          -31.38  -57.95   33   52U   73FLxxno-9x-9WARM GRASS/SHRUBC
31386430000 PAYSANDU                       -32.33  -58.03   61   58U   62FLxxno-9x-9WARM GRASS/SHRUBB
31386440000 MELO                           -32.37  -54.22  100  142S   38HIxxno-9A 5WARM GRASS/SHRUBA
31386560000 COLONIA                        -34.45  -57.83   22    0S   17FLxxCO 2x-9WATER           B
31386565000 ROCHA                          -34.48  -54.30   18   46S   22HIxxCO20A 1COASTAL EDGES   C
31386580000 CARRASCO                       -34.83  -56.00   32   16U 1173FLxxCO 3A 2WARM CROPS      C
31480403000 CORO                            11.42  -69.68   17   19U   69HIxxCO 6A 1TROPICAL DRY FORC
31480410000 BARQUISIMETO                    10.07  -69.32  614  551U  331HIxxno-9x-9WARM GRASS/SHRUBC
31480413000 MARACAY - B.A                   10.25  -67.65  437  640U  255MVxxLA-9A 1WARM GRASS/SHRUBC
31480415000 CARACAS/MAIQU                   10.60  -66.98   48  239U 1035MVxxCO 1A10WATER           C
31480416000 CARACAS/LA CARLOTA              10.50  -66.90  865 1135U 1035MVxxCO12x-9WARM CROPS      C
31480419000 BARCELONA                       10.12  -64.68    7   62U   78HIxxCO 3A 1WATER           C
31480423000 LA GUIRIA              VENEZUE  10.58  -62.30    8  136S   15HIxxCO 1A 1COASTAL EDGES   C
31480435000 MATURIN                          9.75  -63.18   66   70U   98FLxxno-9x-9WARM GRASS/SHRUBC
31480438000 MERIDA                           8.60  -71.18 1498 2555U   74MVxxno-9x-9WARM GRASS/SHRUBC
31480444000 CIUDAD BOLIVA                    8.15  -63.55   48   62U  104FLxxno-9A 1TROP. SAVANNA   C
31480447000 SAN ANTONIO D                    7.85  -72.45  378  474U  220MVxxno-9A 2TROP. MONTANE   C
31480450000 SAN FERNANDO                     7.90  -67.42   48   55S   39FLxxno-9A 5WARM GRASS/SHRUBC
31480453000 TUMEREMO                         7.30  -61.45  181  183R   -9FLxxno-9A-9WARM GRASS/SHRUBA
31480457000 PUERTO AYACUC                    5.60  -67.50   74  162S   10FLxxno-9A10TROP. SAVANNA   A
31480462000 SANTA ELENA D                    4.60  -61.12  907  934R   -9HIxxno-9x-9TROP. MONTANE   C
31581401000 SAINT-LAURENT                    5.50  -54.03    9   34R   -9FLxxCO30x-9EQ. EVERGREEN   C
31581405000 CAYENNE/ROCHA                    4.83  -52.37    9  109S   37HIxxCO10A10MARSH, SWAMP    B
31581408000 SAINT GEORGES                    3.88  -51.80    7   46R   -9FLxxno-9x-9EQ. EVERGREEN   A
31581415000 MARIPASOULA                      3.63  -54.03  106  268R   -9HIxxno-9A-9EQ. EVERGREEN   A

North America

40278583000 BELIZE/PHILLI                   17.53  -88.30    5   16U   51FLxxCO 3A10TROP. SEASONAL  A
40578762000 JUAN SANTAMAR                   10.00  -84.22  939 1060S   33MVxxno-9A 2TROP. SEASONAL  C
40578767000 PUERTO LIMON                    10.00  -83.05    3   60S   30FLxxCO 1x-9TROP. SEASONAL  C
40678367000 GUANTANAMO,OR                   19.90  -75.13   23    3R   -9HIxxCO 1A-9WATER           C
41278705000 LA CEIBA (AIR                   15.73  -86.87   26  240S   39MVxxCO 2A 5WARM FOR./FIELD B
41278708000 LA MESA                         15.45  -87.93   31   43U  151MVxxno-9A10WARM DECIDUOUS  C
41278720000 TEGUCIGALPA                     14.05  -87.22 1007 1047U  305MVxxno-9A 2WARM FOR./FIELD C
41476160000 HERMOSILLO,SO                   29.07 -110.95  211  225U  233HIxxno-9x-9WARM GRASS/SHRUBC
41476220000 TEMOSACHIC,CH                   28.95 -107.83 1870 1944R   -9MVxxno-9x-9WARM DECIDUOUS  B
41476225000 UNIV. DE CHIH                   28.63 -106.08 1435 1528U  327MVxxno-9x-9WARM GRASS/SHRUBC
41476243000 PIEDRAS NEGRA                   28.70 -100.52  250  227S   21FLxxno-9A 1WARM GRASS/SHRUBC
41476311000 CHOIX,SIN.                      26.72 -108.28  238  403R   -9HIxxno-9x-9TROP. SAVANNA   A
41476342000 MONCLOVA,COAH                   26.88 -101.42  615  768U   78MVxxno-9x-9SUCCULENT THORNSC
41476373000 TEPEHUANES,DG                   25.35 -105.75 1810 2061R   -9MVxxno-9x-9WARM DECIDUOUS  B
41476382000 TORREON,COAH.                   25.53 -103.45 1124 1339U  244HIxxno-9x-9WARM GRASS/SHRUBC
41476390000 SALTILLO,COAH                   25.45 -100.98 1790 1594U  201MVxxno-9x-9SUCCULENT THORNSC
41476393000 MONTERREY,N.L                   25.87 -100.20  512  548U 1923MVxxno-9x-9WARM IRRIGATED  C
41476405000 LA PAZ, B.C.S                   24.27 -110.42   18   71S   46HIxxCO 3x-9WATER           A
41476458000 MAZATLAN                        23.20 -105.40    3 1642U  147FLxxCO 1x-9TROP. SAVANNA   A
41476525000 ZACATECAS,ZAC                   22.78 -102.57 2612 2421U   50HIxxno-9x-9WARM DECIDUOUS  C
41476548000 TAMPICO, TAMP                   22.22  -97.85    9   32U  212FLxxCO 5x-9COASTAL EDGES   C
41476556000 TEPIC,NAY.                      21.52 -104.90  922  927U  109MVxxCO30x-9WARM CROPS      C
41476577000 GUANAJUATO,GT                   21.02 -101.25 1999 2244S   37HIxxno-9x-9WARM FIELD WOODSC
41476581000 RIO VERDE,S.L                   21.85 -100.00  990 1038S   17HIxxno-9x-9COOL DESERT     A
41476632000 PACHUCA,HGO.                    20.13  -98.73 2417 2508U   84MVxxno-9x-9WARM CROPS      C
41476640000 TUXPAN.VER.                     20.95  -97.40   28   27S   34FLxxCO 7x-9WARM CROPS      C
41476644000 AEROP.INTERNA                   20.98  -89.65    9   10U  234FLxxno-9A 2WARM CROPS      C
41476647000 VALLADOLID,YU                   20.70  -88.22   22   15S   15FLxxno-9x-9TROP. SAVANNA   C
41476654000 MANZANILLO,CO                   19.05 -104.33    3   30S   21HIxxCO 1x-9TROPICAL DRY FORC
41476662000 ZAMORA,MICH.                    19.98 -102.32 1562 1733R   -9MVxxno-9A-9WARM CROPS      C
41476665000 MORELIA,MICH.                   19.70 -101.18 1913 1979U  199MVxxno-9x-9WARM FIELD WOODSC
41476680000 MEXICO (CENTR                   19.40  -99.20 2303 2307U13994MVxxno-9x-9WARM CROPS      C
41476683000 TLAXCALA,TLAX                   19.32  -98.23 2248 2342S   10HIxxno-9x-9TROP. MONTANE   C
41476685000 PUEBLA,PUE.                     19.05  -98.17 2179 2151U  466MVxxno-9x-9TROP. MONTANE   C
41476687000 JALAPA,VER.                     19.53  -96.92 1389 1423U  161MVxxno-9x-9WARM CROPS      C
41476692000 HACIENDA YLAN                   19.15  -96.12   13    6U  256FLxxCO 1x-9TROP. SEASONAL  C
41476695000 CAMPECHE,CAMP                   19.85  -90.55    5    8U   70FLxxCO 1x-9WATER           C
41476726000 CUERNAVACA,MO                   18.88  -99.23 1618 1720U  240MVxxno-9x-9WARM CROPS      C
41476741000 COATZACOALCOS                   18.15  -94.42   23    3U   70FLxxCO 1x-9WATER           C
41476750000 CHETUMAL,Q.RO                   18.48  -88.30    9    3S   24FLxxCO 1x-9TROP. SEASONAL  C
41476775000 OAXACA,OAX.                     17.07  -96.72 1550 1858U  115MVxxno-9x-9TROP. SAVANNA   C
41476805000 ACAPULCO,GRO.                   16.83  -99.93   13  113U  309MVxxCO 1x-9COASTAL EDGES   C
41476845000 SN. CRISTOBAL                   16.73  -92.63 2276 2336S   26MVxxno-9x-9TROP. SEASONAL  C
41476903000 TAPACHULA, CH                   14.92  -92.27  118  281U   60MVxxCO20A 3TROPICAL DRY FORC
41578741000 MANAGUA, NICARAGUA              12.10  -86.20   56  107U  405HIxxLA-9A 5WARM FIELD WOODSC
42572259000 DALLAS-FORT W                   32.90  -97.03  182  161U 4037FLxxno-9A 5WARM FIELD WOODSC
42572597000 MEDFORD/MEDFO                   42.37 -122.87  405  415S   47MVxxno-9A 2WARM FOR./FIELD C
42978384000 OWEN ROBERTS                    19.28  -81.35    3    0R   -9FLxxCO 1A-9WATER           C
43104220000 EGEDESMINDE                     68.70  -52.75   41   20R   -9HIxxCO 1x-9WATER           A
43104250000 GODTHAB NUUK                    64.17  -51.75   70    0R   -9MVxxCO 1A-9TUNDRA          B
43104312000 NORD ADS                        81.60  -16.67   34   13R   -9HIxxCO 1x-9ICE             A
43104320000 DANMARKSHAVN                    76.77  -18.67   12  265R   -9FLxxCO 1x-9WATER           A
43104360000 ANGMAGSSALIK                    65.60  -37.63   52  275R   -9MVxxCO 1x-9WATER           A
43104390000 PRINS CHRISTI                   60.05  -43.17   74    0R   -9HIxxCO 1x-9TUNDRA          A
43278897000 LE RAIZET,GUA                   16.27  -61.52   11   36S   25HIxxCO 2A 3WATER           C
43378925000 LAMENTIN/MARTINIQUE/FT DE       14.60  -61.10  144   64U   98HIxxCO 1x-9WATER           C
43478866000 JULIANA AIRPO                   18.05  -63.12    9   24R   -9HIxxCO 1A-9WATER           C
43478988000 HATO AIRPORT,                   12.20  -68.97   67    0U   95FLxxCO 1A10WATER           C
43871805000 SAINT-PIERRE                    46.77  -56.17    5   10R   -9HIxxCO 1A-9WATER           B

Pacific Region

50194259000 BURKETOWN                      -17.73  139.53    8    7R   -9FLxxCO30x-9WARM FIELD WOODSA
50194968000 LAUNCESTON AI                  -41.53  147.20  178  146S   31HIxxno-9A 8COOL FIELD/WOODSB
50291652000 UNDU POINT                     -16.13 -179.98   63    0R   -9HIxxCO 1x-9WATER           A
50291680000 NANDI                          -17.75  177.45   18   65R   -9HIxxCO 1A-9TROP. MONTANE   C
50291683000 NAUSORI                        -18.05  178.57    7   88U   64HIxxCO 8A20WATER           B
50291699000 ONO-I-LAU                      -20.67 -178.72   28    0R   -9HIxxCO 1A-9WATER           A
50396109000 PAKANBARU/                       0.47  101.45   31   83U  186FLxxno-9A 3EQ. EVERGREEN   C
50396633000 BALIKPAPAN/SE                   -1.27  116.90    3   19U  281FLxxCO 1A 1EQ. EVERGREEN   C
50396745000 JAKARTA/OBSER                   -6.18  106.83    8   27U 6503FLxxCO 6x-9PADDYLANDS      C
50396797000 TEGAL                           -6.85  109.15   10    0U  132FLxxCO 1x-9PADDYLANDS      C
50396925000 SANGKAPURA                      -5.85  112.63    3    0R   -9HIxxCO 1x-9WATER           A
50396973000 KALIANGET(MAD                   -7.05  113.97    3   70R   -9FLxxCO 1x-9COASTAL EDGES   A
50397048000 GORONTALO/JAL                    0.52  123.07    2   75U   98MVxxCO 3x-9WATER           B
50397182000 UJANG PANDANG                   -5.07  119.55 -999   30U  709HIxxCO 7A15WARM CROPS      C
50397240000 AMPENAN/SELAP                   -8.53  116.07    3   35S   47MVxxCO 2A 3WARM FOR./FIELD B
50397796000 KOKONAO/TIMUK                   -4.72  136.43    3    0R   -9FLMACO 1x-9WATER           A
50548674000 MERSING                          2.45  103.83   45    0S   18FLxxCO 1x-9WARM FOR./FIELD B
50998755000 HINATUAN                         8.37  126.33    3   50R   -9FLxxCO 1x-9TROP. SEASONAL  A
51891643000 FUNAFUTI                        -8.52  179.22    2    0R   -9FLxxCO 1A-9WATER           A
52091554000 PEKOA                          -15.52  167.22   56  286R   -9HIxxCO 5A-9WATER           A
52091568000 ANEITYUM                       -20.23  169.77    7  148R   -9HIxxCO 1A-9WATER           A
52191765000 PAGO PAGO/INT                  -14.33 -170.72    3    0R   -9HIxxCO 1A-9WATER           C
52791334000 TRUK,                            7.47  151.85    2    0R   -9HIxxCO 1A-9WATER           C
52791348000 PONAPE,                          6.97  158.22   46    0R   -9HIxxCO 1A-9WATER           C
52791413000 YAP, CAROLINE                    9.48  138.08   17    0R   -9HIxxCO 1A-9WATER           A
52891925000 ATUONA                          -9.80 -139.03   52    0R   -9HIxxCO 4A-9WATER           A
52891938000 TAHITI-FAAA                    -17.55 -149.62    2    0S   23MVxxCO 1A 2WATER           C
52891943000 TAKAROA                        -14.48 -145.03    3    0R   -9FLxxCO 1x-9WATER           A
52891945000 HEREHERETUE                    -19.87 -145.00    3    0R   -9FLxxCO 1x-9WATER           A
52891948000 TOTEGEGIE, GAMBIER IS.         -23.10 -134.90    3    0R   -9FLxxCO 1A-9WATER           A
52891954000 TUBUAI                         -23.35 -149.48    3    0R   -9HIxxCO 1A-9WATER           A
52891958000 RAPA                           -27.62 -144.33    2    0R   -9HIxxCO 1x-9WATER           A
53191366000 KWAJALEIN/BUC                    8.73  167.73    8    0R   -9FLxxCO 1A-9WATER           B
53191376000 MAJURO/MARSHA                    7.08  171.38    3    0R   -9FLxxCO 1x-9WATER           B
53291577000 KOUMAC                         -20.57  164.28   18   42R   -9MVxxCO 1x-9WATER           A
53291592000 NOUMEA                         -22.27  166.45   72    0U   56HIxxCO 2A 1WATER           C
53691408000 KOROR, PALAU                     7.33  134.48   33    0R   -9HIxxCO 1A-9WATER           B
53991245000 WAKE ISLAND A                   19.28  166.65    4    0R   -9FLxxCO 1A-9WATER           A
54091753000 HIHIFO                         -13.23 -176.17   27    0R   -9HIxxCO 2A-9WATER           A

