I decided to do myself something that so far NOAA has refused to do: give a CONUS average temperature for the United States from the new ‘state of the art’ United States Climate Reference Network (USCRN). After spending millions of dollars to put in this new network from 2002 to 2008, they are still giving us data from the old one when they report a U.S. national average temperature. As readers may recall, I have demonstrated that old COOP/USHCN network used to monitor U.S. climate is a mishmash of urban, semi-urban, rural, airport and non-airport stations, some of which are sited precariously in observers backyards, parking lots, near air conditioner vents, airport tarmac, and in urban heat islands. This is backed up by the 2011 GAO report spurred by my work.
Here is today’s press release from NOAA, “State of the Climate” for July 2012 where they say:
The average temperature for the contiguous U.S. during July was 77.6°F, 3.3°F above the 20th century average, marking the hottest July and the hottest month on record for the nation. The previous warmest July for the nation was July 1936 when the average U.S. temperature was 77.4°F. The warm July temperatures contributed to a record-warm first seven months of the year and the warmest 12-month period the nation has experienced since recordkeeping began in 1895.
OK, that average temperature for the contiguous U.S. during July is easy to replicate and calculate using NOAA’s USCRN network of stations, shown below:
The U.S. Climate Reference Network (USCRN) consists of 114 stations developed, deployed, managed, and maintained by the National Oceanic and Atmospheric Administration (NOAA) in the continental United States for the express purpose of detecting the national signal of climate change. The vision of the USCRN program is to maintain a sustainable high-quality climate observation network that 50 years from now can with the highest degree of confidence answer the question: How has the climate of the nation changed over the past 50 years? These stations were designed with climate science in mind. Three independent measurements of temperature and precipitation are made at each station, insuring continuity of record and maintenance of well-calibrated and highly accurate observations. The stations are placed in pristine environments expected to be free of development for many decades. Stations are monitored and maintained to high standards, and are calibrated on an annual basis. In addition to temperature and precipitation, these stations also measure solar radiation, surface skin temperature, and surface winds, and are being expanded to include triplicate measurements of soil moisture and soil temperature at five depths, as well as atmospheric relative humidity. Experimental stations have been located in Alaska since 2002 and Hawaii since 2005, providing network experience in polar and tropical regions. Deployment of a complete 29 station USCRN network into Alaska began in 2009. This project is managed by NOAA’s National Climatic Data Center and operated in partnership with NOAA’s Atmospheric Turbulence and Diffusion Division.
So clearly, USCRN is an official effort, sanctioned, endorsed, and accepted by NOAA, and is of the highest quality possible. Here is what a typical USCRN station looks like:
A few other points about the USCRN:
- Temperature is measured with triple redundant air aspirated sensors (Platinum Resistance Thermometers) and averaged between all three sensors. The air aspirated shield exposure system is the best available.
- Temperature is measured continuously and logged every 5 minutes, ensuring a true capture of Tmax/Tmin
- All stations were sited per Leroy 1999 siting specs, and are Class 1 or Class 2 stations by that siting standard. (see section 2.2.1 here of the USCRN handbook PDF)
- The data goes through quality control, to ensure an errant sensor hasn’t biased the values, but is otherwise unchanged.
- No stations are near any cities, nor have local biases of any kind that I have observed in any of my visits to them.
- Unlike the COOP/USHCN network where they fought me tooth and nail, NOAA provided station photographs up front to prove the “pristine” nature of the siting environment.
- All data is transmitted digitally via satellite uplink direct from the station.
So this means that:
- There are no observer or transcription errors to correct.
- There is no time of observation bias, nor need for correction of it.
- There is no broad scale missing data, requiring filling in data from potentially bad surrounding stations. (FILNET)
- There are no needs for bias adjustments for equipment types since all equipment is identical.
- There are no need for urbanization adjustments, since all stations are rural and well sited.
- There are no regular sensor errors due to air aspiration and triple redundant lab grade sensors. Any errors detected in one sensor are identified and managed by two others, ensuring quality data.
- Due to the near perfect geospatial distribution of stations in the USA, there isn’t a need for gridding to get a national average temperature.
Knowing this, I wondered why NOAA has never offered a CONUS monthly temperature from this new network. So, I decided that I’d calculate one myself.
