Watts Up With Nuuk?

NEW UPDATES POSTED BELOW

As regular readers know, I have more photographs and charts of weather stations on my computer than I have pictures of my family. A sad commentary to be sure, but necessary for what I do here.

Steve Goddard points out this NASA GISS graph of the Annual Mean Temperature data at Godthab Nuuk Lufthavn (Nuuk Airport) in Greenland. It has an odd discontinuity:

Source data is here

The interesting thing about that end discontinuity is that is is an artifact of incomplete data. In the link to source data above, GISS provides the   Annual Mean Temperature (metANN) in the data, before the year 2010 is even complete:

Yet, GISS plots it here and displays it to the public anyway. You can’t plot an annual value before the year is finished. This is flat wrong.

But even more interesting is what you get when you plot and compare the GISS “raw” and “homogenized” data sets for Nuuk, my plot is below:

Looking at the data from 1900 to 2008, where there are no missing years of data, we see no trend whatsoever. When we plot the homogenized data, we see a positive artificial trend of 0.74°C from 1900 to 2007, about 0.7°C per century.

When you look at the GISS plotted map of trends with 250KM smoothing, using that homogenized data and GISS standard 1951-1980 baseline, you can see Nuuk is assigned an orange block of 0.5 to 1C trend.

Source for map here

So, it seems clear, that at least for Nuuk, Greenland, their GISS assigned temperature trend is artificial in the scheme of things. Given that Nuuk is at an airport, and that it has gone through steady growth, the adjustment applied by GISS is in my opinion, inverted.

The Wikipedia entry for Nuuk states:

With 15,469 inhabitants as of 2010, Nuuk is the fastest-growing town in Greenland, with migrants from the smaller towns and settlements reinforcing the trend. Together with Tasiilaq, it is the only town in the Sermersooq municipality exhibiting stable growth patterns over the last two decades. The population increased by over a quarter relative to the 1990 levels, and by nearly 16 percent relative to the 2000 levels.

Nuuk population dynamics

Nuuk population growth dynamics in the last two decades. Source: Statistics Greenland

Instead of adjusting the past downwards, as we see GISS do with this station, the population increase would suggest that if adjustments must be applied, they logically should cool the present. After all, with the addition of modern aviation and additional population, the expenditure of energy in the region and the changing of natural surface cover increases.

The Nuuk airport is small, but modern, here’s a view on approach:

Nuuk Airport, Aug 11, 2007, photo by Lars Perkins via Picasa web album

Closer views reveal what very well could be the Stevenson Screen of the GHCN weather station:

Nuuk Airport looking Southwest Image: Panaramio via Google Earth

Here’s another view:

Nuuk Airport looking Northwest Image: Panaramio via Google Earth

The Stevenson Screen appears to be elevated so that it does not get covered with snow, which of course is a big problem in places like this. I’m hoping readers can help crowdsource additional photos and/or verification of the weather station placement.

[UPDATE: Crowdsourcing worked, the weather station placement is confirmed, this is clearly a Stevenson Screen in the photo below:

Nuuk Airport, Stevenson Screen. Image from Webshots - click to enlarge

Thanks to WUWT reader “DD More” for finding this photo that definitively places the weather station. ]

Back to the data. One of the curiosities I noted in the GISS record here, was that in recent times, there are a fair number of months of data missing.

I picked one month to look at, January 2008, at Weather Underground, to see if it had data. I was surprised to find just a short patch of data graphed around January 20th, 2008:

But even more surprising, was that when I looked at the data for that period, all the other data for the month, wind speed, wind direction, and barometric pressure, were intact and plotted for the entire month:

I checked the next missing month on WU, Feb 2008, and noticed a similar curiosity; a speck of temperature and dew point data for one day:

But like January 2008, the other values for other sensors were intact and plotted for the whole month:

This is odd, especially for an airport where aviation safety is of prime importance. I just couldn’t imagine they’d leave a faulty sensor in place for two months.