Europe

60237789000 YEREVAN                         40.13   44.47  907 1067U 1019MVxxno-9A 1WARM GRASS/SHRUBC
61111464000 MILESOVKA                       50.55   13.93 -999  409R   -9MVxxno-9x-9COOL CROPS      A
61111518000 PRAHA/RUZYNE                    50.10   14.25  365  322U 1161HIxxno-9A 3COOL FOR./FIELD C
61111520000 PRAHA-LIBUS                     50.02   14.45  304  309U 1161HIxxno-9x-9COOL CROPS      C
61111723000 BRNO/TURANY                     49.15   16.70  246  302U  336HIxxno-9A 4COOL FOR./FIELD B
61111782000 OSTRAVA/MOSNO                   49.68   18.12  256  278U  294HIxxno-9A15COOL CONIFER    A
61206030000 ALBORG                          57.10    9.87   13   10U  155FLxxCO30A 5WARM CROPS      C
61206186000 KOBENHAVN/                      55.68   12.55    9    5U 1328FLxxCO 1x-9WATER           C
61206190000 RONNE                           55.07   14.75   16   81S   15FLxxCO 1A 3WATER           A
61507015000 LILLE                           50.57    3.10   52   33U  171FLxxno-9x-9WARM CROPS      C
61507037000 ROUEN                           49.38    1.18  157  131U  114HIxxno-9A 3WARM CROPS      C
61507110000 BREST                           48.45   -4.42  103   78U  164FLxxCO 7A 3WARM CROPS      C
61507190000 STRASBOURG                      48.55    7.63  154  170U  252FLxxno-9A 3WARM DECIDUOUS  C
61507222000 NANTES                          47.17   -1.60   27   51U  253FLxxno-9A 3WARM CROPS      C
61507255000 BOURGES                         47.07    2.37  166  152U   75HIxxno-9A 1WARM CROPS      C
61507280000 DIJON                           47.27    5.08  227  241U  150HIxxno-9A 4WARM FOR./FIELD C
61507434000 LIMOGES                         45.87    1.18  402  335U  136HIxxno-9A 5WARM CROPS      C
61507460000 CLERMONT-FERR                   45.78    3.17  330  473U  153MVxxno-9x-9WARM CROPS      C
61507510000 BORDEAUX/MERI                   44.83   -0.70   61   44U  220FLxxCO30A 3WARM DECIDUOUS  C
61507560000 MONT AIGOUAL                    44.12    3.58 1565 1019R   -9MTxxno-9x-9MED. GRAZING    A
61507630000 TOULOUSE/BLAG                   43.63    1.37  153  160U  371FLxxno-9A 3WARM GRASS/SHRUBC
61507643000 MONTPELLIER                     43.58    3.97    6   38U  178HIxxCO 8x-9WARM CROPS      C
61507650000 MARSEILLE/MARIGNANE FRANCE      43.30    5.40    8   95U  901HIxxCO10A10WATER           C
61507690000 NICE                            43.65    7.20   10  142U  331MVxxCO 1A 5WARM CROPS      C
61507747000 PERPIGNAN                       42.73    2.87   48   45U  101FLxxCO12x-9WARM CROPS      C
61507761000 AJACCIO                         41.92    8.80    9   80S   47MVxxCO 1A 3MED. GRAZING    C
61710020000 LIST/SYLT                       55.02    8.42   29    0R   -9FLxxCO 1x-9WATER           A
61710348000 BRAUNSCHWEIG                    52.30   10.45   88   74U  269FLxxno-9x-9WARM CROPS      C
61710381000 BERLIN-DAHLEM                   52.47   13.30   58   41U 3021FLxxno-9x-9WARM CONIFER    C
61710384000 BERLIN-TEMPEL                   52.47   13.40   49   41U 3021FLxxno-9A 1WARM CONIFER    C
61710410000 ESSEN                           51.40    6.97  161  113U 7452HIxxno-9A 3WARM FIELD WOODSC
61710444000 GOETTINGEN                      51.50    9.95  171  214U  124HIxxno-9x-9WARM DECIDUOUS  A
61710739000 STUTTGART/                      48.83    9.20  311  301U  600HIxxno-9x-9WARM CROPS      C
61816622000 THESSALONIKI                    40.52   22.97    4  107U  482HIxxCO 1A 6MED. GRAZING    C
61816641000 KERKYRA (AIRP                   39.62   19.92    4   39S   29HIxxCO 2A 1WATER           C
61816648000 LARISSA                         39.63   22.42   74  101U   72HIxxno-9x-9MED. GRAZING    C
61816714000 ATHINAI/OBSER                   37.97   23.72  107  100U 2567HIxxCO 6x-9WARM CROPS      C
61816716000 ATHINAI (AIRP                   37.90   23.73   15   19U 2567HIxxCO 1A 2WARM CROPS      C
61816723000 SAMOS (AIRPOR                   37.70   26.92    7   73R   -9HIxxCO 1A-9WARM CROPS      B
61816726000 KALAMATA                        37.07   22.02    8   99S   39HIxxCO 4A 6WARM CROPS      C
61816734000 METHONI                         36.83   21.70   34   58R   -9HIxxCO 1x-9WATER           A
61816746000 SOUDA (AIRPOR                   35.48   24.12  151   27R   -9HIxxCO 3A-9WATER           B
61816754000 HERAKLION (AI                   35.33   25.18   39   88U   78HIxxCO 1A 3WARM CROPS      C
62316310000 CAPO PALINURO                   40.02   15.28  185   42R   -9MVxxCO 1x-9WATER           A
62316459000 CATANIA/SIGON                   37.40   14.92   22   40U  403HIxxCO15A20MED. GRAZING    B
62440250000 H-4 'IRWAISHE                   32.50   38.20  688  691R   -9FLDEno-9x-9HOT DESERT      B
62440310000 MA'AN                           30.17   35.78 1070 1042S   11HIxxno-9A 3WARM GRASS/SHRUBA
63822165000 KANIN NOS                       68.65   43.30   49    5R   -9FLxxCO 1x-9WATER           A
63822602000 REBOLY                          63.83   30.82  182  188R   -9HIxxLA-9x-9MAIN TAIGA      B
64214015000 LJUBLJANA/BEZ                   46.07   14.52  298  319U  169MVxxno-9x-9WARM CROPS      C
64308075000 BURGOS/VILLAF                   42.37   -3.63  891  894U  118HIxxno-9A 3WARM FIELD WOODSC
64308215000 NAVACERRADA                     40.78   -4.02 1888 1698R   -9MVxxno-9x-9WARM CROPS      B
64502196000 HAPARANDA                       65.83   24.15    6    5R   -9FLxxCO 3x-9COASTAL EDGES   C
64740007000 ALEPPO                          36.18   37.22  393  408U  639FLxxno-9A 2WARM CROPS      C
64740022000 LATTAKIA                        35.53   35.77    7   13U  126FLxxCO 1x-9WATER           C
64740030000 HAMA                            35.13   36.72  309  317U  137FLxxno-9A 2WARM IRRIGATED  C
64740045000 DEIR EZZOR                      35.32   40.15  212  221U  293FLxxno-9A 4WARM GRASS/SHRUBC
64917250000 NIGDE                           37.97   34.68 1208 1364S   32MVxxno-9x-9MED. GRAZING    C
64917255000 KAHRAMANMARAS                   37.60   36.93  549  960U  136MVxxno-9x-9MED. GRAZING    C
64917260000 GAZIANTEP                       37.08   37.37  855  930U  300HIxxno-9x-9MED. GRAZING    C
64917270000 URFA                            37.13   38.77  547  630U  133FLxxno-9x-9MED. GRAZING    C
64917280000 DIYARBAKIR                      37.88   40.18  677  665U  170FLxxno-9A 1WARM GRASS/SHRUBB
64917282000 BATMAN                          37.87   41.17  540  546U   64HIxxno-9x-9WARM GRASS/SHRUBB
64917285000 HAKKARI                         37.57   43.77 1720 2446S   12MVxxno-9x-9WARM GRASS/SHRUBB
64917292000 MUGLA                           37.20   28.35  646  790S   24MVxxCO25x-9MED. GRAZING    B
64917300000 ANTALYA                         36.70   30.73   57   40U  130HIxxCO 2x-9WATER           A
64917340000 MERSIN                          36.82   34.60    3   69U  152MVxxCO 1x-9MED. GRAZING    C
64917370000 ISKENDERUN                      36.58   36.17    3  213U  107MVxxCO 1x-9WARM MIXED      C
64917375000 FINIKE                          36.30   30.15    3   65R   -9MVxxCO 1x-9WATER           B
65206011000 THORSHAVN                       62.02   -6.77   55   90S   12HIxxCO 1x-9TUNDRA          B

Off the right hand edge of the table (not visible on some browsers) are some of the technical fields, like the A flag for “Airstation” (where you often see 1×-9 for a rural non-airport and 1A-9 for a “rural” airport. The A is airport while x is not.

You can shrink the font size or just look at the page source if you wish to see the rest of the record. The only ‘interesting bit’ other than the A flag is the imagination applied to the ‘terrain type’; where what used to be in a surrounding region when a map was made ages ago, is what is asserted to be present today (where Dallas Fort Worth Airport is described as warm fields and woods… in the midst of one of the more extended urban areas on the planet… ). If folks really care, I’ll put the same data in as ‘ragged right’ so you can see it easily.

Oh, and those first two numbers after the name are LAT and LON followed by reported elevation and elevation from a map grid. Then the Urban Suburban Rural flag. Also visible ought to be population in thousands and then the start of a block of codes (that includes the airstation flag). A -9 population is rural.

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200 Responses to NOAA langoliers eat another 1/3 of stations from GHCN database

  1. Mark Young says:

    Wasn’t there already a dearth of stations in Africa already?

  2. b.poli says:

    The only station that will remain is the one above Karl’s and Hansen’s Bunsen burner in their labs.
    OT – I know. :)

  3. What am I missing? I thought we want to drop airports because they show artificial heating…

  4. Andy says:

    How does this list stack up next to the lists of stations that meet or do not meet the technical standards noted in the surface stations report?

    In other words, are these good stations or bad stations? or is there any other correlation?

  5. layne Blanchard says:

    I didn’t realize North America was dominated by such tropical locals. :-)

    Oh well. It’s snowing in FL also right now.

  6. Plato Says says:

    OT Gordon Brown is at it again – numpty

    http://www.guardian.co.uk/environment/2010/feb/12/gordon-brown-climate-change-fundraising

    “But Mr Brown brushed aside the sceptics’ challenge during a UN webcast to launch the group today.

    “Those people who have become global warming deniers and those people who have become climate change deniers are against the grain of all the evidence that has been assembled that global warming and climate change are indeed challenges that the world must meet and that can only be met together,” he said. He has previously denounced what he described as “anti-science, flat-earth climate sceptics”.”

  7. Henry chance says:

    Some are located where there is just too much cold and snow and the checkers have to travel in the mud away from all weather black asphalt surfaces. Pruning makes the data stronger (More robust)

  8. DanD says:

    Did anyone else notice “Batman?!”

  9. Mike Ramsey says:

    Question number five answered by Dr. Gavin Schmidt, a climate researcher at NASA’s Goddard Institute for Space Studies (GISS) in New York City.

    “5. What about the meteorological stations? There have been suggestions that some of the stations are located in the wrong place, are using outdated instrumentation, etc.

    Global weather services gather far more data than we need. To get the structure of the monthly or yearly temperature changes over the United States, for example, you’d need just a handful of stations, but there are actually some 1,100 of them. You could throw out 50 percent of the station data or more, and you’d get basically the same answers. Individual stations do get old and break down, since they’re exposed to the elements, but this is just one of things that NOAA [the National Oceanic and Atmospheric Administration] has to deal with. One recent innovation is the set up of a climate reference network alongside the current stations so that they can look for potentially serious issues on the large scale — and they haven’t found any yet.”

    http://climate.nasa.gov/news/index.cfm?FuseAction=ShowNews&NewsID=248

    “… haven’t found any yet.” Hmmm, Thank you God, for making our foes so easy to take down.

    Mike Ramsey

  10. Peter Dare says:

    In the Africa list, I note that stations 80-99 are all in the Republic of Sudan spanning the northern tropical zone from about 8-22N latitude. The sites cover the capital city and all the major regional towns throughout this vast country, from the desert north through the Sahel and savannah zones south to the edge of the rain forest. In effect, Sudan has been deleted from the climate recording map. It is possible/likely that the meteorological recording system has broken down completely, given the frequent internal political instability/conflicts across much of Sudan over ecent decades.

    Many other deleted stations refer to north-west African countries.

  11. Robert M says:

    I would be interested in seeing if there is any sort of pattern to the station dropouts… Is it possible to tell if a person is selecting whether or not to include a station.

    I know that everytime these guy get caught, they cliam that the changes are not sinister in nature, but simply a mistake. But if the dropouts are nefarious in nature, it is possible that simply looking at the individual station droput data might not show any trends, but I bet they allow other nearby stations that show more recent warming (or earlier cooler temps) to extend their effect.

    One thing is sure, there is an agenda here, it just needs to be teased out.

  12. mrpkw says:

    Well, it will be a lot easier to to the “adjustments” to fewer stations now !!!

  13. Somebody should take EM’s work and do a google map before and after.

  14. tty says:

    Interesting to see that they dropped Haparanda. You can get the hourly readings in real time here:

    http://www.smhi.se/vadret/vadret-i-sverige/observationer#

    Haparanda is also one of the sites with longest continuous record at high latitudes anywhere in the World (it has records back to 1802), and is, while not exactly rural, a rather small town.

  15. Robert M says:

    Seems I can’t type today, cliam should have been claim…

  16. mpaul says:

    You are confusing cause and effect. The thermometers are going extinct *because* of Global Warming. It will be in AR5. In fact, at this rate all temperature stations will likly be extinct by 2035 according to WWF.

  17. vboring says:

    Maybe they realize that satellite data is superior, so they are slowly winding down the surface thermometer mess.

  18. Joe says:

    It will be pretty hard to pull all the reading centers as we still have uncontrolled…ops CBC, ummm, controlled televison stations.

    Ah well we still have the uncencored…oh no the climate cops….
    censored internet.

  19. DirkH says:

    They killed my hometown, Braunschweig, Germany. They must have found out i’m a skeptic. Or maybe it didn’t warm enough: The station is at a small airport that is not tarmacked, and it’s outside the city near a forest, so not much UHI.

    It can be found easily by searching for this in google maps:
    Flughafen Braunschweig-Wolfsburg

  20. Viv Evans says:

    Crikey – they’ve taken out most of France!
    Don’t they like the Fwench?

    There doesn’t seem to be reason behind this, like taking out all ‘U’, or all ‘R’ stations: the thermometers are dying across the board.

  21. Mike Ramsey says:

    DanD (10:22:28) :

    Did anyone else notice “Batman?!”

    I think that Batman is a city in Turkey.

    Mike Ramsey

  22. BrianSF says:

    I don’t see 1 station in the UK or Ireland! Am I missing something?

  23. Chris H says:

    I guess this comes as no surprise as it transgresses every rule of data collection. In my simplicity, I would reckon that for a global temperature average one would take as many stations as one could get and correct for the number of stations per unit area so that well covered areas did not skew the mean. Adjustment for UHI would be reasonable but that’s it. Dealing with missing values is standard statistical practice and would not trouble a decent statistician.

    Even if it is reasonable to drop some stations for incomplete data, the values from those stations should be reported to check that they are representative of the retained stations. Epidemiology 101.

    This is what we jokingly used to call data enrichment. I never thought I would see it for real from supposedly “top” research establishments

  24. Jeff C. says:

    just because NOAA and GCHN have stopped using their data doesn’t mean they have stopped gathering and reporting. We need the “real” raw data, not just the filtered raw data. That would allow us to correlate the dropped staion data against the remaining to see if there is a discrenable bias. By keep the raw data out of the worlds hands they are setting the playing field in their favor.

  25. Ken Harvey says:

    I notice that Durban Airport has been given the chop. I cannot imagine why. Its surrounds are described as “water”. Well, yes, it has the Indian Ocean along one side, but on the other it has the outskirts of a city with a reputed population of 5M. (probably only 3M or 4M or so in fact).

  26. JonesII says:

    I guess surfacestations.org will have to hire a detective team. This is no longer about metereology, so its becoming less interesting.
    The trouble is what kind of experts will be needed in the future for finding all missing data; archeologists, cryptologists or criminologits?☺. Well, real data is lost for ever anyway.

  27. DirkH says:

    I havde to add there is no political unrest in my hometown ATM, unlike Sudan, and, glancing at google i see the Braunschweig airport does have one tarmacked runway now – probably the people at Volkswagen paid for one, their managers use this airport with a Learjet or something like that.

  28. Sean Peake says:

    No Canada… why mostly sweaty places?

  29. Gary Hladik says:

    Mike Ramsey (10:24:13), that reminds me of Isaac Asimov’s 1955 short story “Franchise”. In that story the supercomputer “Multivac” decides all US elections by interviewing a single voter who’s so average that he represents the entire electorate.

    Hey, the “average surface temperature of the Earth” is a number so nebulous and utterly useless that NASA might just as well derive it from a single thermometer and save us all a few bucks.

  30. Sean Peake says:

    Thinking more about this. I guess, now that the science is settled and the end is (Bill) Nye unless we do something, there is no need to rely on temperature stations.

  31. TerrySkinner says:

    I saw a map a few days ago (maybe here, maybe not) where all the poorly or non-measured areas showed up as warmer including the Arctic, Siberia and N. Africa.

    Of course in the past few months Europe, China, USA etc must have had a lot of cold readings. Chances are they are for the ax next.

    However roll forward a few years. What’s the chance the die hard AGW supporters will use 2009/2010 as a new baseline of cold to show ‘unprecedented’ warming over the next few years?

  32. Henry chance says:

    Schmidt and his legacy thermometers. The old ones are tired and need a little help. We have snowfall in all 57 states. Is that showing up on a map?

  33. Roger Knights says:

    Many other deleted stations refer to north-west African countries.

    Don’t you mean northeast?

  34. kadaka says:

    Plato Says (10:19:31) :

    OT Gordon Brown is at it again – numpty
    (…)

    Curious. “Global warming deniers” and “climate change deniers” are now identified as being separate groups. Perhaps there are enough of them now to differentiate based on the slight but noticeable differences, thus leading to declaring them separate species. Evolution in action, as populations grow to fill enticing and expanding niches.

    Didn’t Michael Mann, with his hockey stick, deny there was either climate change or global warming before Modern Industrialization? Thus it is allowable to be “anti-science, flat-earth climate sceptics” with regards to historical times, but not more modern ones?

  35. Sean says:

    The actual number of records is just way to low. If the professionals are unable to collect the temp, or think it can be restricted by dislosure agreements, there are other sources. Many amateurs like to collect temp readings, and business do for commercial reasons also. It should be practical to do an open call to the public to provide long station records. I suspect there are schools, sea ports, ice cream makers, farms, ski resorts which have 20 years records. Even if you set the limit at 50 years, I think we would find significant numbers Of course, you still need QA, but this is easier if there is a direct dialogue with the supplier.

  36. thegoodlocust says:

    Mike Ramsey (10:50:03) :

    “I think that Batman is a city in Turkey.”

    Ridiculous, bats are mammals not birds.

  37. James Sexton says:

    Did I miss it? What does the A, B, or C represent at the end of the row? And why is Mexico such a target? Are the thermometers getting kidnapped too??!!!

  38. It's always Marcia, Marcia says:

    Dallas Fort Worth airport is one of hundreds of GHCN reporting stations gone missing.

    Because it’s too cold in Dallas now?

  39. James Sexton says:

    Yikes!!! First Bolivia and now they’re going after Paraguay!!!

  40. E.M.Smith says:

    Mark Young (10:07:42) :
    Wasn’t there already a dearth of stations in Africa already?

    Yup. The large holey areas just got more holey…

    Leif Svalgaard (10:13:22) : What am I missing? I thought we want to drop airports because they show artificial heating…

    I don’t want to drop any stations. I want the data to clearly state what is happening and for change to be reportable by group (such as Airports vs rural vs Urban vs …) so see what’s contributing to the means.

    With that said: Dropping an airport can still induce an upward bias. Say, for example DFW was in the ‘baseline to now’ period (and so it STAYS in the baseline). And say it was cooler in the baseline (pre -Jets) and ‘warmed the planet’ until now. But since, oh, 1990 or so it has not changed to be much warmer (i.e. traffic is down and they are not adding more tarmac). It’s just not adding any new ‘lift’. So you drop it. But if you leave in a nearby place that is still growing, it will now be showing increases of “anomaly” when compared to the prior values of DFW and those increased values will be used to “fill in” DFW that will then be compared to it’s prior self… and found to be warming.

    And yes, GIStemp compares “old basket of thermometers” to “new basket of thermometers” when computing “grid box anomalies”. It does not compute anomalies as ‘thermometer to self”… except by accident if that’s all it’s got in a box.

    So change itself is a problem.

  41. Clive says:

    Someone may have asked.

    I dunno…it just seems that vast areas are under represented a la the Bolivia Syndrome.

    Are these stations pinpointed on a world map somewhere?

    Just curious.

    Thanks,

    Clive

  42. Dr. Robert says:

    According to Gavin Schmidt, you only need a handful of thermometers to accurately represent the mean temperature of the planet earth. Conveniently, these handful of thermometers are all located in urban environments, and what they read doesn’t matter anyway, because Schmidt will just aimlessly wander over to Hansen’s office and they will adjust the earlier times down and the more recent times up.

    Gotta keep that global warming afloat!

  43. It's always Marcia, Marcia says:

    extinction or culling?