The procedure for a CONUS monthly average temperature from USCRN:
- Download each station data set from here: USCRN Quality Controlled Datasets.
- Exclude stations that are part of the USHCN-M (modernized USHCN) or USRCRN-Lite stations which are not part of the 114 station USCRN master set.
- Exclude stations that are not part of the CONUS (HI and AK)
- Load all July USCRN 114 station data into an Excel Spreadsheet, available here: CRN_CONUS_stations_July2012_V1.2
- Note stations that have missing monthly totals data. Three in July 2012, Elgin, AZ, (4 missing days) Avondale, PA,(5 missing days) McClellanville, SC, (7 missing days) and set their data aside to be dealt with separately.
- Do sums and calculate CONUS area averages from the Tmax, Tmin, Tavg and Tmean data provided for each station.
- Do a separate calculation to see how much difference the stations with missing/partial data make for the entire CONUS.
Here are the results:
USA Monthly Mean for July 2012: 75.72°F
(111 stations)
USA Monthly Average for July 2012: 75.51°F
(111 stations)
USA Monthly Mean for July 2012: 75.74°F
(114 stations, 3 w/ partial missing data, difference 0.02)
USA Monthly Average for July 2012: 75.55°F
(114 stations, 3 w/ partial missing data, difference 0.04)
============================
Comparison to NOAA’s announcement today:
Using the old network, NOAA says the USA Average Temperature for July 2012 is: 77.6°F
Using the NOAA USCRN data, the USA Average Temperature for July 2012 is: 75.5°F
The difference between the old problematic network and new USCRN is 2.1°F cooler.
This puts July 2012, according to the best official climate monitoring network in the USA at 1.9°F below the 77.4°F July 1936 USA average temperature in the NOAA press release today, not a record by any measure. Dr. Roy Spencer suggested earlier today that he didn’t think so either, saying:
So, all things considered (including unresolved issues about urban heat island effects and other large corrections made to the USHCN data), I would say July was unusually warm. But the long-term integrity of the USHCN dataset depends upon so many uncertain factors, I would say it’s a stretch to to call July 2012 a “record”.
This result also strongly suggests, that a well sited network of stations, as the USCRN is designed from inception to be, is totally free of the errors, biases, adjustments, siting issues, equipment issues, and UHI effects that plague the older COOP USHCN network that is a mishmash of problems that the new USCRN was designed to solve.
It suggests Watts et al 2012 is on the right track when it comes to pointing out the temperature measurement differences between stations with and without such problems. I don’t suggest that my method is a perfect comparison to the older COOP/USHCN network, but the fact that my numbers come close, within the bounds of the positive temperature bias errors noted in Leroy 1999, and that the more “pristine” USCRN network is cooler for absolute monthly temperatures (as would be expected) suggests my numbers aren’t an unreasonable comparison.
NOAA never mentions this new pristine USCRN network in any press releases on climate records or trends, nor do they calculate and display a CONUS value for it. Now we know why. The new “pristine” data it produces is just way too cool for them.
Look for a regular monthly feature using the USCRN data at WUWT. Perhaps NOAA will then be motivated to produce their own monthly CONUS Tavg values from this new network. They’ve had four years to do so since it was completed.
UPDATE: Some people questioned what is the difference between the mean and average temperature values. In the monthly data files from USCRN, there are these two values:
T_MONTHLY_MEAN
T_MONTHLY_AVG
http://www.ncdc.noaa.gov/crn/qcdatasets.html
The mean is the monthly (max+min)/2, and the average is the average of all the daily averages.
UPDATE2: I’ve just sent this letter to NCDC – to ncdc.info@ncdc.noaa.gov
Hello,
I apologize for not providing a proper name in the salutation, but none was given on the contact section of the referring web page.
I am attempting to replicate the CONUS temperature average of 77.6 degrees Fahrenheit for July 2012, listed in the August 8th 2012, State of the Climate Report here: http://www.ncdc.noaa.gov/sotc/
Pursuant to that, would you please provide the following:
1. The data source of the surface temperature record used.
2. The list of stations used from that surface temperature record, including any exclusions and reasons for exclusions.
3. The method used to determine the CONUS average temperature, such as simple area average, gridded average, altitude corrections, bias corrections, etc. Essentially what I’m requesting is the method that can be used to replicate the resultant 77.6F CONUS average value.