When I switched the Weather Underground page to display days, rather than the month summary, I was surprised to find that there was apparently no faulty temperature sensor at all, and that the temperature data and METAR reports were fully intact. Here’s January 2nd, 2008 from Weather Underground, which showed up as having missing temperature in the monthly WU report for January, but as you can see there’s daily data:

But like we saw on the monthly presentation, the temperature data was not plotted for that day, but the other sensors were:

I did spot checks of other dates in January and February of 2008, and found the same thing: the daily METAR reports were there, but the data was not plotted on graphs in Weather Underground.The Nuuk data and plots for the next few months at Weather Underground have similar problems, as you can see here:

But they gradually get better. Strange. It acts like a sensor malfunction, but the METAR data is there for those months and seems reasonably correct.

Since WU makes these web page reports “on the fly” from stored METAR reports in a database, to me this implies some sort of data formatting problem that prevents the graph from plotting it. It also prevents the WU monthly summary from displaying the correct monthly high, low, and average temperatures. Clearly what they have for January 2008 is wrong as I found many temperatures lower than the monthly minimum of 19 °F they report for January 2008, for example, 8°F on January 17th, 2008.

So what’s going on here?

  • There’s no sensor failure.
  • We have intact hourly METAR reports  (the World Meteorological Organization standard for reporting hourly weather data for January and February 2008.
  • We have erroneous/incomplete presentations of monthly data both on Weather Underground and NASA GISS for the two months of Jan Feb 2008 I examined.

What could be the cause?

WUWT readers may recall these stories where I example the impacts of failure of the METAR reporting system:

GISS & METAR – dial “M” for missing minus signs: it’s worse than we thought

Dial “M” for mangled – Wikipedia and Environment Canada caught with temperature data errors.

These had to do with missing “M’s” (for minus temperatures) in the coded reports, causing cold temperatures like -25°C becoming warm temperatures of +25°C, which can really screw up monthly average temperatures with one single bad report.

Following my hunch that I’m seeing another variation of the same METAR coding problem, I decided to have a look at that patch of graphed data that appeared on WU on January 19th-20th 2008 to see what was different about it compared to the rest of the month.

I looked at the METAR data for formatting issues, and ran samples of the data from the times it plotted correctly on WU graphs, versus the times it did not. I ran the METAR reports through two different online METAR decoders:

http://www.wx-now.com/Weather/MetarDecode.aspx

http://heras-gilsanz.com/manuel/METAR-Decoder.html

Nothing stood out from the tests with the decoder I did. The only thing that I can see is that some of the METAR reports seem to have extra characters, like /// or 9999, like these samples, the first one didn’t plot data on WU, but the second one did an hour later on January 19th:

METAR BGGH 191950Z VRB05G28KT 2000 -SN DRSN SCT014 BKN018 BKN024 M01/M04 Q0989

METAR BGGH 192050Z 10007KT 050V190 9999 SCT040 BKN053 BKN060 M00/M06 Q0988

I ran both of these (and many others from other days in Jan/Feb) through decoders, and they decoded correctly. However, that still leaves the question of why Weather Underground’s METAR decoder for graph plotting isn’t decoding them correctly for most of Jan/Feb 2008, and leaves the broader question of why GISS data is missing for these months too.

Now here’s the really interesting part.

We have missing data in Weather Underground and in GISS, for January and February of 2008, but in the case of GISS, they use the CLIMAT reports (yes, no ‘e”) to gather GHCN data for inclusion into GISS, and final collation into their data set for adjustment and public dissemination.

The last time I raised this issue with GISS I was told that the METAR reports didn’t effect GISS at all because they never got numerically connected to the CLIMAT reports. I beg to differ this time.

Here’s where we can look up the CLIMAT reports, at OGIMET:

http://www.ogimet.com/gclimat.phtml.en

Here’s what the one for January 2008 looks like:

Note the Nuuk airport is not listed in January 2008

Here’s the decoded report for the same month, also missing Nuuk airport:

Here’s February 2008, also missing Nuuk, but now with another airport added, Mittarfik:

And finally March 2008, where Nuuk appears, highlighted in yellow:

The -8.1°C value of the CLIMAT report agrees with the Weather Underground report, all the METAR reports are there for March, but the WU plotting program still can’t resolve the METAR report data except on certain days.

I can’t say for certain why two months of CLIMAT data is missing from OGIMET, why the same two months of data is missing from GISS, or why Weather Underground can only graph a few hours of data on each of those months, but I have a pretty good idea of what might be going on. I think the WMO created METAR reporting format itself is at fault.

What is METAR you ask? Well in my opinion, a government invented mess.