  44. It's always Marcia, Marcia says:

    E.M.Smith (11:45:29) :

    There you go again E.M., explaining things so it’s easy to understand.

    :-)

  45. KevinM says:

    In plots, the UHI seems to be more of a step than a ramp. In the Russian stations, for instance, It appeared to be a slight ramp for 10 years, then a steep increase for ten years, then a levelling off afterward.

    If you trim data from UHI sites like airports _after_ the UHI step (using the UHI excuse), then you maximize the effect of UHI on the aggregate series rather than minimize it.

    On the other hand, if you trim data from UHI sites like airports by removing the site and its entire history, you will get the most accurate picture for a number of sites that might be to small to be counted as a global proxy.

    On the other hand, if you accept UHI error by not correcting (by deletion) for it at all, then the average sum of the staggered anomoly steps will form a ramp that starts around the 1950s, then correlates well with fossil fuel use (a proxy for urbanization), then stabilizes when the bulk of your sites has passed the “step”. You would then approach an accurate anomoly measurement again (with readings offset from pre-UHI by a static error).

    I think (conspiracy theory) the thermometer team has been gaming these three scenarios, and has maybe painted itself into a corner by exhausting sources of UHI step.

  46. Jean Parisot says:

    Global weather services gather far more data than we need. To get the structure of the monthly or yearly temperature changes over the United States, for example, you’d need just a handful of stations, but there are actually some 1,100 of them. You could throw out 50 percent of the station data or more, and you’d get basically the same answers. — Gavin

    If the distribution is even and random, you might get the same answers. If you carefully select which half to throw out one could introduce confirmation bias. Since the advent of modern data processing, when has too much data ever been a problem.

  47. NickB. says:

    Mike Ramsey (10:24:13) :….Anthony

    Has anyone taken a look at the reference network products, or their siting yet? I’d be curious if these actually fit with the GISS products or not, assuming they’re not adjusted or otherwise shenaniganized…

    http://www.ncdc.noaa.gov/crn/

  48. anna v says:

    There are 10 stations from Greece. I wonder if any are left because the ones taken out seem to cover the country!

    We have had a cool summer and a medium winter. Maybe it is the cool summer that axed them?

  49. kadaka says:

    So are they now set up to declare Africa has become a hotter and more arid desolate wasteland in the years to come, exactly as forecast? Have they properly pared down the weather stations elsewhere so their predictions will be proven true?

  50. E.M.Smith says:

    James Sexton (11:39:32) : Did I miss it? What does the A, B, or C represent at the end of the row? And why is Mexico such a target? Are the thermometers getting kidnapped too??!!!

    For Mexico, a traunch was added some years back in The Megathermal Zone. A casual look at the Mexican deletions look like more up toward the Sierra Madre mountains. They don’t like Mountains at NOAA… Japan has no thermometer above 300 M any more, so, I suspect, will the rest of the world once they are done…

    I found where they kept the data and descriptions once, and kept track of it here:

    http://chiefio.wordpress.com/2009/02/24/ghcn-global-historical-climate-network/

    For those wondering what all the misc flag characters mean, the “documentation” provided as described on that link (BEGIN QUOTE):

    A detailed description of GHCN’s Quality Control can be

    found through http://www.ncdc.noaa.gov/ghcn/ghcn.html.

    So, there you go. Some pretty good pointers to where to get bits and what they mean. But what about these “read.inv.f” and “read.data.f” programs it mentions? Well, I didn’t see them. But I did see one named “v2.read.data.f” that seems to do the same thing.

    The comment block from down in the guts of that program does a nice job of telling you what the fields are:

    c ic=3 digit country code; the first digit represents WMO region/continent

    c iwmo=5 digit WMO station number

    c imod=3 digit modifier; 000 means the station is probably the WMO

    c station; 001, etc. mean the station is near that WMO station

    c name=30 character station name

    c rlat=latitude in degrees.hundredths of degrees, negative = South of Eq.

    c rlong=longitude in degrees.hundredths of degrees, – = West

    c ielevs=station elevation in meters, missing is -999

    c ielevg=station elevation interpolated from TerrainBase gridded data set

    c pop=1 character population assessment: R = rural (not associated

    c with a town of >10,000 population), S = associated with a small

    c town (10,000-50,000), U = associated with an urban area (>50,000)

    c ipop=population of the small town or urban area (needs to be multiplied

    c by 1,000). If rural, no analysis: -9.

    c topo=general topography around the station: FL flat; HI hilly,

    c MT mountain top; MV mountainous valley or at least not on the top

    c of a mountain.

    c stveg=general vegetation near the station based on Operational

    c Navigation Charts; MA marsh; FO forested; IC ice; DE desert;

    c CL clear or open;

    c not all stations have this information in which case: xx.

    c stloc=station location based on 3 specific criteria:

    c Is the station on an island smaller than 100 km**2 or

    c narrower than 10 km in width at the point of the

    c station? IS;

    c Is the station is within 30 km from the coast? CO;

    c Is the station is next to a large (> 25 km**2) lake? LA;

    c A station may be all three but only labeled with one with

    c the priority IS, CO, then LA. If none of the above: no.

    c iloc=if the station is CO, iloc is the distance in km to the coast.

    c If station is not coastal: -9.

    c airstn=A if the station is at an airport; otherwise x

    c itowndis=the distance in km from the airport to its associated

    c small town or urban center (not relevant for rural airports

    c or non airport stations in which case: -9)

    c grveg=gridded vegetation for the 0.5×0.5 degree grid point closest

    c to the station from a gridded vegetation data base. 16 characters.

    c A more complete description of these metadata are available in

    c other documentation

    Unfortunately, it does not tell you just what that ‘other documentation’ might be nor where to find it…

    So you will notice that the far right A,B,C is um, er, “not clearly defined” but you can go look in “other documentation”… somewhere…

  51. Sharon says:

    Viv Evans (10:49:41) :

    Crikey – they’ve taken out most of France!
    Don’t they like the Fwench?

    Yes, they must really hate the French because also gone is the station at St. Pierre, on the North American list. The islands of St. Pierre et Miquelon off the southern coast of Newfoundland belong to France.

    The NOAA cafeteria must still be serving Freedom Fries.

  52. Alexander says:

    If my memory is accurate, (Colonel?) Batman was the bloke who founded Melbourne, Aus.

  53. E.M.Smith says:

    Sean Peake (10:56:42) : No Canada… why mostly sweaty places?

    IMHO, Canada is already showing warming (from prior deletions?) so no need to change anything. Africa is just flat (in temperatures) and hot, so it will take more work to make it show warming. For Latin America, warming is a bit slow too (the Andes were deleted a long time ago…) and some places like Peru had too many headlines with children freezing to death or the snowfall in Southern Brazil and the crop failures in Argentina from cold. So it would need better headlines… /sarcoff>

    Or maybe it isn’t sarcastic… maybe in our Orwellian world… No, no, must not think that way. Where did I leave my soma?…

  54. Vincent says:

    “According to Gavin Schmidt, you only need a handful of thermometers to accurately represent the mean temperature of the planet earth.”

    In that case, why do we need the 3,300 submersibles that make up the Argo network to measure the ocean temperatures?

  55. Dr. Robert says:

    Honestly, I lately I feel like every day is Christmas. We wake up, we get to check WUWT, and then see the latest news that shows what a complete joke the idea of catastrophic global warming is. Every day more and more lies are exposed. Lies, fake data, and everything in between.

    I sincerely hope that someone is keeping track of all of the talking heads, politicians, and pseudo-scientists who perpetuate the myth that “the science is settled.” We should forward all of these things to them, daily.

    Sincerely, the current idea of global warming is only being kept alive by the know-nothings who keep repeating the same lies over and over. There is just nothing of substance left.

  56. John Peter says:

    ” BrianSF (10:50:34) :
    I don’t see 1 station in the UK or Ireland! Am I missing something?”

    I was also looking for UK stations and found none. This is remarkable as the UK temperatures do differ from the Continent. My old home country Denmark on the other hand has three station on perhaps 1/10th the area.

  57. JonesII says:

    E.M.Smith (12:26:21) : Where did I leave my soma?…. BTW there are already several pharmaceutical products which have been invented for that purpose: The drugs for oblivion. Some even innocent ones, and very popular, like known paracetamol/tylenol.

  58. Sean Peake says:

    E.M.Smith: I forgot. I think we have about 7 stations left here in Canada: 3 are beside steel mills, 2 are near charcoal manufacturing plants, and the rest are indoors so they are easier to reach and read in bad weather.

  59. kadaka says:

    “John Batman (21 January 1801 – 6 May 1839) was an Australian farmer and businessman who was one of the first settlers of the Melbourne area and known for founding Victoria.”

    So says the ever-authoritative Wikipedia. If you disagree, please take it up with them, I’m just relaying the info. And find another source for your term papers.

  60. Tilo Reber says:

    Leif:
    “What am I missing? I thought we want to drop airports because they show artificial heating…”

    Even with that they haven’t managed to get any heating in the last 12 years. The ultimate goal is to extrapolate from the thermometer on James Hansen’s outdoor grill to the world. Will you take your steak well done or carbon only, Leif?

  61. James Sexton says:

    E.M.Smith (12:14:46) :……..So you will notice that the far right A,B,C is um, er, “not clearly defined” but you can go look in “other documentation”… somewhere…

    Yeh, sigh, and so it is as it always was. Obfuscation is key for these people. Thanks for the clarity you provided though.

    Obviously, our English speaking thermometers are way better than the others!!!!!

  62. Andy H says:

    The Global Historical Climatology Network (GHCN) is a database of temperature, precipitation and pressure records managed by the National Climatic Data Center, Arizona State University and the Carbon Dioxide Information Analysis Center (CDIAC).

    CDIAC focus on obtaining, evaluating and distributing data related to climate change and greenhouse gas emissions.

    i.e. there’s climate change – we stay in business or there’s no climate change, so what’s the point of CDIAC?

  63. JohnH says:

    Yes no UK or Ireland, strange to leave out the country with the oldest temp records.

    Well not strange as these show there is no warming as temps are close to what they were 100 years ago.

  64. RockyRoad says:

    Dr. Robert (11:51:43) :

    According to Gavin Schmidt, you only need a handful of thermometers to accurately represent the mean temperature of the planet earth. Conveniently, these handful of thermometers are all located in urban environments, and what they read doesn’t matter anyway, because Schmidt will just aimlessly wander over to Hansen’s office and they will adjust the earlier times down and the more recent times up.

    Gotta keep that global warming afloat!
    _______________________
    Reply:

    Better yet, with just a handful of thermometers, he can just tuck them in his shirt pocket and read them at his convenience:

    “Yup… these all say 98.6 degrees again! Dang, the earth’s getting intolerably warm, just as we predicted!”

  65. DirkH says:

    “E.M.Smith (12:26:21) :
    [...]
    Or maybe it isn’t sarcastic… maybe in our Orwellian world.”

    NOAA are swindlers. They cannot justify this. Every economist, every physicist and every engineer can see this.

  66. Patrik says:

    OT: WUWT was shown in Swedish TV in Two different news shows tonight! :)

    The tide is turning here and MSM are now reporting on prime time about Himalayagate (they are lagging a bit as you understand).

    On sunday, one of Swedens most active sceptic bloggers, Maggie of http://www.theclimatescam.se, will participate in a live debate against our environmental minister!

    And this is in what is probably the most self righteous country in the world where it’s (up until now) been regarded as blasphemy or lunacy to question AGW.

  67. James Sexton says:

    lol, IT’S A LANGUAGE BARRIER!!!! The NOAA doesn’t know how to translate temperature in other languages to English temperature!!!!! That must be it!!!

  68. Andy Scrase says:

    It does seem a reasonable question to ask that, if we are to believe we can control temperature through taxation, how are we going to measure the ROI on that approach?

    However, I have noticed that ANY questions on the “science” seems to get you labelled as a skeptic.

    This can’t go on much longer, can it?

  69. KPO says:

    Slightly OT, here is a follow up comment by Andrew Lacis, on the previous piece posted here a while ago. http://dotearth.blogs.nytimes.com/ For me this statement “Anthropogenic warming of the climate system can be detected in temperature observations taken at the surface, in the troposphere and in the oceans.” Is precisely the root argument is it not? Is he implying that the measurements are 100% drop dead certain and reliable – I think not.

  70. mILLrAT says:

    I have converted the station list into a google earth kml file that is available for download at
    http://cid-78d84fe53bde5e59.skydrive.live.com/self.aspx/Public/RIPtempStations.kml
    I can sort the data into different groupings/latitude if anyone is interested.

  71. Strick says:

    I live just off Dallas-Fort Worth Airport. Remember it’s huge enough to be an environment of its own, even though it’s surrounded by suburbs.

    I’d consider their land surrounding the airport proper woodlands. The last remaining stretch of tree covered country lane is on airport property and I drove it on the way home yesterday my flight last night. The trees were completely flocked with snow, a scene right out of Currier and Ives.

    Not something you see every day around here.

  72. Harry says:

    Vincent (12:36:46)

    “In that case, why do we need the 3,300 submersibles that make up the Argo network to measure the ocean temperatures?”

    There is a fundamental question as to ‘Where is all the heat going’. It doesn’t show up in the surface temperature record, it doesn’t show up in the lower and upper atmosphere records. So it must be hiding in the ocean. So 3300 Buoy’s to find the missing heat in the ocean.

  73. BCGreenBean says:

    So, they’re discarding/exempting data from certain temperature stations, and this somehow increases their confidence in their predictions and further settles the science.. Isn’t that like a sportscaster reporting who won a baseball game by only adding up the run totals from the 1st, 5th, and 9th innings?

    (You think bookies in Vegas would buy that..?)

    Cheers,

  74. E.M.Smith says:

    BTW, in response to the KUSI broadcast where the issue of thermometers being dropped was aired, there was a response posted at Yale that said:

    http://www.yaleclimatemediaforum.org/2010/01/kusi-noaa-nasa/

    When glancing at the chart showing the number of temperature stations used over time, it does appear rather odd to see the number of stations used in the GHCN network drop dramatically between the 1970s and present. D’Aleo and Smith point to purposeful elimination of those stations.
    However, as Thomas Peterson and Russell Vose, the researchers who assembled much of GHCN, have explained:

    The reasons why the number of stations in GHCN drop off in recent years are because some of GHCN’s source datasets are retroactive data compilations (e.g., World Weather Records) and other data sources were created or exchanged years ago. Only three data sources are available in near-real time.

    It’s common to think of temperature stations as modern Internet-linked operations that instantly report temperature readings to readily accessible databases, but that is not particularly accurate for stations outside of the United States and Western Europe. For many of the world’s stations, observations are still taken and recorded by hand, and assembling and digitizing records from thousands of stations worldwide is burdensome.
    During that spike in station counts in the 1970s, those stations were not actively reporting to some central repository. Rather, those records were collected years and decades later through painstaking work by researchers. It is quite likely that, a decade or two from now, the number of stations available for the 1990s and 2000s will exceed the 6,000-station peak reached in the 1970s.

    For the 2010 deletions, “That dog won’t hunt”… since BY DEFINITION those stations were reporting up until now. I’m sure someone can find a Somalia or Sudan set that went flakey for political reasons (much as Indonesia did for a few years during their troubles, but was not dropped, just had missing data flags put in…) but, well, DFW and Strasbourg… And last I looked Mexico was not having a revolution…

    BTW, the link for the “explanation” from NOAA is:

    http://www.ncdc.noaa.gov/oa/climate/research/Peterson-Vose-1997.pdf

    Guess they will need some time to come up with a new explanation…

  75. Deb says:

    EGEDESMINDE
    GODTHAB NUUK
    NORD ADS
    DANMARKSHAVN
    ANGMAGSSALIK
    PRINS CHRISTI

    Looks like Greenland has been turfed. Are there even any stations left there at all?? I couldn’t find any on the quick pass I just did. Must be all that pesky ice. Odd considering they’re so worried about the place melting and drowning us all…

  76. Warren Berman says:

    Maybe somebody can answer the following:
    1. I keep reading that temps have risen(as an example: .7C) over a certain time period, such as the last 100 years…
    a. how are these temps measured, by what kind of instrument, and are all of them worldwide the same?
    b. If these current instruments are digital readouts, capable of measuring to a few tenths, what was used prior to digital?
    c. how are all of them worldwide calibrated?
    2. My point is: How can anyone say that temperatures have risen by X amount, when in all probability that amount is within the variance for error of all instruments used, especially prior to digital readouts, and especially since the number and location of sites seems to vary constantly, and since many are exposed to urban growth around them.

  77. The ghost of Big Jim Cooley says:

    In 2006 the UK experienced a very hot July. In all of over 300 years of record keeping, the UK had never experienced a month where the average temperature was 20 deg c. We waited for the final figure from the Met Office to discover that it was 19.7 deg c. It was, to paraphrase one of our prime ministers, a damn close run thing. It turned out to be 2.82 deg c above the average July temperatures for the previous 10 years. Inevitably it was touted as more evidence of global warming and its impact on the UK. The BBC journalists almost wet themselves with excitement.

    It will be interesting then, to see what the media makes of the fact that the UK Met Office have just re-adjusted the January temperature – which turns out to be just 1.4 deg c for the month – which is 3.62 deg c LOWER than an average of the past 10 years of Jan temps.

    BBC: I’ll see your +2.82 and raise you -3.62. But I’m betting that NOTHING will be said.

  78. JohnWho says:

    I’m thinking the next step will be to say:

    “We don’t need no stinking reporting stations. We have enough historical data to interpolate global temperature without wasting time reading and manipulating the data.”

    Both “Nature” and “Science” will have predominate “peer reviewed” articles showing that this is the best way to provide accurate information to the world while also describing how “carbon neutral” the process is.

  79. E.M.Smith says:

    anna v (12:08:09) :
    There are 10 stations from Greece. I wonder if any are left because the ones taken out seem to cover the country!

    We have had a cool summer and a medium winter. Maybe it is the cool summer that axed them?

    Bad Greece! No Baklava for you! ;-)

    [chiefio@Hummer data]$ grep ^618 2010_uniq_station_list
    [chiefio@Hummer data]$

    None in 2010, but here they are in 2009:

    [chiefio@Hummer data]$ grep ^618 2009_uniq_station_list
    61816622000
    61816641000
    61816648000
    61816714000
    61816716000
    61816723000
    61816726000
    61816734000
    61816746000
    61816754000
    [chiefio@Hummer data]$

    And checked directly against the v2.mean file to assure no ‘screw up’ of my process:

    [chiefio@Hummer data]$ grep ^618 v2.mean | grep 2010
    [chiefio@Hummer data]$

    We’ll need to see if a warm Italy or hot Turkey warms you up or not…

    When I tried the GISS map setting it to Jan 2010 it said:

    “Surface Temperature Analysis: Maps

    Error

    I cannot construct a plot from your input. Time interval: data not available yet.”