4. A flowchart of the procedures in step 3 if available.
5. Any other information you deem relevant to the replication process.
Thank you sincerely for your consideration.
Best Regards,
Anthony Watts
===================================================
Below is the response I got to the email address provided in the SOTC release, some email addresses redacted to prevent spamming.
===================================================
—–Original Message—–
From: mailer-daemon@xxxx.xxxx.xxx
Date: Thursday, August 09, 2012 3:22 PM
To: awatts@xxxxxxx.xxx
Subject: Undeliverable: request for methods used in SOTC press release
Your message did not reach some or all of the intended recipients.
Sent: Thu, 9 Aug 2012 15:22:43 -0700
Subject: request for methods used in SOTC press release
The following recipient(s) could not be reached:
ncdc.info@ncdc.noaa.gov
Error Type: SMTP
Error Description: No mail servers appear to exists for the recipients address.
Additional information: Please check that you have not misspelled the recipients email address.
hMailServer
===============================
UPDATE3: 8/10/2012. This may put the issue to rest about straight averaging -vs- some corrected method. From http://www.ncdc.noaa.gov/temp-and-precip/us-climate-divisions.php
It seems they are using TCDD (simple average) still. I’ve sent an email to verify…hopefully they get it.
Traditional Climate Divisional Database
Traditionally, climate division values have been computed using the monthly values for all of the Cooperative Observer Network (COOP) stations in each division are averaged to compute divisional monthly temperature and precipitation averages/totals. This is valid for values computed from 1931 to the present. For the 1895-1930 period, statewide values were computed directly from stations within each state. Divisional values for this early period were computed using a regression technique against the statewide values (Guttman and Quayle, 1996). These values make up the traditional climate division database (TCDD).
Gridded Divisional Database
The GHCN-D 5km gridded divisional dataset (GrDD) is based on a similar station inventory as the TCDD however, new methodologies are used to compute temperature, precipitation, and drought for United States climate divisions. These new methodologies include the transition to a grid-based calculation, the inclusion of many more stations from the pre-1930s, and the use of NCDC’s modern array of quality control algorithms. These are expected to improve the data coverage and the quality of the dataset, while maintaining the current product stream.
The GrDD is designed to address the following general issues inherent in the TCDD:
- For the TCDD, each divisional value from 1931-present is simply the arithmetic average of the station data within it, a computational practice that results in a bias when a division is spatially undersampled in a month (e.g., because some stations did not report) or is climatologically inhomogeneous in general (e.g., due to large variations in topography).
- For the TCDD, all divisional values before 1931 stem from state averages published by the U.S. Department of Agriculture (USDA) rather than from actual station observations, producing an artificial discontinuity in both the mean and variance for 1895-1930 (Guttman and Quayle, 1996).
- In the TCDD, many divisions experienced a systematic change in average station location and elevation during the 20th Century, resulting in spurious historical trends in some regions (Keim et al., 2003; Keim et al., 2005; Allard et al., 2009).
- Finally, none of the TCDD’s station-based temperature records contain adjustments for historical changes in observation time, station location, or temperature instrumentation, inhomogeneities which further bias temporal trends (Peterson et al., 1998).
The GrDD’s initial (and more straightforward) improvement is to the underlying network, which now includes additional station records and contemporary bias adjustments (i.e., those used in the U.S. Historical Climatology Network version 2; Menne et al., 2009).
The second (and far more extensive) improvement is to the computational methodology, which now addresses topographic and network variability via climatologically aided interpolation (Willmott and Robeson, 1995). The outcome of these improvements is a new divisional dataset that maintains the strengths of its predecessor while providing more robust estimates of areal averages and long-term trends.
The NCDC’s Climate Monitoring Branch plans to transition from the TCDD to the more modern GrDD by 2013. While this transition will not disrupt the current product stream, some variances in temperature and precipitation values may be observed throughout the data record. For example, in general, climate divisions with extensive topography above the average station elevation will be reflected as cooler climatology. A preliminary assessment of the major imapacts of this transition can be found in Fenimore, et. al, 2011.
Rattus Norvegicus says:
BTW, is 2012 a record in the CRN record, because that is the proper comparison.
No, it isnt. That would only be a proper comparison if their periods of record were comparable. They are not.