When I was a private pilot (which I had to give up due to worsening hearing loss – tower controllers talk like auctioneers on the radio and one day I got the active runway backwards and found myself head-on to traffic. I decided then I was a danger to myself and others.) I learned to read SA reports from airports all over the country. SA reports were manually coded teletype reports sent hourly worldwide so that pilots could know what the weather was in airport destinations. They were also used by the NWS to plot synoptic weather maps. Some readers may remember Alden Weatherfax maps hung up at FAA Flight service stations which were filled with hundreds of plotted airport station SA (surface aviation) reports.

The SA reports were easy to visually decode right off the teletype printout:

From page 115 of the book “Weather” By Paul E. Lehr, R. Will Burnett, Herbert S. Zim, Harry McNaught – click for source image

Note that in the example above, temperature and dewpoint are clearly delineated by slashes. Also, when a minus temperature occurs, such as -10 degrees Fahrenheit, it was reported as “-10″, not with an “M”.

The SA method originated with airmen and teletype machines in the 1920′s and lasted well into the 1990′s. But like anything these days, government stepped in and decided it could do it better. You can thank the United Nations, the French, and the World Meteorological Organization (WMO) for this one. SA reports were finally replaced by METAR in 1996, well into the computer age, even though it was designed in 1968.

From Wikipedia’s section on METAR

METAR reports typically come from airports or permanent weather observation stations. Reports are typically generated once an hour; if conditions change significantly, however, they can be updated in special reports called SPECIs. Some reports are encoded by automated airport weather stations located at airports, military bases, and other sites. Some locations still use augmented observations, which are recorded by digital sensors, encoded via software, and then reviewed by certified weather observers or forecasters prior to being transmitted. Observations may also be taken by trained observers or forecasters who manually observe and encode their observations prior to transmission.

History

The METAR format was introduced 1 January 1968 internationally and has been modified a number of times since. North American countries continued to use a Surface Aviation Observation (SAO) for current weather conditions until 1 June 1996, when this report was replaced with an approved variant of the METAR agreed upon in a 1989 Geneva agreement. The World Meteorological Organization‘s (WMO) publication No. 782 “Aerodrome Reports and Forecasts” contains the base METAR code as adopted by the WMO member countries.[1]

Naming

The name METAR is commonly believed to have its origins in the French phrase message d’observation météorologique pour l’aviation régulière (“Aviation routine weather observation message” or “report”) and would therefore be a contraction of MÉTéorologique Aviation Régulière. The United States Federal Aviation Administration (FAA) lays down the definition in its publication the Aeronautical Information Manual as aviation routine weather report[2] while the international authority for the code form, the WMO, holds the definition to be aerodrome routine meteorological report. The National Oceanic and Atmospheric Administration (part of the United States Department of Commerce) and the United Kingdom‘s Met Office both employ the definition used by the FAA. METAR is also known as Meteorological Terminal Aviation Routine Weather Report or Meteorological Aviation Report.

I’ve always thought METAR coding was a step backwards.

Here is what I think is happening

METAR was designed at a time when teletype machines ran newsrooms and airport control towers worldwide. At that time, the NOAA weatherwire used 5 bit BAUDOT code (rather than 8 bit ASCII) transmitting at 56.8 bits per second on a current loop circuit. METAR was designed with one thing in mind: economy of data transmission.

The variable format created worked great at reducing the number of characters sent over a teletype, but that strength for that technology is now a weakness for today’s technology.

The major weakness with METAR these days is the variable length and variable positioning of the format. If data is missing, the sending operator can just leave out the data field altogether. Humans trained in METAR decoding can interpret this, computer however are only as good as the programming they are endowed with.

Characters that change position or type, fields that shift or that may be there one transmission and not the next, combined with human errors in coding can make for a pretty complex decoding problem. The problem can be so complex, based on permutations of the coding, that it takes some pretty intensive coding to handle all the possibilities.

Of course in 1968, missed characters or fields on a teletype paper report did little more than aggravate somebody trying to read it. In today’s technological world, METAR reports never make it to paper, they get transmitted from computer to computer. Coding on one computer system can easily differ from another, mainly due to things like METAR decoding being a task for an individual programmer, rather than a well tested off the shelf universally accepted format and software package. I’ve seen all sorts of METAR decoding programs, from ancient COBOL, to FORTRAN, LISP, BASIC, PASCAL, and C. Each program was done differently, each was done to a spec written in 1968 for teletype transmission, and each computer may run a different OS, have a program written in a different language, and different programmer. Making all that work to handle the nuances of human coded reports that may contain all sorts of variances and errors, with shifting fields presence and placement, is a tall order.