    So we won’t know for a while yet if you got warmer… No fair looking out the window ;-)

  80. George E. Smith says:

    Well Gavin Schmidt says he doesn’t need all the data he has now; and could throw out a lot more stations.

    I can even buy that; he may in fact have enough stations in the US to represent the group of stations he has in the US.

    But unfortunately; the public is led to believe, and the really important public news media, is led to believe that what they are reporting actually represents the whole world; which sadly it does not. So yes they may be oversampling the areas of the USA that they are sampling; but they aren’t oversampling the whole planet.

    By the way, E.M. I understand that somebody we both know used to work for you years ago; he speaks highly of his experience working for you.

    George

  81. Frank Mosher says:

    E.M Smith. Much thanks for your tireless, concise, and easy to understand analysis. fm

  82. climaesceptico says:

    I am amazed and outraged with the two deletions affecting Spain:

    64308075000 BURGOS/VILLAF 42.37 -3.63 891 894U 118HIxxno-9A 3WARM FIELD WOODSC
    64308215000 NAVACERRADA 40.78 -4.02 1888 1698R -9MVxxno-9x-9WARM CROPS B

    I almost don’t know where to start with these!

    Amongst all the provincial capitals in Spain, Burgos holds the record for the lowest average temperature (9.9C). Two other provinces which are widely known as being very cold, are second and third with… Avila: 10.4C, Soria: 10.5C.

    Burgos also happens to hold the record for the lowest average minimum during summer, at 10.6C. The absolute minimum for Burgos was -22C in 1971.

    In fact, “Villafría” (Burgos) is explicitly mentioned as having achieved the lowest temp. in Spain (-17.1C) during last December’s cold spell here.

    So they have just wiped Spain’s coldest city off the record. Nice one!

    With regards to the “Airport” rating, well… sort of. The official statistics for the whole of 2009 state that it handled 27,710 passengers, 3,569 flights, and exactly zero cargo. In terms of number of flights, that makes it Spain’s 4th airport… from the bottom of the table!! The average number of passengers per flight is 7.76, which should give a good idea as to the type of aircraft it handles.

    This is anecdotal, but interesting nonetheless : the full unabbreviated name for the station should be: “Burgos/Villafría” because it is actually nearest “Villafría de Burgos”, a small town on the outskirts of Burgos city. Now any of you with a bit knowledge of spanish will immediately recognise that “fría” part of the name as meaning: cold (“frío”, but in feminine form because the first part, “Villa” is also feminine) . So an appropriate translation of this small town’s name would be something like “Coldville”. No kidding!

    Now, if the first station’s removal is outrageous, the second is even worse. There are very few places where you can go skiing in central Spain, and yes you guessed it, Navacerrada is one of them. Actually, it is THE main place where you can ski in central Spain (nearby “La Pinilla” has a rather patchy record for snow cover) The nearest ski run (piste) is less than 3 Km away from the station’s official coordinates, and about the same height above sea level (~1900m)

    Despite the station’s name, it is not particularly near the small town called “Navacerrada”. The name has to do with the nearby mountain pass known as “Puerto de Navacerrada”.

    The “WARM CROPS” attribute had me rolling on the floor for a good while: for chrisssssake, this is subalpine vegetation!!! It is probably Spain’s most extensive and best conserved population of Pinus sylvestris (Scots pine). There is even a small breeding population of Bluethroat (Luscinia svecica) in the area, a bird much more typical of more northerly climates (Norway, Russia and Poland, with smaller and more localised populations in central Europe)

    What are these people playing at????!!!!

  83. Vuk etc says:

    It is refreshing to see, among all the doom and gloom, the sun is on the go. Hopefully, more global warming in the pipeline.
    http://stereo-ssc.nascom.nasa.gov/beacon/latest/behind_euvi_195_latest.jpg

  84. Theo Goodwin says:

    In my hundreds of discussions with people who support the theory that there is significant global warming caused by man made CO2 in the atmosphere, what I find most striking is that all their reasonings are “a priori” and they seem to have nothing to say about experiment. As far as I can tell, there are no interesting experiments being conducted by climate scientists. Their disdain for the experimental extends to monitoring stations for atmospheric temperature and CO2 concentrations. Some have argued that the one CO2 monitoring station at Mauna Loa is not only enough but actually superior to having multiple stations on the surface and throughout the atmosphere. They seem to buy into Al Gore’s belief that the science is settled, that it has transcended experiment.

  85. E.M.Smith says:

    The list is a list of Thermometers DELETED. There are UK and Irish thermometers kept:

    [chiefio@Hummer 2010]$ country IRELAND
    621 IRELAND
    [chiefio@Hummer 2010]$ country UNITED
    230 UNITED ARAB EMIRATES
    425 UNITED STATES OF AMERICA
    651 UNITED KINGDOM
    [chiefio@Hummer 2010]$ cd data
    [chiefio@Hummer data]$ grep ^621 2010_uniq_station_list
    62103953000
    62103955000
    62103962000
    62103967000
    62103969000
    62103976000
    62103980000
    [chiefio@Hummer data]$ grep ^651 2010_uniq_station_list
    65103005000
    65103026000
    65103091000
    65103100000
    65103162000
    65103257000
    65103302000
    65103377000
    65103862000
    65103917000
    [chiefio@Hummer data]$

    So it’s a good thing that you are not finding Irish or UK stations on the lists!

  86. Andrew says:

    Anthony, WUWT gets an honourable mention in an article by Christopher Pearson in today’s The Australian newspaper.

    “World wide web of doubt.”

    http://www.theaustralian.com.au/politics/opinion/world-wide-web-of-doubt/story-e6frgd0x-1225829874281

    Congratulations on being a burr under the saddle of AGW.

  87. Tom in Texas says:

    EMS, have you (or anyone else) checked to see if V1raw = V2raw?

  88. James Sexton says:

    lol, while my other comments await moderation…….honestly, it seems the NOAA has a bias for English speaking thermometers. U.S., Canada, U.K., Australia, and New Zealand. Lost a total of 5. India seems fairly unscathed also. They lost 3 in the mountainous northern area. Russia and what used to be known as the Soviet bloc in Europe is also fairly unscathed(A few were lost in what appears to be the Urals.). While I can’t attach any meaning to it, it seems very strange to me. I know people don’t do things out of randomness. One does things for a purpose. This may take me a twelve pack or so to sort out.

  89. David Alan Evans says:

    Leif Svalgaard (10:13:22) :

    Leif, you should know that Ålborg & Köbenhavn are both ports, not airports.

    DaveE.

  90. DirkH says:

    Warren Berman (13:29:48) :

    Warren, it’s a travesty. First of all, it’s complicated to get accurate measurements in its own right because instruments change over time, environment of weather stations changes through land use changes and UHI, weather stations move, time series are often incomplete. But worse, the guardians of the data collection do their best to mess with the data. NOAA drops more and more thermometers and GISS takes the remainder and extrapolates from them the now missing holes up to 1200 km away.

    Search here at WUWT for GISTEMP and read the musings of the chiefio (a.k.a. E.M. Smith) at http://chiefio.wordpress.com/

    Data gets tortured with bad algorithms, i won’t call them statistics because that gives statistics a bad name it doesn’t deserve. It’s blatant disregard for the Nyquist theorem in temporal and spatial dimensions, it’s below any standard. If one would compute all this with confidence intervals or error range propagation or any sound numerics i’m sure the alleged 0.6 deg C warming in the 20th century would drown in the error bars.

    Here’s a guy who did a very simple analysis of raw data who comes to the conclusion that there is no discernible trend:

    http://crapstats.wordpress.com/2010/01/21/global-warming-%e2%80%93-who-knows-we-all-care/

  91. Margaret says:

    Any views on the Pacific — it looks to me like a lot of island thermometers have gone… again I thought the Pacific ocean area was one that was not well covered.

    Is there not a case in some key areas to keep collecting even if the data doesn’t come electronically because of the GDP of the country concerned?

    I really think that this community should be thinking about doing something about the great dying. Can’t we organise (surfacestation style) to resurrect some of them by collecting the data and presenting it electronically to NOAA so they have no excuses for not using it?

  92. Margaret says:

    One other thought — if we were going to do a collection, I would have thought it was sensible to collect all the records for the stations that were going before the great die-off began ie from the early 1990s or whatever that it.

    It goes without saying such an operation would need to be well-organised and well peer reviewed so it would need one of you lot who knows about these things to kick it off.

  93. E.M.Smith says:

    Deb (13:28:27) : Looks like Greenland has been turfed. Are there even any stations left there at all?? I couldn’t find any on the quick pass I just did. Must be all that pesky ice. Odd considering they’re so worried about the place melting and drowning us all…

    [chiefio@Hummer 2010]$ country GREEN
    431 GREENLAND (DENMARK)
    [chiefio@Hummer 2010]$ cd data
    [chiefio@Hummer data]$ grep ^431 2010_uniq_station_list
    [chiefio@Hummer data]$

    Sheesh. Greenland gone too?

    OK, this got me worried that maybe I’d blown the file download, so just to be sure, I’ve double checked. The “ls -l” on the file shows it is in the ‘usual’ size range of about 45 MB and with a date stamp from when I downloaded it. New records are scattered through it (sorted by stationID then by year) and it ought to end with the highest station IDs as the last records, and it does. Those “8” records:

    [chiefio@Hummer data]$ ls -l
    -rw-rw-r– 1 chiefio chiefio 45959144 Feb 8 16:28 v2.mean
    -rw-rw-r– 1 chiefio chiefio 103258856 Feb 8 16:47 v2.mean.inv
    -rw-rw-r– 1 chiefio chiefio 22529199 Feb 8 16:27 v2.min
    -rw-rw-r– 1 chiefio chiefio 742560 Feb 8 16:10 v2.temperature.inv
    [chiefio@Hummer data]$ tail v2.mean
    8009991400101962 149 141 154 159 172 219 242 256 246 231 204 169
    8009991400101963 158 129 143 148 171 189 231-9999 245 226 194 169
    8009991400101964 148 123 148 163 167 192 240 258 253 231 194 152
    8009991400101965 142 141 156 140 172 199-9999 247 256 237 196 172
    8009991400101966 143 146 149 145 176 200-9999-9999 257 231 192 169
    8009991400101967 152 144 137 149 158 194 244 260 257 237 185 170
    8009991400101968 143 123-9999 161 174 196 239 253 241 244 186 172
    8009991400101969 159 151 142 164 197 207 246 251-9999 215 183-9999
    8009991400101970 141 136 123-9999 183 197-9999 259 243 225 196 165
    8009991400101971 150 128 149 165-9999 206 232-9999 245 221 200 170
    [chiefio@Hummer data]$

    Though on a re-visit to the ftp site, the meta data for the file give a last modification time of ‘today’… Wonder if they are fixing things again ;-)

    So if there were a truncation, the “8”s would be gone, and with the last records there (and gunzip not barfing on the unpack) then all the lower station IDs ought to have their 2010 records in place…

    So, looks to me like I did the download fine. Though with that mod time of ‘today’ I’m going to do another download and compare them later to see ‘what changed’…

    For folks wanting to “try this at home” the ftp URL is:

    ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v2

  94. Bulldust says:

    Tilo Reber (12:51:38) :

    Leif:
    “What am I missing? I thought we want to drop airports because they show artificial heating…”

    Even with that they haven’t managed to get any heating in the last 12 years. The ultimate goal is to extrapolate from the thermometer on James Hansen’s outdoor grill to the world. Will you take your steak well done or carbon only, Leif?
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    Tilo:
    Outdoor barbeque? Are you mad? We can’t have random weather events affecting the thermometer. A safer place would be inside in front of Hansen’s air conditioner with his hand resting on the thermostat. Foolish man… what were you thinking? ;)

    Leif:
    I think we could file the dropping of airports under “Be careful what you wish for…” :D

  95. Andrew30 says:

    Would it be possible to create a web site that had three instances of Google Earth, one with gold halo markers on the saved sites and one with red pitchfork markers for the not-saved sites; then one map with just the halo markers and another map with just the pitchfork markers.

    I think that it would be a very good illustration of the situation and if it was possible to include a surface station photo of the site that would be informative; in absence of a photo of the site a generic photo of the area would serve a similar purpose. having the last known trend and anomaly would also be nice.

  96. zelda says:

    Is that Kobenhaven ( Copenhagen) on the list of dropped stations ?
    That will teach them

  97. Stephen Brown says:

    I will endeavour to obtain the raw data from the Zambian stations mentioned (Livingstone, Mongu and Kabwe) as well as others I know only too well are still in existence and from which data is still obtained (Chirundu, Lusaka {both airport and University} and Chilanga {experimental agricultural station}) I think that the farmers in Mazabuka also keep records, if only for their own benefit.
    I will report back in due course!

  98. Steve Jones says:

    Burketown Australia was one of a very small number of stations well less than twenty) which had continuous data from the 19th century till now. The Bureau of Meteorology seems to have deleted it in 2009. Only about nine remain; most are cities. In Victoria the only rural stations which still exist are Cape Otway and Wilson’s Promontory. Both show a pronounced cooling since the 19th century.

  99. Chris says:

    Heads up – CNN reporting a school shooting at UAH Shelby Center. Isn’t that where John Christy works? Hope things aren’t too bad over there. If I was the type, I’d be saying prayers.

    http://www.reuters.com/article/idUSTRE61B59V20100212

  100. E.M.Smith says:

    Warren Berman (13:29:48) : Maybe somebody can answer the following:

    The bits I know:

    a. how are these temps measured, by what kind of instrument, and are all of them worldwide the same?

    They vary a lot. Early on was mercury in glass, recent is electronic. There have been questions raised about some of the calibration and cross calibration…

    b. If these current instruments are digital readouts, capable of measuring to a few tenths, what was used prior to digital?

    Plain old liquid in glass. Read (for the USA) to 1/10 F then ROUNDED by the observer to whole degrees F that were sent in on paper sheets. Lacking a reading, the NOAA guidelines say the observer is allowed to GUESS.

    In other countries, I don’t know the rounding policy nor do I know if the electronic ones also get rounded.

    c. how are all of them worldwide calibrated?

    Some are good, some are lousy. Hawaii was “hot” for a few months before anyone figured it out. I think they did not bother to remove the ‘new record’ that was set…

    2. My point is: How can anyone say that temperatures have risen by X amount, when in all probability that amount is within the variance for error of all instruments used, especially prior to digital readouts, and especially since the number and location of sites seems to vary constantly, and since many are exposed to urban growth around them.

    BINGO!

    All that 1/10 C and especially the 1/100 C numbers are just playing in the error bands of the process.

    Now you will get folks who try to assert that the law of large numbers and the central limit theorem will let you know the AVERAGE to 1/100 C even if the data are dodgy. But in GIStemp, for example, you carry temperature data all the way to the ‘fill in the GRID / BOX’ step where the (now 1000 ish) thermometer records fill in 8000 boxes. Yes, 8 boxes per thermometer. So you don’t get a lot of ‘large numbers’ in that average. You get, for the monthly anomaly, ONE highly processed number for many boxes. Said number starting life as an average of Daily (MAX+MIN)/2 so you have at most 31 of them that then get averaged.

    The “Central Limit Will Save Us” folks like to trot out 7000+ thermometers with a gazillon records and start calculating the theoretical precision on the average of all of them. They get grumpy when you say the code doesn’t do that…

    They get grumpier when you say it’s only 1000 real thermometers (now) and that many of the things they are averaging are just made up “fill in” data based on that other crappy data and a dodgy method.

    They get very much grumpier when you say that after the code has processed it a dozen times or more they will be lucky if the original 1 C can be held steady as “scientific programming” has been found to do a terrible job of keeping precision in their calculations:

    http://chiefio.wordpress.com/2010/02/07/of-hypothetical-cows-and-real-program-accuracy/

    (For example, in GIStemp I found a single line that ‘warmed’ 1/10 of the records by 1/10 C due to a bit shift in the order in which they did the F to C conversion… )

    So I’d suggest not talking about it too much unless you really want to have a lot of grumpy “scientists” in your face claiming you don’t understand statistics (when you understand them just fine…) 8-}

    But you’re right… ;-)

  101. p.g.sharrow "PG" says:

    Just normal bureaucratic SNAFU, too many chiefs and not enough indians. Must save money, layoff low level workers. Must reduce load on remaining workers, reduce data entry and cover up with software. Not a problem, we only need a few data entrys, the rest are simulated in the software.

    The raw data is likely still out there and still being gathered. Just not being entered into the computer data base. The bureaucratic system to record the local weather data was set up to operate with very little or no on budget cost and oversight, so it is most likely still in operation. Bureaucratic inertia is one of the most powerful forces in the world.

    Getting that information into the computer database is the bottleneck as that costs large amounts of money in one place and budget. Now there are only 3 data bases that need to be controlled to set policy. As long as they can control the output, they control the argument, and the raw database entries are made at one time and shared to save time and money. Each of the 3 data bases are then modified to suit the needs of the holder. Do I correctly understand the process?

  102. hswiseman says:

    Before everyone hyperventilates on this topic, there are some questions to ask.

    Is there a principled method for either selecting or deselecting a reporting station?

    Does the principled method make sense scientifically/statistically-how was it derived and tested?

    Is the method as described actually being practiced?

    If these questions can be answered affirmatively, all the huffing and puffing doesn’t mean too much. The temperature compilers should be credited for creating an accurate collection method that reduces workload and complexity.

    If the answer to any of the questions is a negative, or if there is no available elaboration of the methods and justifications, then there are lots of worthy questions and alternative analysis to be done.

    If one wishes to have a conversation about science, one should adhere to some form of scientific approach…a stepwise analysis of the factors and methods one wishes to discuss and criticise. Otherwise it is just a bunch of barstool blowhards bloviating.

  103. James Sexton says:

    E.M.Smith (14:37:46) :
    For folks wanting to “try this at home” the ftp URL is:

    ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v2

    Dang it E.M.!!!! Now I’m running a muck in their ftp site. You know they’ve actually a file named virusnt.exe? Now I’m in a quandary. I’d like to run it and see what it does, but if it loads a virus, how would I complain? It says virus…..lol

  104. DirkH says:

    “E.M.Smith (15:09:21) :
    [...]
    (For example, in GIStemp I found a single line that ‘warmed’ 1/10 of the records by 1/10 C due to a bit shift in the order in which they did the F to C conversion… )”

    They do know about this thing called floating point numbers, do they, those “scientist” people?

  105. James Sexton says:

    hswiseman (15:10:56) :

    See the E.M.Smith (13:23:08) : post. There are actually 2 different explanations but neither make much sense and in these recent cases the actions (deleting from database) are contradictory to the explanation. E.M. has provided the links in his post.