Rattus: Yes, very good idea. Use all the USCRN July data from each station since 2008 to calculate the average for that station. Then calculate the average from that station for each July, and compare the anomalies. July 2012 will be likely be the highest for many of the stations.
Then the anomalies can be averaged using a gridded procedure to get an estimate of the USCRN July 2012 CONUS anomaly.
Finally, the USCRN CONUS anomalies for each July can be compared to the anomalies reported by the NCDC to see if the trends match. The baselines of course, will still be different, but the trends should be comparable.
If one had the time and inclination, the baseline for 2008-2012 for the NCDC data could determined, and adjusted out, to get a better comparison.
The result is likely to be very close.
Wouldn’t this also give them an excellent means to “Adjust” the old network in a manner that matches reality? If the new network is designed to not have all the issues they are constantly trying to adjust for in the old network, then they merely have to adjust to old network results so that they match the new network. They can they revise all the old results accordingly.
Here is how I’d suggest comparing USHCN and USCRN to see which July is the “hottest”:
Take all USHCN stations, turn them into anomalies relative to a particular baseline period (unilike in Hansen’s paper, for these purposes the choice isn’t particularly important), assign them to 2.5×3.5 lat/lon grids, average anomalies within grid cells, apply a land mask, and weight each grid cell by its resulting area to create a CONUS-weighted average anomaly.
Do the same process for USCRN. Now take the resulting anomalies and fit them together over a common period of overlap (say, 2004 to 2008). Now you have a more apples-to-apples way to compare a 1930s USHCN temperature with a 2012 USCRN temperature, given that the stations in both networks likely have different absolute temperatures due to elevation, spatial coverage, siting, etc.
REPLY: The CRN network wasn’t complete until 2008. From 2002 to 2008 there were large spatial distribution gaps, so calculation of grids/anomalies is really problematic. Four years of complete data isn’t enough to calculate a meaningful baseline from. Besides, this article is about absolute temperatures, not anomalies. What we really need to know is how NOAA determines their CONUS area average for a month. Once that is known, then I can replicate with CRN. IMHO that’s the right way, not trying to calc anomalies to compare to an absolute number issued by NOAA. – Anthony
cms
Spelling aside, from each according to his means to each according to his needs, says it all and the concept that crime is purely a socioeconomic problem along with the socialist theories of economics have not worked out well even when applied in a more benevolent fashion than in the USSR, just look at Europe’s general economic condition, and where the USA is headed. The gentlemen to whom I was referring are both socialists irrespective of the accuracy of the quote from Marx, C or K as I have seen it both ways. You may be right but it is of little consequence. Communism did not even work in the early Christian Church as there will always be those who freeload on such a system, see St. Peter’s letter on this in the Bible.
@Anthony – If you object to how NOAA is doing things then explain that in your post, but you know that many people will not think of an altitude adjustment to temperature and your original post makes NOAA look very bad if you don’t mention that. You don’t want to give bad impressions of people for false reasons, because that makes you look very bad. If they deserve to look bad, then explain accurate reasons why. Bring to the front every significant thing that casts doubt on your theory. That is the scientific way and the way of any rational person who cares about their credibility. At Real Climate they would have probably just deleted or altered my or Nick Stokes post, but I think you have more integrity than that. Update your original post, let it show, and do it quick.
[REPLY: Anthony has already responded to your concerns. Continuing this approach qualifies as badgering and thread-bombing. You’ve had your say, now drop it or be snipped. -REP]
Tom in Florida says:
August 9, 2012 at 5:29 am
>>Stokes does make a valid point. Comparing raw data from different sets is not correct when looking for changes>>
Excuse me? – so if I have, say, a dataset of measured adult heights in one county, and another in a separate county,- and I see that one set shows increasing height with time, and the second set also shows increasing height – what do the two datasets confirm? That there is a gradual increasing in height! They don’t mix, they only independently correlate the ‘observation’! Whats the flipping problem?
Anthony decides to look and see if one dataset confirms//correlates with the other – they don’t! so either the hypothesis is wrong or one of the datasets is wrong – Now, I don’t know which is right or wrong without trawling through all the data and mechanisms, etc, etc – but based on Anthony’s description, I’d hazard a guess that it’s the historical adjusted data that’s a bit iffy. What’s to argue about? As I see it – there isn’t really an apples and oranges thing here – lapse rate adjustment or whatever – if there is an underlying trend (of increasing temps – and specifically ‘record’ July temps) it would surely be apparent in any RAW data? It is not apparent in the spanky new dataset – Watts up with that?