That being said, NOAA made a free METAR decoder package available many years ago at this URL:

http://www.nws.noaa.gov/oso/metardcd.shtml

That has disappeared now, but a private company is distributing the same package here:

http://limulus.net/mdsplib/

This caveat on the limulus web page should be a red flag to any programmer:

Known Issues

  • Horrible function naming.
  • Will probably break your program.
  • Horrible variable naming.

I’ve never used either package, but I can say this: errors have a way of staying around for years. If there was an error in this code that originated at NWS, it may or may not be fixed in the various custom applications that are based on it.

Clearly Weather Underground has issues with some portion of it’s code that is supposed to plot METAR data, coincidentally, many of the same months where their plotting routine fails, we also have missing CLIMAT reports.

And on this page at the National Center for Atmospheric Research (NCAR/UCAR), we have reports of the METAR package failing in odd ways, discarding reports:

>On Tue, 2 Mar 2004, David Larson wrote:

>

>>

>>Robb,

>>

>>I've been chasing down a problem that seems to cause perfectly good

>>reports to be discarded by the perl metar decoder.  There is a comment

>>in the 2.4.4 decoder that reads "reports appended together wrongly", the

>>code in this area takes the first line as the report to process, and

>>discards the next line.

>>

Here’s another:

On Fri, 12 Sep 2003, Unidata Support wrote:

>

> ------- Forwarded Message

>

> >To: support-decoders@xxxxxxxxxxxxxxxx

> >From: David Larson <davidl@xxxxxxxxxxxxxxxxxx>

> >Subject: perl metar decoder -- parsing visibility / altimeter wrong

> >Organization: UCAR/Unidata

> >Keywords: 200309122047.h8CKldLd027998

>

>

> The decoder seems to mistake the altimeter value for visibility in many

> non-US METARs.

So my point is this. METAR is fragile, and at the world’s premier climate research center, it seems to have problems that suggest it doesn’t handle worldwide reports yet it appears to be the backbone for all airport based temperature reports in the world, which get collated to GHCN for climate purposes. I think the people that handle these systems need to reevaluate and test their METAR code.

Even better, let’s dump METAR in favor of a more modern format, that doesn’t have variable fields and variable field placements requiring the code to not only decode the numbers/letters, but also the configuration of the report itself.

In today’s high speed data age, saving a few characters from the holdover of teletype days is a wasted effort.

The broader point is; our reporting system for climate data is a mess of entropy on so many levels.

UPDATE:

The missing data can be found at the homepage of DMI, the Danish Meteorological Institute http://www.dmi.dk/dmi/index/gronland/vejrarkiv-gl.htm “vælg by” means “choose city” choose Nuuk to get the numbers monthly back to january 2000.

Thanks for that. The January 2008 data is available, and plotted below at DMI’s web page.

So now the question is, if the data is available, and a prestigious organization like DMI can decode it, plot it, and create a monthly average for it, why can’t NCDC’s Peterson (who is the curator of GHCN) decode it and present it? Why can’t Gavin at GISS do some QC to find and fix missing data?

Is the answer simply “they don’t care enough?” It sure seems so.

UPDATE: 10/4/10 Dr. Jeff Masters of Weather Underground weighs in. He’s fixed the problem on his end:

Hello Anthony, thanks for bringing this up; this was indeed an error in

our METAR parsing, which was based on old NOAA code:

/* Organization: W/OSO242 – GRAPHICS AND DISPLAY SECTION */

/* Date: 14 Sep 1994 */

/* Language: C/370 */

What was happening was our graphing software was using an older version of

this code, which apparently had a bug in its handling of temperatures

below freezing. The graphs for all of our METAR stations were thus

refusing to plot any temperatures below freezing. We were using a newer

version of the code, which does not have the bug, to generate our hourly

obs tables, which had the correct below freezing temperatures. Other

organizations using the old buggy METAR decoding software may also be

having problems correctly handling below freezing temperatures at METAR

stations. This would not be the case for stations sending data using the

synoptic code; our data plots for Nuuk using the the WMO ID 04250 data,

instead of the BGGH METAR data, were fine. In any case, we’ve fixed the

graphing bug, thanks for the help.