  106. Willis Eschenbach says:

    A bit of analysis of the deleted stations

    Urban 214
    Suburban 105
    Rural 166

    Median elevation 160 metres

    Airport 196
    Non Airport 289

  107. DirkH says:

    “hswiseman (15:10:56) :
    [...]
    If one wishes to have a conversation about science, ”

    Maybe you can contribute a convincing idea as to why they drop the thermometers instead of denouncing this debate? Do you think Gavin Schmidt’s explanation that they have far more data than they needed is even slightly convincing? Or in any way scientific?

  108. E.M.Smith says:

    Tom in Texas (14:13:14) : EMS, have you (or anyone else) checked to see if V1raw = V2raw?

    Well, they are not exactly “raw”…. The official term is “unadjusted” for GHCN. For USHCN they are explicitly adjusted and USHCN is significantly colder than USHCN.v2 but for GHCN I think there have been some spot checks showing GHCN.v2 “unadjusted” is close to the original reports. Don’t know about GHCN.v1 but I think it can be downloaded from the same ftp site listed in comments above (just go above the ‘v2′ part of the link ;-)

    Oh, and there is also a “GHCN Adjusted” that tells you how NOAA would like to re-imagine the world and that is highly different from ‘unadjusted’ with the past made much colder…

    Frank Mosher (13:52:48) : E.M Smith. Much thanks for your tireless, concise, and easy to understand analysis. fm

    You are most welcome. I attribute the ‘tireless’ part to massive infusions of coffee (Irish and otherwise ;-) along with tea… ;-)

    @George: Say “Hi” for me! And there is an email address buried in the ‘about’ box on chiefio.wordpress.com if they want to send me an email.

    @James Sexton: Be Advised: Russia has a European number (635?) for everything West of the Urals and an Asian number (222?) for everything to the east… so you have to check both continents to see WUWT for Russia…

    @Margaret: http://www.wunderground.com/ has the reports for most of the GHCN dropped thermometers. I’d also wager there are METARS and other on line sources. A lot of a ‘new collection’ can be done from a comfortable keyboard at home…

  109. Deb says:

    E.M.Smith (14:37:46) :

    Sheesh. Greenland gone too?

    Ah, but it seems there might still be a couple thermometers left in Newfoundland within that 1200km band that can correlate north. I mean if the beaches in Peru and the Amazon jungle are good enough for Bolivia then surely the rocky harbours of Nfld/Labrador or the woods of Quebec are a good enough match for a giant ice cube. ;-) /sarc

    Thanks for checking. You’ve been doing really excellent work sorting through all this data.

    I really don’t understand how these people think they can get away with this sort of thing. Like many of us here, if I ignored and deleted data like this while doing my job I’d be fired!

  110. b.poli says:

    Anthony:
    I had a look at the two A-stations in Germany:

    61710020000 LIST/SYLT 55.02 8.42 29 0R -9FLxxCO 1x-9WATER A
    Looking at Google Earth there seems to be no stucture (51.50 9.95) which could represent a Stevenson Screen. Even if so it would be plain rural and nowhere near an airport (A for Airport). The Sylt airport is more to the south near Westerland: 54,91N 8.34E

    61710444000 GOETTINGEN 51.50 9.95 171 214U 124HIxxno-9x-9WARM DECIDUOUS A
    Looking at Google Earth there seems to be a stucture (51.50N 9.95E) which could represent a Stevenson Screen. Even if so it is plain rural and nowhere near an airport (A for Airport). The city of Goettingen doesn’t know anything about an airport (website).

    Please look and correct me.

    Are they removing stations from fictional airports just to tell us “We removed the airport stations and left the rural ones”?

  111. Ed Moran says:

    I’ve just made a quick scan of the comments and worthy almost all are but didn’t see any reference to your superb headline.

    You can run, you can hide the data but The Langoliers will come through the trees and munch it all.

    (That will have lots of people saying “What’s this guy on?”)

  112. Smokey says:

    From the Yale article’s explanation:

    For many of the world’s stations, observations are still taken and recorded by hand, and assembling and digitizing records from thousands of stations worldwide is burdensome.

    If they’ve been digitizing the records, that makes the claim that the earlier temperature records were dumped questionable. Either the raw data was digitized and is still available, or someone is telling tall tales. Yale dropped the ball by not following up on that assertion.

    I have another question: when someone reads a temperature from a mercury thermometer, and it’s, say, 65.7° F, or 18.7°C [as close as the divisions on a glass thermometer can be eyeballed], do they just average up and record 66° or 19°? What temperature do they record, if it looks like exactly 65.5° F, or 18.5°C?

    A change in policy could account for this divergence: click

    And “adjusting” the raw data can dramatically alter the trend line: click

    Also, the comment mentioning that digital thermometers are accurate to within a few tenths of a degree is technically accurate. That would be for a thermometer in calibration, with its calibration traceable to N.I.S.T. [formerly the National Bureau of Standards].

    But in practice there are ascending levels of calibration. The calibration interval can be lengthened or reduced. And the thermometer can be calibrated to an instrument standard, or to a primary physical standard like the triple point of water.

    Under the current system, a worldwide temperature change of a fraction of a degree can not be observed with any confidence. And the record can be manipulated in other ways, such as those mentioned in this article, making the temperature record unreliable.

    For those who haven’t seen it, here’s a blink gif of NOAA raw vs adjusted temperatures going back to 1880: click [give it a few seconds to load]

  113. Jan says:

    So, no thermometers left for Czech Republic at all??
    (Oldest temprature record -regular since 1775- in Central Europe is Praha-Klementinum record – incorporated by GHCN in the record of Praha/Ruzyne – or more exactly “connected” into international airport Praha-Ruzyne record – now also dropped?)
    I was just looking at Giss and found maybe interesting things:
    1. they state they produce their world anomaly maps from GHCN “Global Maps from GHCN Data” (http://data.giss.nasa.gov/gistemp/maps/)
    2. I looked absolutely randomly in the 2009 data for the stations from Chiefio list at
    (http://data.giss.nasa.gov/gistemp/station_data/)
    and
    NEW YORK CENTRAL PARK – data AUG-DEC missing
    BARCELONA – data SEP-OCT missing
    ILES GLORIEUS – data SEP-OCT missing
    NALUT – all except JAN missing (very interesting record…)
    CHIFENG – APR, SEP-DEC missing
    KRMANSHAH – MAY, AUG, DEC missing (very interesting record too)
    SAIKHAN-OVOO – APR missing
    TERESINA – JUL, AUG, NOV, DEC missing (aso very interesting record)
    LAMENTIN/MARTINIQUE/FT DE – SEP-NOV missing
    etc. etc.
    – one wonders how anybody on earth can – in the era of permanent manned space missions – use such incomplete records for global temperatures analysis?? …and now I’m fully convinced Hansen urgently needs a custody…
    In all cases the Giss 2009 global anomaly and temperature maps are just a big nonsense or lie.
    One hardly finds a station without some 2009 data missing. What this people at NOAA, NASA are doing? What for the taxpayers and others pay them? To have records even a high school student would be ashamed of? Unbelievable!!
    This whole thing stinks like whole that prospering bunch of polar bears around the “mostly no GHCN, GISS thermometers at all” Arctica…

  114. James Sexton says:

    Simply unbelievable. 1500 stations are too much to track? BS. I work at a very small electric utility. We have about 3100 meters. I can graph each meters hourly use each day. Of course, from time to time, I don’t get reads on all my meters all the time. I don’t throw out the usage data. And I don’t average or extrapolate some of the meters usage to give my total usage to my boss. I’m expected to be correct. I would assume the NOAA has a bit more resources at their disposal than my little COOP. I suppose I could volunteer to give them a bit of instruction as to how to properly manage such a huge(snicker, snicker, lol, rotflmao, rotflmaopmp!!!) database. Sigh, now to clean up and go drink some beer contemplating the case of the disappearing thermometers. Cheers to all!!!

  115. rbateman says:

    Removal of entire regions (like the Arctic) makes for -9999’s in the data set.
    They show up as pegged in the scale on the homogenized map.
    Rather ingenious method of creating ‘hot spots’ in lieu of no data.
    Following the trend of station removal/relocation you get an entirely new data set that cannot be calibrated against the old.
    Satellite data needs to be calibrated to ground. Simply alter the ground stations and spice to taste. Move data collection equatorward for the winter, homogenize areas lying poleward to new improved warmer locations.
    Walla …. It’s getting hotter faster than previously arranged.

  116. son of mulder says:

    Who said “Climate data ‘not well organised’ “?

    Who “agreed that two periods in recent times had experienced similar warming. And he agreed that the debate had not been settled over whether the Medieval Warm Period was warmer than the current period.”?

    http://news.bbc.co.uk/1/hi/sci/tech/8511701.stm

    I hear fingernails on the granite face

  117. M. Simon says:

    You think bookies in Vegas would buy that..?

    If they were in charge of the adding.

  118. E.M.Smith says:

    DirkH (15:22:38) : They do know about this thing called floating point numbers, do they, those “scientist” people?

    They know about them, they just don’t use them very well…

    Details:

    http://chiefio.wordpress.com/2009/07/30/gistemp-f-to-c-convert-issues/

    Oh, and this one too:

    http://chiefio.wordpress.com/2009/11/21/hadley-hack-and-cru-crud/

    where the CRU crew ‘fix’ a bad handling of overflow via removing a bad data item from the input and leave the program broken…

  119. tarpon says:

    In today’s OBAMA 2010 DEFENSE REVIEW on security threats to America, it did not include the word ‘Iran’ or Islamic. The entire report is 128 pages long. There were 8 pages devoted to the dire threat to the USA from global warming.

    Maybe the stations that had to be shot were naughty for telling the truth?

  120. Frank Lansner says:

    Here some more info on the IPCC made up temperatures for Scandinavia:
    http://hidethedecline.eu/

    The site also has an A – Z of sceptic viewpoints and knowledge for all newcomers that want fast effective introduction to the climate debate.

    K.R. Frank

  121. hswiseman says:

    I am not denouncing debate in the least, merely saying that debating the deletion of thermometers without debating the underlying rationales is an utter waste of time. I don’t presume that the reasoning is either perfect or utterly flawed. I don’t know. I try to avoid ascribing motives (it is getting harder and harder). And it is also possible that once pointed to the methodology that I don’t have the science chops to do the analysis myself. In which case I contribute to the debate how? Unless someone does the work for my benefit I am just making guesses. I can (and sometimes do) throw out a layman’s observation or common sense analysis, and like a blind squirrel, I find an acorn now and then. Some very smart folks have been kind enough to challenge or validate my reasoning from time to time and I have learned alot.

    The deletion of thermometers and reducion of observational science is interesting. Check. Now proceed from first principles. It is alot more interesting to debate the validity of method, reasoning, theory, process, etc, than the fact that any particular station has been included or excluded absent context. E.M. Smith says he has done the analysis of the justifications/methods and has found them unconvincing or otherwise unsatisfactory. I appreciate his undertakings but is he the last word on this? Smith makes no such claim himself, and I suspect he would be happy to discuss, debate and argue the matter with someone representing the the GHCN perspective. If they showed that they were right, I believe he would agree with them. But Smith and anyone else with the temerity to even ask a question has been excoriated and treated as someone beneath contempt, unworthy of acknowledgement or engagement. I don’t care to perpetuate the disgraceful attitude of the climate establishment with my own prejudice, paranoia and contempt. I have curiosity about nature and I think that it is important to understand what impacts we are having on the planet with the discharge of CO2 and other pollutants. Kneejerk conspiracy theories don’t advance anyone’s knowledge, nor does a presumption that I know all the answers before the questions are asked.

  122. E.M.Smith says:

    b.poli (15:50:58) : Are they removing stations from fictional airports just to tell us “We removed the airport stations and left the rural ones”?

    No, just that the meta-data are really crappy. But that’s OK, it’s not like they use the URS, elevation, population, etc. data to control the processing or that they expect to direct the world economy based on the results…Oh, wait, they are 8-{

  123. DirkH says:

    “E.M.Smith (16:13:44) :
    [...]
    Details:

    http://chiefio.wordpress.com/2009/07/30/gistemp-f-to-c-convert-issues/

    I see this incredible sloppy line

    nint( 50.*(temp-32.)/9 )

    and your analysis about the compiler dependency. For non programmers: they failed to define explicitly whether the program shall multiply with 50.0 first or divide by the integer value 9 first. Worse, why even mix integer and float constants here? This is a precision-killing bug, a reliability foulup, a numeric SNAFU. Probably nobody ever reviewed the code and nobody ran a detailed test.

  124. Cement a friend says:

    Strange that they put Indonesia in the Pacific area. They must have had pretty old maps if they class Jakarta as Paddy Fields. Wikipedia (I know I should not quote that but it puts in the direction) states that the greater Metropolitan area has 23 million people (2004) and is second largest in the world. I know that it takes a couple of hours on main roads and freeways to get out. Once out, you get into the hills and mountains. There are many mountains over 2000m.
    I would image that Bandung (an old colonial Dutch settlement with a catholic cathedral) in the centre of the south-east of Java should have a long weather record dating back a couple of hundred years. I think it was the seat of Dutch government and that there is a University there.

  125. Adam from Kansas says:

    To go along with all the talk of global temps. the January record at UAH is making the rounds in the blogosphere, like here
    http://www.techeblog.com/index.php/tech-gadget/hottest-january-on-record

    This blog has a lot of interesting stuff, the bad news is that they decided to get their information/explanation from Joe Romm.

  126. Andrew30 says:

    DirkH (16:30:04) :
    “nint( 50.*(temp-32.)/9 )”

    Is this a temperature scale conversion, F to C, if so should the 50.0 not actually be 5.0?

  127. RayG says:

    Fascinating quote from a Phil Jones-BBC interview today. This is part of his answer to the first question that the interviewer asked. Note the first sentence.

    “…Temperature data for the period 1860-1880 are more uncertain, because of sparser coverage, than for later periods in the 20th Century. The 1860-1880 period is also only 21 years in length. As for the two periods 1910-40 and 1975-1998 the warming rates are not statistically significantly different (see numbers below)….”

    If the data are more uncertain because of sparser coverage, it follows that the same rule must apply to deleting stations today. Did Jones go “off message” here?

    news.bbc.co.uk/2/hi/science/nature/8511670.stm

  128. David Hyde says:

    A caution on this one: I get 1179 stations

    http://www.dplot.com/agw/station_count.png

    from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v2/zipd/v2.mean.zip, not 1113. And since our 2009 figures agree (1597 stations) I assume we’re using the same source. Mr. Smith may be right and stations are dropping like flies… but maybe the count will go up tomorrow and the day after that and the day after that. It doesn’t seem unreasonable to assume that some stations are just slow to report.

  129. Neil Crafter says:

    one would think that as AGW is such a professed ‘crisis’ that the number of temp measuring stations would be increasing rather than decreasing. Just goes to show the way GISS think, that the opposite is the case.

  130. Mac the Knife says:

    The Langoliers! Now that is sooooooo apropo! In that schlocky B grade flick, the langoliers were chewing up chunks of space/time or some such, as I remember, and threatening to cut the woeful aircraft and passengers off from a return to their reality. It works really well as analogy for the unreal mastication of GHCN reporting stations and data sets by the NOAA Langley-Liers! As more recording sites are dropped and the data sets backfilled with approximations from the remaining sites, we get analyses that are more and more estranged from scientific reality.

    A very good analogy, indeed! Esoteric… but Good!!!

  131. Mac the Knife says:

    hswiseman (16:21:50)
    “I have curiosity about nature and I think that it is important to understand what impacts we are having on the planet with the discharge of CO2 and other pollutants. ”

    What is your basis for concluding that CO2 is a pollutant? How does a gas essential for all flora growth and, as flora is fodder to fauna, the root of all fauna growth on the planet as well, justify classification as a contaminate?

  132. Ron Broberg says:

    Regarding DFW. There are 10ths deg C:

    rbroberg@ubuntu910:~/Downloads$ ls *failed*
    v2.max.failed.qc v2.mean.failed.qc v2.min.failed.qc

    rbroberg@ubuntu910:~/Downloads$ grep 425722590000 *failed* | grep 2010
    v2.max.failed.qc:425722590000 2010 1 -22
    v2.mean.failed.qc:425722590000 2010 1 -62
    v2.min.failed.qc:425722590000 2010 1 -103

    So NOAA was showing a mean monthly average of -6.2C.
    NOAA GHCNv2 did the right thing tossing DFW out of the data set.
    But what caused this obviously erroneous data range?
    NOAA processing or DFW reporting?

    Don’t have an answer to that.

  133. Ron Broberg says:

    And just fyi,

    here is my station count …
    (using v2.mean.Z not v2.mean.zip)

    rbroberg@ubuntu910:~/Downloads$ cat v2.mean | grep 2010 | cut -b 4-9 | sort -n -u | wc
    1187 1187 8309

  134. Sam says:

    I can understand removing some stations in countries with endless wastes of flat land where there is nothing much in the way of geographical features to affect temperature-chaging microclimates. But in countries with many mountanous areas it’s incomprehensible.

    I’m not sure where exactly the deleted temp station is/was at Clermont Ferrand, but I used to live about 10 miles S/W of the city. It’s set on the edge of a plain to the East, with a quite sizeable mountain range immediately to the West, which retains its snow cover til early summer (esp on the Puy de Dome). I lived in a South facing village built into a rock face, which has been occupied since Mesolithic times. We could sit on our terrace in tee shirts in mid winter bathed in sunshine whilst across the village valley on the facing slope the temp stayed well below freezing all day, every day, for months.

    The entire area has any number of wildly differing microclimates within a few sq miles: siting of a temp station would be an interesting exercise! Gavin Schmidt’s remarks as quoted above have left me with even less respect for his integrity than I had before.

    The terrifying thing is that these arrogant people still have the ear of policymakers worldwide. And their ‘useful idiots’ – teachers and politicians – have infected all our children

  135. greg2213 says:

    p.g.sharrow “PG” (15:09:50) :

    Just normal bureaucratic SNAFU, too many chiefs and not enough indians. Must save money, layoff low level workers. Must reduce load on remaining workers, reduce data entry and cover up with software. Not a problem, we only need a few data entrys, the rest are simulated in the software.

    With the bajillions of $$$ being spent on “climate studies” and with the massive “WE’RE ALL GOING TO BAKE!!!” group-think among the college crowd one would think they (GISS & Co) could nab a few (dozen) interns to do some of the work.

  136. Ron Broberg says:

    David Hyde (17:18:51) : It doesn’t seem unreasonable to assume that some stations are just slow to report.

    There are only a handful of stations listed as having failed QC in 2010. I suspect that you are right to expect to see more of the ‘missing’ stations show up in the v2.mean file.

    rbroberg@ubuntu910:~/Downloads$ grep ” 2010 1 ” v2.mean.failed.qc | cut -d ” ” -f 1 | sort -u -n
    109646550002
    122637400001
    137616790003
    207420710003
    207424750003
    304890560004
    314804160003
    314804230003
    425722590000
    425725970000
    643082150001

  137. Bob Koss says:

    E.M.Smith,

    I think you might want to keep your powder dry on the number of stations. A lot of stations may not show up until a few months into the year, due to late reporting. The list you have now would only include those having already reported January temperature.