REPLY: Hello Dr. Masters.

It is my pleasure to help, and thank you for responding here. One wonders if similar problems exist in parsing METAR for generation of CLIMAT reports. – Anthony

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RichieP
October 3, 2010 4:29 pm

Louis Hissink says:
October 3, 2010 at 3:43 pm
“And it’s the well meant policies of the well meaning that seem to cause most of humanity’s misery over the centuries.”
C.S Lewis:
‘Of all tyrannies, a tyranny sincerely exercised for the good of its victims may be the most oppressive. It would be better to live under robber barons than under omnipotent moral busybodies. The robber baron’s cruelty may sometimes sleep, his cupidity may at some point be satiated; but those who torment us for our own good will torment us without end for they do so with the approval of their own conscience.’

Neil
October 3, 2010 4:39 pm

So can someone tell me how we can measure global temperature change to accuracies of 0.1 C if METAR codeing drops decimals?
Final accuracy of data depends on the least accurate data in the dataset, not the most.

October 3, 2010 4:43 pm

Well,
The entire GHCN V3 inventory is now progressing through geocoding. Damn it takes long. If people want to I’ll see if I can provide access so that the crowd can source away.
Other points:
Missing Values: In GHCN missing values are coded as -9999. No need to worry about dropped signs in this one. been check many many times.
crosspatch:
“Exactly. Which is why I would like to see the “missing” values plugged in and the whole thing run through the mill again to see what pops out the other side. But there are some subjective pieces, such as deciding which way to adjust the values of a station for UHI.
But “liking to see” something and actually having the time and other resources needed to do it is another thing entirely.”
what do you mean by missing values?
1. values that GHCN has thrown out because of QA?
2. Months that are dropped because of missing days in the month?
3. Data that somebody generated that GHCN did not use?
Three different issues:
1. You would have to get back to the source data.
2. this may be in the new QA flags, or you have to go to daily data.
3. You would have to source and verify that data.
three big tasks. Any takers?

October 3, 2010 4:46 pm

Here: how QA works in V3
DMFLAG: data measurement flag, nine possible values:
blank = no measurement information applicable
a-i = number of days missing in calculation of monthly mean
temperature (currently only applies to the 1218 USHCN
V2 stations included within GHCNM)
QCFLAG: quality control flag, seven possibilities within
quality controlled unadjusted (qcu) dataset, and 2
possibilities within the quality controlled adjusted (qca)
dataset.
Quality Controlled Unadjusted (QCU) QC Flags:
BLANK = no failure of quality control check or could not be
evaluated.
D = monthly value is part of an annual series of values that
are exactly the same (e.g. duplicated) within another
year in the station’s record.
K = monthly value is part of a consecutive run (e.g. streak)
of values that are identical. The streak must be >= 4
months of the same value.
L = monthly value is isolated in time within the station
record, and this is defined by having no immediate non-
missing values 18 months on either side of the value.
O = monthly value that is >= 5 bi-weight standard deviations
from the bi-weight mean. Bi-weight statistics are
calculated from a series of all non-missing values in
the station’s record for that particular month.
S = monthly value has failed spatial consistency check
(relative to their respective climatological means to
concurrent z-scores at the nearest 20 neighbors located
withing 500 km of the target; A temperature fails if
(i) its z-score differs from the regional (target and
neighbor) mean z-score by at least 3.5 standard
deviations and (ii) the target’s temperature anomaly
differs by at least 2.5 deg C from all concurrent
temperature anomalies at the neighbors.
T = monthly value has failed temporal consistency check
(temperatures whose anomalies differ by more than
4 deg C from concurent anomalies at the five nearest
neighboring stations whose temperature anomalies are
well correlated with the target (correlation > 0.7 for
the corresponding calendar monthly).
W = monthly value is duplicated from the previous month,
based upon regional and spatial criteria and is only
applied from the year 2000 to the present.
Quality Controlled Adjusted (QCA) QC Flags:
M = values with a non-blank quality control flag in the “qcu”
dataset are set to missing the adjusted dataset and given
an “M” quality control flag.
X = pairwise algorithm removed the value because of too many
inhomogeneities.
DSFLAG: data source flag for monthly value, 18 possibilities:
C = Monthly Climatic Data of the World (MCDW) QC completed
but value is not yet published
G = GHCNM v2 station, that was not a v2 station that had multiple
time series (for the same element).
K = received by the UK Met Office
M = Final (Published) Monthly Climatic Data of the World
(MCDW)
N = Netherlands, KNMI (Royal Netherlans Meteorological
Institute)
P = CLIMAT (Data transmitted over the GTS, not yet fully
processed for the MCDW)
U = USHCN v2
W = World Weather Records (WWR), 9th series 1991 through 2000
0 to 9 = For any station originating from GHCNM v2 that had
multiple time series for the same element, this flag
represents the 12th digit in the ID from GHCNM v2.
See section 2.2.2 for additional information.
2.2.2 STATIONS WITH MULTIPLE TIME

October 3, 2010 5:08 pm

Neil says:
October 3, 2010 at 4:39 pm (Edit)
So can someone tell me how we can measure global temperature change to accuracies of 0.1 C if METAR codeing drops decimals?
Final accuracy of data depends on the least accurate data in the dataset, not the most.
##########
very simply.
if I have 2 people, measured to the nearest foot one comes out at 5 ft, the other at 6 feet, you simply average. 5.5ft. noting of course, that the real people could have
been 4.5 ft and 6.5 feet. which would strangely enough, have given you the same answer. And if you like you can simulate this process. Create a vector of a few hundred thousand measurements: super accurate. take an average. compute an Sd. Then pass them through a filter that drops the accuracy. , then round it, then take an average, then compare it to the average of the super accurate measurements. See what you find out and report back. That will be a good project, you can do it in excell. Look at Lucias spreadsheet, the rounding part has already been done.

LearDog
October 3, 2010 5:09 pm

Hmmm.

Jeff Alberts
October 3, 2010 5:56 pm

The problem with “infilling” and extrapolation is NOT that it gives you the wrong answer.

The problem with infilling is that it’s imaginary data. Whether or not it affects the result is irrelevant. In fact, if it seems not to affect the result, just don’t infill.

crosspatch
October 3, 2010 6:05 pm

“what do you mean by missing values?”
I mean where GISS shows no value for a date for a given station but looking for the exact same station code from another source shows complete data.
Though I am not sure if that is the case with this Nuuk sequence, I believe SteveM has reported in other cases where a given station shows missing values in GISS but shows complete data elsewhere.
The idea would be to enter the complete data record, where available, and see how that influences the GISS result.

October 3, 2010 6:28 pm

First things first..
http://stevemosher.wordpress.com/2010/10/04/v3-station-mash-up/
That’s all the stations in GHCN V3 that are rural airports with lights =A [dark]
Inventory from NOAA ftp
Read in with R ( moshtemp)
Output as CSV
Upload to Google’s Fusion tables. Its public so you can see the data.
Create Filters of the data ( airport = true, airport =T, Lights = A)
Export KML network link
Import Link into google my maps
The whole inventory is rather to large for My maps, so I might have to make sections of the world..

October 3, 2010 6:31 pm

Jeff Alberts October 3, 2010 at 5:56 pm
There is no such thing as imaginary data. There is no such thing as adjusted data. There is no such thing as homogenized data.
There is data; and, there is everything else, which is not data. Even if it once was data, it’s not data anymore. Arguably, it is un-data.

October 3, 2010 6:33 pm

crosspatch says:
October 3, 2010 at 6:05 pm (Edit)
“what do you mean by missing values?”
I mean where GISS shows no value for a date for a given station but looking for the exact same station code from another source shows complete data.
Though I am not sure if that is the case with this Nuuk sequence, I believe SteveM has reported in other cases where a given station shows missing values in GISS but shows complete data elsewhere.
The idea would be to enter the complete data record, where available, and see how that influences the GISS result.
# exact same station code? as what, same as what?
the GHCN “station code” is a combination of codes. but if you mean the same
“WMO” (ghcn is = country code+wmo code+Imod) then you have some more issues as some stations report with the same WMO.. hence the Imod.
do you mean same wmo, same location?
then you have the problem of multiple instruments at the same location. hence the imod.
So tough job.