  138. North of 43 south of 44 says:

    You need to do a file compare and list any new additions as well.

  139. E.M.Smith says:

    Andrew30 (16:57:24) : Is this a temperature scale conversion, F to C, if so should the 50.0 not actually be 5.0?

    The data are in 1/10 C increments, so 102 is 10.2 F.

    Ron Broberg (17:59:43) : here is my station count …
    (using v2.mean.Z not v2.mean.zip)

    rbroberg@ubuntu910:~/Downloads$ cat v2.mean | grep 2010 | cut -b 4-9 | sort -n -u | wc
    1187 1187 8309

    Two bits of ‘finesse’. One is that you need to grep for …………2010 to avoid getting any station ID that has 2010 in the middle of it counted as a ‘year’ (that is, you don’t want the hypothetical station 42520101000 to be counted as the year 2010) and I conflated any stations with the same station ID (8 digit) but different ‘modification flags’. That is, I count 425123450001 and 425123450002 as the same station.

    Also, on the ‘failed QA': This is speculation, but do you think that snow in Pensacola Florida will cause it’s February data to “fail QA”? The simple fact is that we’re getting historic snow (and the attendant cold) from our shift to a colder regime (IMHO due to a sleepy sun and the PDO flip to the cold phase) and if we’ve got gatekeepers on the data that don’t believe those values are ‘real’ then they will ‘fail QA’.

    So I’d like to have assurance that tossed records are really bad, and not just being ‘unbelieved’.

  140. Norm/Calgary says:

    Inconceivable! May statistics are reported years later, as in 2006 is the latest available data. The IPCC/NOAA/NASA should try to use every resource available. The most recent 1/3 to die were presumably reporting in real time otherwise they would have been purged in the early 1990’s; so what has occurred now to warrant this latest extinction?

    They could/should have a preliminary report based on the real time stations complete with their manipulations and/or corrections. They should then report based on all stations, using corrections for missing data where required at some point after most of the data has been received. In addition, since we are talking computers here there should be no problem adding a late reporter.

    The *bonus* from an approach like this would be that the favored real time corrected/manipulated data set temperatures from a given month could be compared to the *actual* reported a few months later. This would verify or repute the ever smaller real time data, and allow corrections to the smaller data set to make the real time data much more accurate. But until we have this comparison we skeptics have no faith in the shrinking real time data set.

    Why is this not done? It’s not like we’re going to implement something drastic that has to have up to the minute data.

  141. E.M.Smith says:

    Bob Koss (18:24:27) : I think you might want to keep your powder dry on the number of stations. A lot of stations may not show up until a few months into the year, due to late reporting. The list you have now would only include those having already reported January temperature.

    The claim is that once the due date passes for a month they don’t go back and put stations in who report late. That was one of the excuses I ran into on Bolivia (that they reported a few days after the ‘cutoff’ for the month, each month). While I’d love to hear that they were accepting late updates, I’d also want to see the changed procedure published… and Bolivia could then be put back in.

    The ‘readme’ on the ftp site says:

    “New monthly data are added to GHCN a few days after the end of
    the month. Please note that sometimes these new data are later
    replaced with data with different values due to, for example,
    occasional corrections to the transmitted data that countries
    will send over the Global Telecommunications System.”

    So they say they put the data set up a few days after the end of the month ( I got it on the 8 th which seems to be about when they first put things up) and the only caveat on it is that individual values might change from corrections later, not that whole countries might be slipped in later. “Occasional” does not mean 30% to me… and ‘data replaced’ is not “whole countries put in”.

    Further down there is a statement that ‘QA failed’ data might be put back in if someone can convince them the data are real. So folks in Dallas might want to consult the January newspaper reports and see if it was frozen the whole month ;-)

    So, no, I don’t see much reason to ‘keep power dry’. I could easily see a single digit number of thermometers change from “QA failed” being itself QA’d but if they start putting 5% or more of the records ‘back in’ I think they are violating their published methodology… (though I’d welcome it). I’d also welcome them putting the thermometer record in the v2.mean data set with the required -9999 missing data flag if a site has failed QA but has not been deleted. That is what they are supposed to be doing, but the DFW case implies that a site is simply left out until (whenever) in a year it gets it’s first valid datum, then the whole year-to-date of missing data flags get written. Sloppy at best.

    Oh, and GIStemp would need to put a caveat on all those monthly horror reports that they are being made on partial data and not to be trusted.

    But basically, I’d be fine with them putting in the readme file that the data are not to be trusted when published for a few months… if that’s what they are doing.

  142. hswiseman says:

    Mac
    The monotonic increase in C02 is well established and the preponderance of the evidence supports a human source.

    http://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2_data_mlo.pdf

    A discharged material can be pollutant even if it is naturally occuring and not otherwise harmful. We regularly emit C0 in small, non-lethal amounts into the atmosphere. Like C02, in high concentrations C0 will kill you. Not too many people would argue that tailpipe C0 is not a pollutant. The world at large has made a cost-benefit determination that creating additional C0 provides so many benefits to man-kind that it tolerates and adapts to the small burdens of potential C0 poisioning in closed spaces. The cost-benefit analysis for C02 cannot be conducted accurately if the science through which cost is determined is faulty or incomplete. Burn fossil fuels and create energy, plant food and some as yet undetermined increment of infrared radiation absorbtion. The benefits attached to the creation of C02 are pretty obvious. The costs are in dispute, but most likely less than the doomsday crowd would have you believe. Whatever the costs, I would argue that the adaptation burden should be borne by the parties with the lowest cost of risk management. Displacing a 1000 inundated islanders makes more sense than beggering the entire world population. The actual cost of displacement should be borne by the parties that garner the most benefit. I would I respectfully disagree if your argument is that we should not even bother to do the calculus.

  143. Bernd Felsche says:

    It’s a bit disturbing when the only weather stations that remain are in the CBD of a city.

    I found another local source of weather data in the local Department of Agriculture
    (http://www.agric.wa.gov.au/PC_93316.html) here in Western Australia.

    Although the nearest station (http://agspsrv34.agric.wa.gov.au/climate/clig/climinfo/awsdata/MD.htm) doesn’t have any really old data online from before the station was automated, th period since 1995 to present shows no warming at all. Temperature follows insolation very closely (R^2 > 0.8); delayed by between 1 and 2 months; presumably due to thermal capacity.

  144. Ron Broberg says:

    E.M.Smith (19:29:57) : Two bits of ‘finesse’…

    Yup. Good catch. I stand corrected.
    (also, the field range for the 5 char station id is 4-8)

    grep ………..2010 v2.mean | cut -b 4-8 | sort -n -u | wc
    1179 1179 8253

    So I am now in agreement with David Hyde.

    So I’d like to have assurance that tossed records are really bad, and not just being ‘unbelieved’.

    Not hard to figure out.
    Just grab the daily data at WU for January and calculate.

    Here is the link for Jan 1, 2010
    http://www.wunderground.com/history/airport/KDFW/2010/1/1/DailyHistory.html?format=1

    I tweaked the scripts here for Tmean using Tmax,Tmin presented here:
    http://rhinohide.wordpress.com/2010/02/07/charles-pierce-methods-of-monthly-means/

    I calculate the monthly mean for DFW in Jan 2010 to be 44.4F = 6.9C

    That is significantly higher than the -6.2C that NOAA threw out. (sign inversion?)

    How does that compare to previous NOAA v2.mean for DFW for January?
    (recall that v2.mean is in 10thsC so the number we are comparing is 69) …

    for i in `seq 2001 2009`; do grep 425722590000 v2.mean | grep “$i ” | cut -b 1-21; done

    4257225900002001 59
    4257225900002002 86
    4257225900002003 64
    4257225900002004 92
    4257225900002005 98
    4257225900002006 128
    4257225900002007 57
    4257225900002008 83
    4257225900002009 90

    It obviously lies in the low end of the range for the decade, but not the lowest. Out of curiousity, here are the previous years with lower DFW Jan mean temps per NOAA v2.mean ….

    grep 425722590000 v2.mean | cut -b 1-21 | sort -r -n -k2
    4257225900001968 69
    4257225900001997 67
    4257225900001960 67
    4257225900001964 66
    4257225900001974 65
    4257225900001957 65
    4257225900002003 64
    4257225900001983 64
    4257225900001996 61
    4257225900001991 60
    4257225900002001 59
    4257225900001973 59
    4257225900001959 58
    4257225900002007 57
    4257225900001988 57
    4257225900001961 49
    4257225900001970 48
    4257225900001966 46
    4257225900001962 42
    4257225900001984 41
    4257225900001949 39
    4257225900001948 35
    4257225900001985 32
    4257225900001963 32
    4257225900001979 19
    4257225900001977 15
    4257225900001978 10

    This still doesn’t tell us which end of the pipe broke with DFW data reported to NOAA. I did a quick peek into the GHCN daily ftp directory.
    ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/all/USC00412242.dly

    Only 10 days of data with Tmax and Tmin with GHCN daily. But these 10 days also yield a Tmean of 4.6C. So two independent methods are converging on 4C as being the about right for DFW for Jan 2010. Still not sure how the -6.2 ended up in the v2.mean. But NOAA GHCN was correct to reject it. Nothing in the GHCN daily file resembles the numbers that GHCN holds in v2.max and v2.min for DFW.

  145. Ron Broberg says:

    Bah. I need an editor. Strike this: So two independent methods are converging on 4C as being the about right for DFW for Jan 2010.

    The point about converging on 4C is in error. The Weather Underground wx data yields 6.9C. I had earlier mistakenly used the dew point as Tmin – that erroneous calc yielded a bad Tmean of 4.somethingC.

  146. Ron Broberg says:

    E.M.Smith (19:29:57) :… and I conflated any stations with the same station ID (8 digit) but different ‘modification flags’. That is, I count 425123450001 and 425123450002 as the same station.

    My method does the same. By selecting all 2010 records, but then cutting just the 5 char station id and removing dupes, I only count station ids and multiple records for a station are discarded. Returning the same number of 2010 stations as David Hyde. Maybe you can post your station counting code …

    grep ………..2010 v2.mean | cut -b 4-8 | sort -n -u | wc
    1179 1179 7074

  147. Ron Broberg says:

    @ Deb (13:28:27) :

    EGEDESMINDE
    GODTHAB NUUK
    NORD ADS
    DANMARKSHAVN
    ANGMAGSSALIK
    PRINS CHRISTI

    Looks like Greenland has been turfed

    Nah. All these stations remain in GHCN for Jan 2010.

    grep ^431 v2.mean | grep ^…………2010 | cut -b 4-8
    04220
    04250
    04312
    04320
    04360
    04390

    To get their names, we can grep it out of v2.temperature.inv

    for i in `grep ^431 v2.mean | grep ^…………2010 | cut -b 4-8`; do grep ^…$i v2.temperature.inv | cut -b 1-40; done
    43104220000 EGEDESMINDE
    43104250000 GODTHAB NUUK
    43104312000 NORD ADS
    43104320000 DANMARKSHAVN
    43104360000 ANGMAGSSALIK
    43104390000 PRINS CHRISTI

  148. Bob Koss says:

    E.M.Smith (19:52:09) :

    I have several of their databases from the past couple years. Here are some of the download dates and number of stations containing data for 2008.
    02-08-2008 928
    11-14-2008 1233
    01-20-2010 1613

    I mentioned in a post at CA a couple weeks ago they added 100’s of stations back in at the beginning of 2009.
    http://climateaudit.org/2010/01/30/lazarus-of-the-thermometers/

    The two databases from 2008 indicate they add them as they get data throughout the year.

  149. E.M.Smith says:

    E.M.Smith (19:52:09) :
    “Bob Koss (18:24:27) : I think you might want to keep your powder dry on the number of stations.”

    So, no, I don’t see much reason to ‘keep power dry’.

    Well, in thinking about this I decided that I was depending on NOAA:

    1) Following their own statements.
    2) Doing things that make sense and are consistent.
    3) Following proper professional data set update standards.
    4) Assuring that a broken (i.e. un-ripe) data set is not released.
    5) Having a largely automated and standardized process for doing things.

    Basically, I’m expecting professional standards of behaviour that may not be in evidence… The more I pondered, the more I realized there is little reason to expect any of those 5 behaviours given the things we have seen from CRU, GISS, GIStemp, et. al.

    So I decided to go looking for the published “availability date” standard.

    I could not find one.

    If they have one, it’s well hidden from a casual user of their data.

    But what I did find was a letter in the FOI set at this web site:

    http://junkscience.com/FOIA/mail/1226959467.txt

    That paints a rather haphazard picture of the ‘update’ process. Given that haphazard method, I’ve decided there really IS no reason to think that the GHCN data set is ever “done” or “ready”. It is just a “work in progress” and is a bit “slapdash” at any time…

    Some quotes:

    From: Phil Jones
    To: Gavin Schmidt
    Subject: Re: GHCN
    Date: Mon Nov 17 17:04:27 2008

    Gavin,

    First the figures are just for you – don’t pass on!!! I don’t normally see these. I just asked my MOHC contact – and he’s seen the furore on the blogs.

    So about a year and 3 months ago. Probably still what happens…

    These 3 paras (below) are from the GHCN web site. They appear to be the only mention I can see of the WMO CLIMAT network on a web site.

    I could not find much on the web site either, but perhaps searching with chunks of the material you are looking for as keys would find it? Something to explore later…

    The rigorous QC that is being talked about is done in retrospect.
    They don’t do much in real time – except an outlier check.

    OK, so QC is sort of an after the fact glue on… and the web pages are doing some sellers puff about “rigor”.

    Anyway – the CLIMAT network is part of the GTS. The members (NMSs) send their monthly averages/total around the other NMSs on the 4th and the 18-20th of the month afterwards.

    So, by the 8th the data set OUGHT to have been complete. But it comes around again on the 18-20th. That means we might get the data ‘fixed’ next week… but maybe by the end of the month for sure?

    Few seem to adhere to these dates much these days, but the aim is to send the data around twice in the following month.

    Or NOT…

    Data comes in code like everything else on the GTS, so a few centres (probably a handful, NOAA/CPC, MOHC, MeteoFrance, DWD, Roshydromet, CMA, JMA and the Australians) that are doing analyses for weather forecasts have the software to pick out the CLIMAT data and put it somewhere.

    “put it somewhere”… that’s comforting…

    At the same time these same centres are taking the synop data off the system and summing it to months – producing flags of how much was missing. At the MOHC they compare the CLIMAT message with the monthly calculated average/total. If they are close they accept the CLIMAT. Some countries don’t use the mean of max and min (which the synops provide) to calculate the mean, so it is important to use the CLIMAT as this is likely to ensure continuity.

    “how much is missing” flags… “If they are close”… now there is a fine standard metric of acceptable error band. /sarcoff>

    If they don’t agree they check the flags and there needs to be a bit of human intervention. The figures are examples for this October. What often happens is that countries send out the same data for the following month.

    “a bit of human intervention”… “often happens” “same data”. So, we have no idea if there are loads of bad data that was just broken in both the dailies and the monthlies (if they are both broken by, oh, reading a sign wrong on the dailies, it will just sail through?!) or if blocks are just repeated because what ‘often happens’ happens? And we have no idea what a “bit of human intervention” is, or if there are standards for it or for how long it might take. AND we wait until the 18-20th to get a second bite at the apple and hope one of them works out to be right…

    This happens mostly in developing countries, as a few haven’t yet got software to produce the CLIMAT data in the correct format. There is WMO software to produce these from a wide variety of possible formats the countries might be using. Some seem to do this by overwriting the files from the previous month. They add in the correct data, but then forget to save the revised file. Canada did this a few years ago – but they sent the correct data around a day later and again the second time, after they got told by someone at MOHC.

    So if someone notices a really bad screw up, they can get the data sent around again. Sort of whenever. In some format or other. Possibly overwritten with old values, if they remembered to save the file..

    My guess here is that NOAA didn’t screw up, but that Russia did. For all countries except Russia, all data for that country comes out together. For Russia it comes out in regions – well it is a big place! Trying to prove this would need some Russian help – Pasha Groisman? – but there isn’t much point. The fact that all the affected data were from one Russian region suggests to me it was that region.
    Probably not of much use to an FAQ!
    Cheers
    Phil

    And some chunks of countries could get screwed up too, but hey, it’s not like you can find out if it was screwed up or when or by whom or whatever “there isn’t much point”… so why bother. It’s only data…

    There are then three paragraphs of ‘sellers puff’ quoted from the web site that looks oddly familiar, vapid, and empty.

    Then the Gavin statement to which the reply was sent, and the original complaint from Jones that caused Gavin’s reply. I’ll just include Gavin’s bit:

    At 12:56 17/11/2008, you wrote:

    thanks.
    Actually, I don’t think that many people have any idea how the NWS’s
    send out data, what data they send out, what they don’t and how these
    things are collated. Perhaps you’d like to send me some notes on this
    that I could write up as a FAQ? Won’t change anything much, but it
    would be a handy reference….
    gavin

    Where we find out that not “many people have any idea” how it all works and they didn’t even have a FAQ about it but could use one as ‘a handy reference’.

    Well, with those kinds of “standards” and “procedures” and “documentation” I find I must now recant my statement that I didn’t see much reason to “keep my powder dry”.

    It would seem that there is no way to ever know what stations are in, what stations are out, WHEN they are in, and WHEN they are out, or even if what is IN is really what is supposed to be in. As long as it’s self consistent between the dailies and monthlies, it can be in, unless it’s a very bad “outlier” (but even that might be an error if we are going to be having “extreme weather events” as they ought to be, by definition, outliers…)

    At any rate, given their “process”, it looks like we can have some confidence that the glue-on QA will be done eventually and most of the time a lot of the data will be available by the middle of the following month, except when it’s the end of the month, or maybe the next month… or whatever.

    Just unbelievable…

  150. E.M.Smith says:

    Ron Broberg (22:32:06) : Maybe you can post your station counting code …

    grep ………..2010 v2.mean | cut -b 4-8 | sort -n -u | wc

    Not that much different from yours. I’m prone to parameters in scripts, though, so I have work directories and years as passed parameters with defaults. That way the ‘process’ is consistent and I don’t have as much to worry about in the typo / qa process. Since the v2.mean file is already sorted by stationID, I don’t have a sort step:

    WRK=${2-./data}

    grep …………${1-2010} $WRK/v2.mean | cut -b 1-11 | uniq > $WRK/$1_uniq_station_list

    and then I do things like “wc -l” on the ouput list or diff or…

    I suspect that the difference in results is due to provenance of the data. It does have a ‘today’ update date on the ftp server and my copy is from 4 days ago.