Milwaukee Bob
October 3, 2010 6:39 pm

From ASTC 2010 (Association of Science-Technology Centers convention) in Honolulu, where Climate Change is all the rage: Another great job Doc.
But I think you’re being a bit kind in saying there is “only simple incompetence” here. Beyond the fact that all of this could have been have been resolved decades ago, this goes way beyond simple incompetence as evidenced by the fact that they have KNOWN about the problem “forever” and have taken NO action to resolve it and have NO plans to do so. But I’m not saying it’s because of some hidden (or not so hidden) agenda, probably more because if it was totally automate (and accurate) it would put hundreds if not thousands out of work and what in the world would we do with all that computer power? – – – and grant money?

duncan binks
October 3, 2010 6:45 pm

Maybe it’s just me but I find that first paragraph beautiful, elegant and poignant. Outstanding work Anthony and please carry on. Top dollar as always.

Spector
October 3, 2010 6:56 pm

I wonder if aviapogenic might be a valid word…

October 3, 2010 7:04 pm

Ed Reid says:
October 3, 2010 at 6:31 pm (Edit)
Jeff Alberts October 3, 2010 at 5:56 pm
There is no such thing as imaginary data. There is no such thing as adjusted data. There is no such thing as homogenized data.
There is data; and, there is everything else, which is not data. Even if it once was data, it’s not data anymore. Arguably, it is un-data.
###########
ed, there is actually no such thing as data.
In the US for many years a volunteer went out to the stevenson screen once a day to “collect” the tmperature.
Well, he never recorded the “temperature” He read an instrument. That instrument gives a certain level of liquid in a glass tube. The numbers on the device are a proxy for “temperature” ( go see what that phyical reality is) Looking at those numbers he was supposed to record the Tmax ( rounding to 1F) and the Tmin ( rounding to 1 F)
Already we have the first application of a human process to the data. Then the two are averaged and rounded again. That is the raw data. Not very “raw”. In that raw data we find obvious mistakes. Transcription errors. recording the max as lower than the min. Rpeating measurements for days in a row. dropped signs, writing 91 as opposed to 19 on a cold winter day. All sorts of things to adjust in the ‘data’. quite simply there is no raw data. every bit of data is filtered by some theory, some model, some application of a method, however seemingly benign.

u.k.(us)
October 3, 2010 7:07 pm

Damn, it’s fun to watch an informed debate.

October 3, 2010 7:10 pm

Jeff Alberts says:
October 3, 2010 at 5:56 pm (Edit)
The problem with “infilling” and extrapolation is NOT that it gives you the wrong answer.
The problem with infilling is that it’s imaginary data. Whether or not it affects the result is irrelevant. In fact, if it seems not to affect the result, just don’t infill.
#################
Most people would just look at the results using both methods and then give an honest appraisal. Its also possible to bias answers by throwing out whole time series because of a missing day or missing month. So, there is no harm in looking at things from several perspectives. its good practice.

Phil's Dad
October 3, 2010 7:38 pm

Several commentators as well as the esteemed author have noted that the UHI adjustment seems inverted. They will all know that this is not the first time; in fact it seems rather common.
I can understand why you would want to make a UHI adjustment – removing a known variable in the data to see what signal, if any, you have left.
Has any one ever given a half satisfactory answer as to why, when doing this, you would adjust historical temperatures downwards rather than leaving the history alone and adjusting more recent temperatures downwards by the UHI amount?
Or for that matter adjusting the historical upward by the UHI amount to match current measuring conditions? Either would be defendable. What is the defence for current practice?

Jantar
October 3, 2010 8:32 pm

Steven Mosher says:
….
“infilling” has no noticeable effect on the answer. I use no infilling whatsoever in my approach and the answer is the same. the reason for this is mathematically obvious.
1 X 3
Average them.
Now in fill:
1,2,3
Average them.
The problem with “infilling” and extrapolation is NOT that it gives you the wrong answer. The problem is it doesnt “carry forward” the uncertainty in the infilling
data operation. In the first example you have two data points. In the second example, you do NOT have three data points, you have 2 data points and the model ( with error) of the third. So if your calculating a standard deviation, you dont get to pretend ( in the second example) that you magically have a third data point, although people do
.
Steven is correct.
The error occurs when the real value for that missing data point is something like -4. Now the true average should be 0 rather than 2. Or, the true average is even lower than either of the data ponits that were available. Infilling is only appropriate when the missing values must fall within the range of the values being used to estimate the missing value. This doesn’t happen with temperatures.
So although infilling doesn’t change the answer from that which is obtained from only using the values that are available, a more correct answer can only be obtained by using those missing values in the first place. DMI seems to have them. Anthony has shown that they are avaible, so why doesn’t GISS use them? Maybe because they would give an answer that GISS doen’t want.

crosspatch
October 3, 2010 9:08 pm

do you mean same wmo, same location?
then you have the problem of multiple instruments at the same location. hence the imod.

I will see if I can dig it out of Steve’s archives. It was some time ago, maybe a couple of years ago. I seem to recall that it might have been at about the same time it was noticed that sometimes the data from the previous month was re-reported as the current month’s data which doesn’t make so much difference in the dead of summer or winter but makes a huge difference in the fall and spring.
I might be connecting the events in my mind, though, because they were both discovered in sort of the same way where a large anomaly was noted and someone went and looked at why that might be. In one case it was duplicated data reporting in (if memory serves) a month in the fall and the other case was where there was very little data in the GISS data for that station that month but a check of (again, if memory serves) the data source showed a complete report.

October 3, 2010 9:53 pm

DMI seems to have them. Anthony has shown that they are avaible, so why doesn’t GISS use them? Maybe because they would give an answer that GISS doen’t want.
#######
it’s pretty simple. You have these things to consider
1. the DMI process ( it’s models + data )
2. Do they pick DMI up going forward or recalculate the past. If they recalculate
the past, many people will scream ( see recent threads here )
3. The DMI would have to accepted at a trusted source. not saying they arent
4. CRU and NCDC dont use it.
5. Nothing stops anybody else from importing the data into GISSTEMP and running
the numbers.
6. maybe they’ve done it and it makes no difference.
Many practical reasons for not doing it. when somebody tries #5, then we have a real argument. until then, there is no argument, only questions

October 3, 2010 10:08 pm

Phil’s Dad says:
October 3, 2010 at 7:38 pm (Edit)
Several commentators as well as the esteemed author have noted that the UHI adjustment seems inverted. They will all know that this is not the first time; in fact it seems rather common.
######
steve McIntyre has collated the frequency of adjustments. we can speak precisely about it. Also, the adjustment, in the end, amounts to very little. Take or leave it, the world is warming.
“I can understand why you would want to make a UHI adjustment – removing a known variable in the data to see what signal, if any, you have left.
Has any one ever given a half satisfactory answer as to why, when doing this, you would adjust historical temperatures downwards rather than leaving the history alone and adjusting more recent temperatures downwards by the UHI amount?”
Its a consequence of the algorithm. The “urban” station is compared to it “rural” neighbors. the urban station is coerced to match its rural friends. So you could just DROP the urban and get the same answer. people should not forget that basic mathematical fact. The urban is forced to match the rural. You get essentially the same answer by dropping the urban all together. essentially, at the global average level.
“Or for that matter adjusting the historical upward by the UHI amount to match current measuring conditions? Either would be defendable. What is the defence for current practice?”
The algorithm does what it does. the urban is adjusted to match the rural.
THIS IS WHY the most important question is the question over metadata.
What is urban? what is rural?
Cause the algorithm, will merely coerce the urban to match the rural. Thats it.
No mystery there. No devastating criticism. You dont like the adjustemnt? just use rural stations. Answer doesnt change. Which means… what is rural? does nightlights pick out rural? should we look at land use? building height? irrigation, or “lights”
Again. the algorithm is not the issue. mathematically, not the issue. yes it gives bizaare adjustments, but it is adjusting what it thinks is urban ( and small town) to what it thinks is rural. which means, the whole enterprise rests on the definition of urban/rural.

October 3, 2010 11:08 pm

Wow. Doesn’t this all just remind you of HARRY_READ_ME ?
It’s hard to believe that the billions/trillions of dollars spent trying to buttress the fearsome Catastrophic Global Warming theory have all been justified on the basis of flat out lousy record keeping and amateur hour programming. Incredible.

October 3, 2010 11:17 pm

here folks try editing this
I’ve shared a map with you called Greenland:
http://maps.google.com/maps/ms?ie=UTF&msa=0&msid=
115776389757820887459.000491c44e0ff63524f70