    And, given their rather ersatz ‘process’ as described by the folks who ought to know this the most (the leader of the unit in the UK) it looks like updates can happen at any time…

  151. EW says:

    Oh yes – all Czech thermometers were scraped. Why couldn’t they leave at least Milesovka – it’s nice professional station at the mountain top with a record from the end of 20’s.

    http://travel.webshots.com/photo/1038138769028455201hytgZu

  152. carrot eater says:

    Wait a month or two, then get back to us.

  153. vjones says:

    I make that – Stations Dropped:

    Africa – 118
    Asia – 137
    S America – 62
    N&C America – 59
    Pacific Reg – 39
    Europe – 70
    Antarctica – 1

    For Europe – using database-derived trends for GISS adjusted data:
    65 of the 70 dropped stations were in the database – the 5 missing ones may be below the QC threshold

    Average trend for ALL European Stations: 0.56 Deg C/Century warming
    Average trend for Dropped European Stations: 1.23 Deg C/Century warming
    Average trend for Remaining European Stations: 0.5 Deg C/Century warming

    So for Europe at least, dropping these thermometers MAY not have a warming effect. However, these trends are for all years – it depends on the forward trends at the remaining stations.

  154. Erik The Viking says:

    @mILLrAT (13:04:40) :

    “I have converted the station list into a google earth kml file that is available for download at
    http://cid-78d84fe53bde5e59.skydrive.live.com/self.aspx/Public/RIPtempStations.kml

    Thank you Sir!
    (the file ext. is found as *.xml – just rename to *.kml)

    …3 stations left in Denmark, 1 in Copenhagen (lagest city), 1 in Aalborg (4 th. lagest city), and the last (Roenne, Bornholm) on a smal iland in the middle of a parking lot

    ..an on a funny note: they left out the one in Tarm, a Danish syn. for “anus”…

    again, your work is highly appreciated!

  155. DirkH says:

    “E.M.Smith (19:29:57) :

    Andrew30 (16:57:24) : Is this a temperature scale conversion, F to C, if so should the 50.0 not actually be 5.0?

    The data are in 1/10 C increments, so 102 is 10.2 F.”

    You’ve got a typo, you mean “102 is 10.2 C”. Andrew, sometimes programmers multiply a float with a value that is usually a power of 10 or power of 2 like 256 and store the product in an integer. From there they use fixed-point arithmetic. Often done in economy where you want exact dollar/cents (you multiply by 100 in this case) and avoid the inability of floating point numbers to exactly represent certain fractions – sometimes you get a pocket calculator throwing something like 0.999999999 at you when you know it should say 1 – this is such an artefact.

    This is legit, only the multiplication/division they did was buggy.

  156. kadaka says:

    Willis Eschenbach (15:33:44) :

    A bit of analysis of the deleted stations

    Urban 214
    Suburban 105
    Rural 166

    Median elevation 160 metres

    Airport 196
    Non Airport 289

    Offhand it looks like I surmised correctly. The goal is to establish there is a long-term strong warming trend. They have adjusted individual station data downward in the far past but upward in the near past. However this was done clumsily and has been detected at Darwin etc. Their ignoring of UHI and the proper adjustments has also been noted.

    Realizing they will be facing increased scrutiny, they are placing their bets on stations that will show an increased warming trend. The high altitude stations will not show that, so they are eliminated. Urban thermometers that have bottomed-out on UHI and will now report anomalies more consistent with real global temperatures, were removed. For example, note the dropping of the Dallas-Fort Worth airport. It seems unlikely there will be a large increase in air travel anytime soon, and between current economic conditions and theoretical upcoming “green taxes” air travel may likely decrease, thus it will not show a great warming trend, thus it is dropped.

    What is left appear to be stations where nearby development with UHI will bias the readings to a warm trend in the coming years. They went light on the suburban deletions, as increased populations and future economic development will make suburbs warmer. There are rural stations that are unlikely to see nearby development anytime soon, may even be showing cooling, thus they are good candidates for deletion. But rural stations that will see development and thus increases would be kept, including those that now or in the future should not be labeled “rural.”

    Of course far more research needs to be done as to the specific deletions and retentions to positively conclude the result will likely yield an increased artificial warming trend, in line with AGW predictions. But offhand, to me, I can believe that until shown otherwise.

  157. Ron Broberg says:

    … it looks like updates can happen at any time…

    Which is unsurprising to me when dealing with international data sets

    I also occasionally take a look at the EIA world oil production numbers. While interim updates are published more-or-less on a monthly schedule, the data effected can go back two years. Estimates are used for oil production in countries that don’t report in a timely fashion, and those estimates are updated whenever a particular country gets around to providing more accurate figures.

    Those estimates can affect things like total world oil production. Early figures in 2009, for instance, suggested that annual oil production reached a maximum in 2008. Current figures suggest 2005. (See 4.1d). (And the ‘max’ statement is a statement of past history, not a future prediction)

    http://www.eia.doe.gov/ipm/supply.html

    So I’m used to seeing international data sets – even on “very important topics” – shift a bit. Not everyone reports in a timely fashion. The major difference here seems to be that NOAA has enough thermometers in play that it does not need to make estimates for stations that haven’t reported yet.

    My prediction: more stations in the data set for 2010 next time it is updated.

  158. DirkH says:

    “DirkH (08:35:35) :
    [...]
    This is legit, only the multiplication/division they did was buggy.”

    Oh well, and they should have thought of the fact that nint truncates, not rounds, so they should have added 0.5 before the truncation so as to not introduce bias.

  159. DirkH says:

    “DirkH (08:43:01) :
    [..]
    Oh well, and they should have thought of the fact that nint truncates, not rounds, so they should have added 0.5 before the truncation so as to not introduce bias.”

    Dang! correcting myself. It rounds. Sorry not being a Fortran specialist and talking out of my ass again. So that’s a wrong assertion of mine, thank you, google.

  160. Roger Knights says:

    hswiseman (20:46:48) :

    A discharged material can be pollutant even if it is naturally occurring and not otherwise harmful. We regularly emit C0 in small, non-lethal amounts into the atmosphere. Like C02, in high concentrations C0 will kill you. Not too many people would argue that tailpipe C0 is not a pollutant. The world at large has made a cost-benefit determination that creating additional C0 provides so many benefits to man-kind that it tolerates and adapts to the small burdens of potential C0 poisoning in closed /spaces.

    Co isn’t analogous to CO2, because CO2 is much more inert. CO is taken up by red blood corpuscles in a way that is effectively poisonous. It’s qualitatively bad, unlike CO2.

  161. DirkH says:

    E.M., i just saw on your blog that the people who try to rewrite GISTEMP in Python continue the tradition of omitting brackets where they can. That means they will get different undefined artefacts than the Fortran original but undefined artefacts it will be. Good luck Python guys. Go to E.M.’s blog to the thread he linked to to see my comment.

  162. E.M.Smith says:

    Well, I’ve just completed a new download of the v2.mean.Z file followed by unpacking and a “diff” with the 8 Feb 2010 version.

    The most interesting difference is this one:

    < 4347898800032009 264 260 261 274 278 281-9999 289 295 290 288-9999
    > 4347898800032009 264 260 261 274 278 281-9999 289 295 290 288 278
    > 4347898800032010 274-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

    Notice that, in addition to putting IN the 2010 record for this station, the 2009 record has had a December value added.

    So between 8 Feb 2010 and now, Dec 2009 was “discovered” somewhere and put in. So even a month and a half after the fact, lost thermometers can be found.

    There were 67 other “adds” to the file

    > 1476190100022010 205-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 2174445400022010 127-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3128122500022010 253-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3138633000022010 250-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3138635000002010 244-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3138643000002010 250-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3138644000022010 237-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3138646000032010 246-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3138656000022010 257-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3138656500042010 246-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3138658000032010 236-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148040300032010 276-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148041000032010 244-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148041300022010 258-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148041500032010 261-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148043500042010 279-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148043800032010 197-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148044400032010 276-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148044700042010 278-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148045000032010 280-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148045300042010 260-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148045700032010 287-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 3148046200032010 225-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147622000002010 49-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147622500052010 87-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147624300002010 113-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147631100002010 185-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147634200012010 124-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147637300012010 108-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147638200032010 141-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147639000032010 84-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147639300052010 138-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147640500042010 197-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147645800032010 219-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147652500042010 82-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147655600052010 180-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147657700052010 130-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147658100002010 137-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147663200002010 111-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147664000002010 171-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147664400052010 220-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147665400032010 254-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147666200012010 150-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147666500012010 139-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147668000042010 137-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147668300002010 117-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147668700002010 136-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147669200022010 199-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147674100032010 206-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147675000022010 231-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147680500032010 268-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147684500002010 123-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4147690300032010 279-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4310422000032010 -66-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4310425000032010 -35-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4310431200002010 -287-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4310432000032010 -230-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4310436000032010 -20-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4310439000032010 3-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 4347898800032009 264 260 261 274 278 281-9999 289 295 290 288 278
    > 4347898800032010 274-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 5039779600002010 275-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 6120603000012010 -37-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 6120618600012010 -24-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 6120619000012010 -21-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 6244025000012010 120-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 6244031000012010 120-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    > 6520601100032010 37-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999
    
    

    So at this point, it looks like when NOAA make a file available, it’s only a suggestion not an endorsement, and it will be constantly changed, day by day, for months. Given that each month there will be a new month of data: any single day the image of the file may well be different from any other day but with a ’spike’ of newness near the 2nd week of the month.

    How does one assure any consistency of processing or comparison of results between researchers when the base data is constantly mutated? How does one “measure” the process or even standardize the error detection? Unless you know the DAY that GISS chooses to do their copy of the GHCN input, you can not duplicate their results (though you could come “close” most of the time.) But given the tendency to ’spread’ a value 1200 km in GIStemp, that single December added value could influence a box of space 2400 km x 2400 km and that could also shift hemispheric values, etc.

    Just amazing.

  163. Anthony Watts says:

    Here’s the monthly history for DFW AP

    JAN 2010
    http://www.wunderground.com/history/airport/KDFW/2010/1/13/MonthlyHistory.html
    DEC 2009
    http://www.wunderground.com/history/airport/KDFW/2009/12/31/MonthlyHistory.html

    JAN mean is 45F
    DEC mean is 43F

    Here’s Chiefio’s GHCN station reference: (*) Dallas-Fort W 32.9 N 97.0 W 425722590000 4,037,000 1947 – 2009

    Checking NCDC’s MMS database it shows it to be current, but curiously, shows a start date of 1974 NOT 1947 so I wonder if the start date is a typo? According to AP history records, DFW Airport opened for commercial service on January 13, 1974. Sure looks like a typo in the GHCN database to me.

    I thought maybe the issue has to do with METAR reporting, as I have
    previously pointed out to have data transmission errors. So I went through every hourly METAR for Dec09

    Starting here and working backwards through Dec09 and forwards through Jan10:

    http://www.wunderground.com/history/airport/KDFW/2009/12/31/DailyHistory.html
    (be sure to click “show full METARS”)

    Every METAR report for every hour of the month of December checks out. As does every one on January 2010

    So if they are using DFW AP data, and the Lat/Lon given ( 32.9 N 97.0 W) places it there, see…

    http://maps.google.com/maps?q=32.9+N+97.0+W&oe=utf-8&client=firefox-a&ie=UTF8&hq=&hnear=%2B32%C2%B0+54%27+0.00%22,+-97%C2%B0+0%27+0.00%22&gl=us&ei=6dJ2S_v5BIPMsgPH073LCA&ved=0CAgQ8gEwAA&ll=32.9,-97&spn=0.119052,0.128574&z=13

    …then there’s nothing wrong with the raw data that I can find.

    So it puts the onus on NCDC’s algorithms.

  164. carrot eater says:

    I don’t see why this is so surprising to you. The most recent month(s) continue getting updated over time, as the data come in. I find it strange that your first thought was that it’s all due to a “hatred” of thermometers.

    You must have other, older versions of v2.mean lying around. Just do the same comparisons on them.

    “you can not duplicate their results (though you could come “close” most of the time”

    Has GISS even updated their webpage for Jan 2010 results yet? Not that I can see. I do think they wait until they think enough stations are in. But yes, the numbers change do change a hair as more updates come in. You can see this for yourself, and I think it’s been noted on this website.

    “It also shows that the excuse of things being dropped for not electronically reporting is pretty much a lie, too. I note that Dallas Fort Worth Airport..”

    Care to re-word that?

  165. Ron Broberg says:

    @ E.M. Smith

    It also shows that the excuse of things being dropped for not electronically reporting is pretty much a lie, too. I note that Dallas Fort Worth Airport is on this list and I’m pretty sure they have electronic reporting…

    For the 2010 deletions, “That dog won’t hunt”… since BY DEFINITION those stations were reporting up until now.

    I notice your “update” statement that DFW is indeed a failed QC station.
    Are you going to retract your “pretty much a lie” statement?

    Ok, enough of my complaint …

    Nah. Say it aint so … ;-)

    Or maybe it isn’t sarcastic… maybe in our Orwellian world… No, no, must not think that way. Where did I leave my soma?…

    So, no, I don’t see much reason to ‘keep power dry’. I could easily see a single digit number of thermometers change from “QA failed” being itself QA’d but if they start putting 5% or more of the records ‘back in’ I think they are violating their published methodology… (though I’d welcome it).

    Really? You would “welcome it?”

    So at this point, it looks like when NOAA make a file available, it’s only a suggestion not an endorsement, and it will be constantly changed, day by day, for months. …

    … Just amazing.

    I’ll assume your ‘amazement’ comes with an undertone of ‘welcoming’ ;-)

    —————

    kadaka wrote:The goal is to establish there is a long-term strong warming trend.

    See vjones’ work below.

    vjones wrote:Average trend for ALL European Stations: 0.56 Deg C/Century warming
    Average trend for Dropped European Stations: 1.23 Deg C/Century warming
    Average trend for Remaining European Stations: 0.5 Deg C/Century warming

    Thanks for that update. I appreciate it.

    A small shadow cast on the thesis that: NOAA is selectively removing cooling stations.
    In this case, the stations dropped for Europe were warming stations.

  166. carrot eater says:

    “…then there’s nothing wrong with the raw data that I can find.

    So it puts the onus on NCDC’s algorithms.”

    Or, somebody/something messed up when coding in the CLIMAT report. You can look it up, and it shows the weird numbers that caused it to fail QC.

  167. Richard M says:

    What I find amazing is that any scientist would not be horrified by this process. All data should be held out and made official on a specific date. With billions going for climate research, a new website to promote AGW and a new computer in WY, you’d think someone might consider updating the methodology to at least the 16th century.

    I guess not.

  168. geo says:

    Some airports going away, eh? Isn’t it pretty much SOP for this bunch when criticised with logic and facts, to 1). Tell the public it is unfair criticism by a bunch of skunks-at-the-garden-party that obviously has no substantive impact. . . and then 2). Run around behind the scenes going “Hmm, that does have some merit after all” and address it without comment or admission of why?

  169. kadaka says:

    Ron Broberg (11:44:27) :
    (…)
    A small shadow cast on the thesis that:
    NOAA is selectively removing cooling stations.
    In this case, the stations dropped for Europe were warming stations.

    And amazingly enough, that wasn’t even my thesis. Or my hypothesis.

    As you presented, the European stations that have shown great warming have been dropped. As I surmised. They have already been milked for the greatest positive change in rate of warming. UHI and other factors have taken place, they will now show a rate consistent with the rate for global average temperatures. The ones retained, I surmised, have the greatest potential to show the greatest positive change in rate of warming in the future.

    The first set has been milked dry, now they are moving on to the next set. By creative selection of stations, they will have their great rate of warming without resorting to messy and detectable mis-adjustments of the raw data.

    Try again.

  170. E.M.Smith says:

    @Anthony: The GISS web site shows a “GHCN + USHCN” record that starts in 1947:

    http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425722590000&data_set=0&num_neighbors=1

    A look in the v2.mean file shows it is in GHCN:

    4257225900001947-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999   88
    4257225900001948   35   80  116  217  229  282  294  299  254  193  131  108
    4257225900001949   39   93  140  176  243  279  300  284  242  186  149   99
    

    Though that 1947 record is just a December value.

    So I’m left to assume there was something recording there long ago. Perhaps some older rural airport prior to DFW being created? If so, that begs the question of how one mod flag spans so much time and change. If not, that begs the question of where the data came from. In either case, it’s an interesting question of station and data quality and provenance.

    If there are any Dallas locals who know the history, perhaps they could speak up.

    Oh, and in 2009 it had a couple of data dropouts in Sept and Oct as well.

    4257225900002009   90  132  149  182  229  288  303  296-9999-9999  153   59
    

    Or has them now? Or might have them in the future, but maybe not?

    In the mean time, having established that GHCN v2.mean is a perpetual “work in progress”: that complicates picking a new ‘poster child’ for electronic reporting and “dropped”. It will be easy to identify an electronic reporting and “missing in action”, but then there is that problem of prodigal thermometers returning months later.

    The other interesting path this leads to is the question of just how much GIStemp products have “stability”. If the data can be MIA for a few months, then show up, how much “faith” can be put in any claims of “Warmest” or “Most Extreme” anything? WHEN does a GIStemp product become ‘stable’?

    Best I can think of is to get a copy of GHCN at the moment any GISS claim is made and do a “missing data” analysis on it. What fraction of stations in the current month have “missing data” compared to ??? ( 6 months before? A year before? ) but even that becomes a bit of a random act. If the claim is made the 22 of the month, was the data downloaded by GISS on the 20th? The 15th? Did NOAA provide updates on the 21st?

    For example:

    http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=434789880003&data_set=0&num_neighbors=1

    that station with the December Return. It has a 2009 “average” on the GISS graph. It does not include December. In a few (days? weeks?) it will. How would you know that the value in a few weeks is based on different data from the value now without a detailed data mining that just about nobody on the planet will do? Does it matter?

    How often do the GISS published charts change? And by how much? Is it significant?

    I don’t see an easy way to answer those QA questions. ( And “trust me” or “guess it’s OK” are not valid QA processes.)

    (In accounting, there are similar issues. So you have a moment in time where you issue your annual report. You may ‘restate your numbers’ after an audit in some future year or in next year’s annual report, but that becomes part of the record. it stands out if every year you have “restated last years earnings lower 10%” for a few years. Here in GHCN / GISS that ‘restatement’ is an ongoing and un-audited process in perpetuity… )

    But at least now we start to have a handle on why so many people have saved images of various GISS graphs or done data plots only to find they don’t quite match later. It’s all about the stability and provenance of the base data, and those are ill defined.

    Then blend that with the GIStemp homogenizing that re-imagines the past based on changes in the present and you have a foundation of the past made of sand.

  171. Nick Stokes says:

    Really, what’s the points of these posts on mass dying of thermometers, when all “being eaten” means is that the stations weren’t among the first batch to report for the month? So folks speculate on why Greenland is being dropped, on the basis that there weren’t many stations in the list reporting Jan results by 8 Feb. Then we look on 13 Feb and amazing, there are six more! Good, so we can have the same discussion next month.

  172. Ron Broberg says:

    what’s the points of these posts on mass dying of thermometers

    I’m assuming this is rhetorical, but just in case anyone needs it spelled out …

    The point is to create an atmosphere of doubt and uncertainty. Two prongs of the stratagem known as: FUD

    Fear is fed by the stories of ‘one world government carbon taxing us into the stone age.’

    One response to FUD is to examine the details. In this case, DFW was raised as a rhetorical point by EM Smith to cast doubt on NOAA’s claims that stations are dropped if they aren’t reporting electronically.

    I subsequently ‘found’ DFW in the v2.mean.failed.qc file. All I had to do was look. It was never lost. It was processed by NOAA and found wanting. E.M Smith was apparently too unfamiliar with the NOAA GHCN data set to be aware of the file.

    Why NOAA v2.mean had a processed monthly mean of -6C is an interesting question. It is unlikely to be resolved in this thread. But NOAA GHCN was correct to reject their monthly mean for DFW (which was -6C). As I noted and Anthony followed on, METAR data is available for DFW in Jan 2010. It provides a monthly mean of +6C. I don’t think that tells us much about why NOAA did not receive their CLIMAT data updates or were not able to process them. DFW has values for onlu the first 10 days of January in the GHCN daily file for that station.

    Of course, DFW was just an exemplar. Long lists of other stations were held out as more evidence of deliberate manipulation by an agency which was accused of ‘pretty much lying.’ E.M. Smith suggested he would ‘welcome’ seeing that list made shorter, missing stations added. When it became obvious that is in fact what happened in the 4-5 days between Feb 8 and Feb 12, he responded with pointed questions which suggest his ‘welcome’ was less than warm and friendly. From here, it appears that it is more important for him to hold on to the “theme” – to bolster the stratagem – to spread as much ‘uncertainty and doubt’ as possible – then to welcome the additional data sets. Hopefully, E.M. Smith’s welcome will be warmer as the additional stations accumulate next month and throughout the rest of the year.

    As I said, all this seems fairly obvious. I’m just spelling it out on the off-chance the question was not rhetorical.

  173. carrot eater says:

    E.M.Smith (14:44:19) :

    “If the data can be MIA for a few months, then show up, how much “faith” can be put in any claims of “Warmest” or “Most Extreme” anything?”

    I’ve seen GISS monthly global anomalies get revised by upto +/- 0.02 C. You should realise there are uncertainty bounds on these numbers. If 2 years or months are within those bounds of each other, you can’t say one is warmer than the other. For example, a lot of breath is wasted here about whether 1934 or 1998 was warmer in the US, when they’re really just statistically tied. On the other hand, the difference between the 2000s, 1990s, 1980s and 1970s averages are significant.

    But again, you’ll notice that GISS hasn’t put out Jan 2010 numbers yet. But you’ve got the GISS code running. Why don’t you just run it for the two different v2.means, and see what happens? Maybe you’ll see the reason why GISS is holding back on Jan 2010; I don’t know.

    But in any case, I’m still wondering if you care to reword certain rash statements made above.

    Ron Broberg (17:07:40) :

    Thank you to you for actually looking into things. As for DFW, you can look up the CLIMAT report and see it for yourself. It has the numbers GHCN logged. I can’t speculate what went wrong where, but maybe we can watch it to see if it gets corrected.

  174. Bob Koss says:

    The Giss monthly update normally occurs the middle of each month.

  175. Ron Broberg says:

    Hey carrot eater, I’ll bite.

    How does one review the CLIMAT data?
    Or are you referring to the data that was in v2.mean.failed.qc?

  176. Juraj V. says:

    But this affect only the NOAA dataset, right? CRU/GISS still use stations whichever they want? I know that CRU dataset, besides its problems, uses much more stations that two latter datasets and is better (or less worse) than those.

  177. carrot eater says:

    By the way, EM Smith:

    This is what happens when you remove high latitude stations from the record:

    http://data.giss.nasa.gov/gistemp/graphs/IntegArea.pdf

    Removing the SH high latitude makes little difference.
    Removing the NH high latitude results in a slightly cooler record in the last few years, but that shouldn’t be a surprise, given how GISS features a warming Arctic over that time period.

    So yes, anomalies do work. Absolute temperatures don’t matter when you work with anomalies; it’s the changes in temperature that matter.

  178. carrot eater says:

    OK, I bet we can figure out what happened to DFW if we work together a bit.

    From the CLIMAT summary, I get:

    Monthly mean temp: -6.2 deg C with at standard deviation of 5.2 C deg
    Max temp: -2.2 deg C
    Min temp: -10.3 deg C

    And zero days of missing data for temperature.

    On 29 days, the min temp was below 0 C.
    On 21 days, the max temp was below 0 C.

    This latter bit looks totally wrong. Either something got messed up at DFW when coding this in, or another station filed their report using DFW’s station code by accident.

  179. carrot eater says:

    And some more info from the DFW CLIMAT report

    The day with the highest mean temperature: +2.5 C, on Jan 7 and also at least one other day

    The day with the lowest mean temperature: -16.1 C on Jan 1

    The maximum maximum temperature: + 4.4 C on Jan 7
    The minimum minimum temperature: -23.8 C on Jan 2

    This clearly isn’t right, so the NOAA QC was perfectly correct in tossing it out. Maybe somebody at DFW is very confused, or this is the report from some station in Siberia, with somehow the wrong station ID number.

    Bob Koss (20:42:22) :

    Well, there you go. This is perhaps why they usually wait a couple weeks – they need to wait for enough stations to report.

    Juraj V. (01:02:55) :

    GISS draws its land stations from GHCN and USHCN. Only ocean and some Antarctica come from elsewhere.

    But again, note that GISS didn’t put out Jan numbers yet..

  180. DirkH says:

    “carrot eater (03:47:45) :
    [...]
    Removing the SH high latitude makes little difference.
    Removing the NH high latitude results in a slightly cooler record in the last few years, but that shouldn’t be a surprise, given how GISS features a warming Arctic over that time period.”

    The problem is that they change the basket of thermometers over time, yet make comparisons between their final average that they get with the basket-du-jour with one they got with a very different basket of thermometers in say 1979. This opens up the room for arbitrary manipulations. If you call that scientific, that’s entirely your problem, work on your understanding of what sound statistics would require.

    And if Ron Broberg calls this concern spreading of FUD, that doesn’t make it true, it’s a simple deconstruction of a botched way of working.

  181. carrot eater says:

    DirkH (06:40:44) :

    Your complaint is vague to the point it lacks meaning. Point out exactly what you’re afraid of, and how it would impact the numbers. Then suggest a mathematical test to see if it occurs.

    But I think the plot speaks for itself. Dropping the high latitudes from the analysis causes a slight cooling, if anything, because the NH high latitudes contribute some recent warming to GISS. Those studying the difference between GISS and CRU will be well aware of that.

  182. vjones says:

    Ron Broberg (11:44:27) :

    Your glee at my Europe data was obvious but premature. Now that I have had time to look at N&C America, and S. America, you’ll be surprised to hear that the trends there for removed stations are primarily negative.
    Average trend for Dropped N&C American Stations: -0.732 Deg C/Century
    Average trend for Dropped S American Stations: -1.28 Deg C/Century

    carrot eater (03:47:45) :

    That graph you link to that removes high latitude stations….

    I took a look at the stations in the GISS record that are removed by that analysis and at the temperature trends for each station.

    It only removes 9 stations for >70S but 37 from >70N.

    Those 37 have an unweighted average trend of 4.396 deg C/Century warming trend for the UNADJUSTED data set, but a 7.039

    deg C/Century warming trend in the ADJUSTED data set. Given that the GISS adjustments are intended to correct for UHI, I

    would say that is a very large average adjustment for a part of the world with few urban areas. I’m not surprised leaving the

    >70N data out of an analysis cools the data.

  183. Phil says:

    @ Mr. Stokes, Mr. Eater and Mr. Broberg

    Can you all think of an explanation of what happened to all 484 stations that are not included? As for DFW, given the extreme accuracy and precision claimed for the land record, it is troubling that such a prominent station could produce data that is apparently so wrong. It would seem that more digging would be necessary to find out how DFW got its figures so wrong. That might shed some light on things that might perturb the land temperature records.

    P.S. The issues I am raising are meant to be sincere and not rhetorical. It is not my intent to foment a FUD fight. Please forgive the (intentional) pun.

  184. Ron Broberg says:

    Phil (11:13:18) : Can you all think of an explanation of what happened to all 484 stations that are not included?

    I accept the possibility that those station have simply not reported January numbers to NOAA in a timely manner and/or those station have not yet been processed at NOAA.

    vjones (10:48:26) : Your glee at my Europe data was obvious but premature. Now that I have had time to look at N&C America, and S. America, you’ll be surprised to hear that the trends there for removed stations are primarily negative.

    Glee?

    Nor am I surprised. I don’t believe that NOAA is selectively removing stations. That some regions could show ‘warming stations’ being dropped and other regions are showing ‘cooling stations’ being dropped seems to fall into line as evidence that no intentional selection is being applied. Randomness has that characteristic: some high, some low.

    But more importantly, I am glad that someone is taking the first steps towards analyzing the effects – rather than just pointing to an event and making wild-eyed assertions.

    Of course, the analysis isn’t over until someone checks how ‘dropped’ stations affect the final products: gridded anomalies and global averages.

  185. I’ve followed EMS for a while now and add my vote of admiration to those above.

    As an accounting type, I’m simply flabbergasted that, in an age where terabytes of SQL data can be interrogated with return times of milliseconmds, these guys are still sticking with text files and multiple passes, not to say, munges.

    For my tax dollar I’d want to see a proper accounting-style transactional record for each and every read of each and every station. And I’d want to see every single adjustment, up, down or sideways, to each and every such record, type-grouped and categorised up the wazoo.

    Hell, if us accounting types neglect to journal things properly, and don’t leave a proper transactional audit trail, the IRS won’t accept “well, we just filled ‘er in from nearby invoices”.

    Neither, as taxpayers, should we demand anything less from our climate accountants.

    A made-up sample of the sort of thing I’m urging:

    Type Station Date ToD (Zulu) Tempx10 Process Who
    RAW 4500234765 20100113 0850Zulu 120 AutoRead Therm
    RAW 4500234765 20100113 1204Zulu 150 AutoRead Therm
    UHI 4500234765 20100131 2359Zulu -15 UHI adj V2.1 EOM UHI
    MIN 4500234765 20100113 0400Zulu -35 AutoRead Therm
    MAX 4500234765 20100113 1320Zulu 157 Autoread Therm
    CAL 4500234765 20100113 2359Zulu -5 Calibrate QA Crew

  186. carrot eater says:

    vjones (10:48:26) : You might not be surprised that leaving out high latitude NH cools the data (and really, nobody should be), but yet EM Smith thinks that leaving them out would warm the data. So far as I can tell, the reason for his thinking is that he’s using absolute temperatures in place of anomalies.

    Phil (11:13:18) : Patience. Wait for GISS to put out its Jan 2010 numbers, then see what’s in v2.mean. Then check again a month later. Not every station sends out its CLIMAT report on the first day of the next month.

    As for DFW, well, this is why GHCN has a quality control step. My uneducated guess is still that it’s a mislabeled station number, but that’s just an idle guess.

  187. Nick Stokes says:

    Re: Phil (Feb 14 11:13),
    Mr. Stokes, Mr. Eater and Mr. Broberg
    Can you all think of an explanation of what happened to all 484 stations that are not included?

    Easy. Their reports of Jan 2001 figs didn’t get into the online file by 8 Feb, when EMS did his count. But by 12th Feb, the number was down to 415. I’m sure there’ll be another “resurrection” in a few days. The number reporting will probably be up to about 1500 by end Feb. Then I guess someone can look at the Feb figures on 8 March, and we can have another mass dying article.

  188. kadaka says:

    @Ron Broberg (14:40:19) :

    Do you take the time to realize what you are saying before you post it?

    (…)
    Nor am I surprised. I don’t believe that NOAA is selectively removing stations. That some regions could show ‘warming stations’ being dropped and other regions are showing ‘cooling stations’ being dropped seems to fall into line as evidence that no intentional selection is being applied. Randomness has that characteristic: some high, some low.
    (…)

    Do you think someone is hitting a “Purely Random Delete” button somewhere? Whereby both “good” and “bad” stations all have an equal chance of deletion therefore it’s a completely neutral unbiased process? Otherwise there is some form of selection criteria involved, therefore NOAA is selectively removing stations. From there one has to figure out what the selection criteria are, whether purely “quality control” or if something else is involved and to what degree.

    I accept the possibility that those station have simply not reported January numbers to NOAA in a timely manner and/or those station have not yet been processed at NOAA.
    (…)

    Aren’t there stations with automatic electronic reporting that are missing? Why would it take longer to process them than the ones without such?

  189. Nick Stokes says:

    For those who are interested, I’ve posted on my blog a list of the latest v2.mean stations, in order of most recent reporting month (as at 12 Feb 2010), and with the countries marked (in groupings).

  190. carrot eater says:

    kadaka (00:20:42) :

    “Aren’t there stations with automatic electronic reporting that are missing? Why would it take longer to process them than the ones without such?”

    There’s an incorrect assumption embedded in your statement. Just because they’re sent electronically doesn’t mean they’re generated automatically. There will be some human interaction there (keying in data, checking for errors like the DFW weirdness above)

    If you dig around, I think WMO guidelines ask for the CLIMAT reports to be sent in by the 5th or 8th of the following month.

  191. kadaka says:

    carrot eater (05:01:21) :

    There’s an incorrect assumption embedded in your statement. Just because they’re sent electronically doesn’t mean they’re generated automatically. (…)

    There’s an incorrect assumption embedded in your reply. I know dang well what I meant by “automatic electronic reporting.” The weather station info gets sent into a computer where it is logged. Then compiling it into a report and sending it in becomes a normal scheduled job for the computer. No human action to initiate anything should be needed, making it fully automated and done automatically. Don’t they have stations set up to do that?

  192. carrot eater says:

    kadaka (07:01:26) :

    You made the exact same error again. It’s electronically transmitted, yes. But who told you it was always automatically generated and sent?

    Read about CLIMAT reports on the WMO website sometime; you’ll find a lot of material there. They’ve made available software that generates the CLIMAT report in the required format, but somebody still has to sit there and manually key in the data, in order to use that software. You can see screenshots of the software. Maybe in some countries, the databases are fancy enough that the CLIMATs can be written with no human interaction, but this is not the rule. Judging from the illustrations of common errors, some countries were probably writing the reports manually.

  193. kadaka says:

    Me:

    kadaka (07:01:26) :

    (…) No human action to initiate anything should be needed, making it fully automated and done automatically. Don’t they have stations set up to do that?

    Sure looks to me like simple enough wording, easy to understand. The question at the end, don’t they have stations set up for full automation?

    Which got me:

    carrot eater (07:43:23) :

    You made the exact same error again. It’s electronically transmitted, yes. But who told you it was always automatically generated and sent?

    Me: Don’t they have fully automatic stations?
    carrot eater: Who told you they were always automatic?

    Logic error detected. Aborting process. Error report: Systemic lack of comprehension with intended recipient.

  194. carrot eater says:

    kadaka (13:42:06) :

    Ok, I’m sorry, maybe you were asking a question instead of making an assumption.

    Either which way, the answer is no. It is not a fully automated process, at least in many countries. Some African countries are especially bad at reporting on a timely and regular basis, though some others do really well.

    All the active stations in any given country tend to get reported and added in chunks. EM Smith noticed Strasbourg hadn’t gotten in yet, but in fact none of France was in yet. One of these days you’ll look and probably see 17 French stations pop up for Jan 2010, all showing up at once.

  195. carrot eater says:

    Looking a bit more, using Nick Stokes’s breakdown:

    EM Smith’s missing ~400 stations includes a lot of countries that don’t report regularly at all. For example, it includes Libya, which has had better things to do than send numbers in, since Jan 2009. That’s 17 stations there. Given their unreliability, who knows when they’ll report again.

    A better predictor of what might show up in the next week is the difference between Dec 09 and Jan 10. This is 179, minus I think 11 in QC. So where are these missing ~168?

    North Korea, for 7.
    Mongolia, for 34.
    Paraguay, for 10.
    Peru, for 12.
    France, for 17.
    Greece, for 10.
    French Polynesia for 7
    Mozambique for 5
    That’s a sum of 102, and then little bits here and there. All of the above countries are entirely missing as of now.

    So maybe those will show up by month’s end, though there’s a chance some of these are also sporadic reporters, and just happened to file reports last month.

    So if I had to guess, maybe 80 more stations will show up soon, while the rest of the ~400 could take several months.

  196. Alexander says:

    Gavin Schmidt: “Considering the enormous success that we achieved with our global climate models, we decided to dropping the weather stations completely. From now on, all GHCN data will be interpolated from the output of our models, which provides a highly accurate representation of the Earth’s climate and what’s better, allows us to get surface temperatures from any point on the globe, instead of only those where weather stations are located.”

  197. Dillon Allen says:

    After spending a large part of last year in Central America (officially part of North America in this data set) and becoming familiar with all of the major airports in the region, I am very surprised to see deletions of:
    Belize City, BLZ
    Managua, NIC
    Tegucigalpa, San Pedro Sula, and La Ceiba, HND
    San Jose (Juan Santamaria), CRI

    I haven’t yet taken the time to see what is left in the data set from CENTAM, but there are only a couple of other airports in the entire region (San Salvador, SLV; Guatemala City, GTM; and Panama City, PAN). There may also be some rural sites throughout the region, but I can’t imagine there are many. Perhaps it has been decreed from on high that all CENTAM “climate” can be represented by the data provided by the USAF at Soto Cano Air Base, situated in a valley around 2500ft (~800m) right beside mile-long strips of concrete and asphalt – perfect representation of the tropics.

    Oh wait, Soto Cano doesn’t appear either, since there is only hourly data from there since the early 80s. Perhaps the CENTAM information is being interpolated from surrounding areas like Houston, New Orleans, Miami, and Antarctica.

  198. dp says:

    Is there an analysis that shows that the changing fortunes of weather stations over time has an impact on the measured temperature record?

  199. Ron Broberg says:

    Ron Broberg (08:40:38) : My prediction: more stations in the data set for 2010 next time it is updated.

    grep ^…………2010 v2.mean | cut -b 1-8 | sort -u -n | wc
    1348 1348 12132

    1348 stations for 2010 in the March 5 3:38AM v2.mean.Z

    EM Smith: Just shy of 1/3 of the stations, taken out back and shot this year.

    “I’m getting better!”

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