JJ,
The USCRN and the USHCN networks are in no sense comparable — the sets of stations are entirely disjoint. All you can say is that they are different because, well, they are. Paul’s suggestion is good, although there really is not enough data available for trend computation, but computing the anomalies is a good idea.
Not entirely, but yes, the fully adjusted results from the old network should match the USCRN network gridded anomaly. And from the work done by Menne in a paper in 2010 using partial data up through 2009, they matched beautifully. But now for many of the USCRN stations, we have over five years of data.
This has always been the weakest point in the SurfaceStations project logic. If one really wanted to quantify the siting issues, just compare the gridded anomalies from the old network with the anomalies from the USCRN stations in and surrounding that grid.
I work in the health-care field. Another profession known for creative use of statistics for personal gain…
All testing should (but rarely is) held to a common standard wherein all measurements must be sensitive, specific AND meaningful. When held to this standard, medical testing is abysmal at best.
I’m oddly uplifted to see that we (as a profession) are not alone in this performance. In reality, I’m saddened to see that any other field which claims to be “scientific” is as bad as we are. Congratulations “climatology” – you’ve made it.
It’s getting to the point that I do not believe ANYTHING that comes out of the government’s mouth anymore. These liars are practicing a faith-based religion every bit as superstitious as any religion or superstition attributed to deities, and they will do anything to boost their religion and downplay anything that does not jive with their “scriptures”.
Maybe sombody should do a freedom of information act request for the NOAA method of calculating the temps.
Interestingly July 2012 saw the highest level of solar flare activity of any July in the past 10 years, and probably af any other month (although I haven’t had a chance to go through data for every single month)
Note especially the last 3 columns of Solar flares.
http://www.solen.info/solar/old_reports/2012/july/indices.html
http://www.solen.info/solar/old_reports/2011/july/indices.html
http://www.solen.info/solar/old_reports/2010/july/indices.html
http://www.solen.info/solar/old_reports/2009/july/indices.html
http://www.solen.info/solar/old_reports/2008/july/indices.html
http://www.solen.info/solar/old_reports/2007/july/indices.html
http://www.solen.info/solar/old_reports/2006/july/indices.html
http://www.solen.info/solar/old_reports/2005/july/indices.html
http://www.solen.info/solar/old_reports/2004/july/indices.html
http://www.solen.info/solar/old_reports/2003/july/indices.html
For solar activity data since 2003 here are monthly statistics.
http://www.solen.info/solar/old_reports/
Interestingly July 2008, didn’t see a single flare, and was 2.63 degrees colder (by noaa standards) than July 2012.
July 2009, which is the lowest July of the past decade, saw only 2 small class c flares.
Is it a coincidence that when the Sun flares up, temps on Earth go up, and when the sun stops flaring, temps on Earth go down?
Rattus Norvegicus says:
The USCRN and the USHCN networks are in no sense comparable — the sets of stations are entirely disjoint. All you can say is that they are different because, well, they are.
They are comparable in some senses, but not all such comparisons are legitimate or meaningful. They are not at all comparable with respect to period of record. The period of record of CRN is so short as to render any claim of “its a record!” (as you suggested be done) to be absolutely meaningless.
When backed by HCN, such claims are merely essentially meaningless.
Paul’s suggestion is good, although there really is not enough data available for trend computation, but computing the anomalies is a good idea..
No, that is pointless.
The proper use of CRN wrt HCN is to point to the former and say “if we had done that 150 years ago, we’d be a lot further along toward finding a correct answer to the wrong question than we are now”, while pointing at the latter and laughing derisively.
Actually the USCRN has plenty of data to identify siting issues, and measurement problems, because you can use daily or weekly data. It doesn’t take 30 years of data to identify measurement problems; five years of daily data should be plenty.
And regarding sampling requirements; if I recall correctly, only 13 properly sited USCRN station sites in the CONUS would give reasonably accurate anomalies. The 107 USCRN stations are plenty, and in fact over samples the region.
The claims made in Watts et.al. 2012 draft would have been easy to verify, if the USCRN data had been used.
I would like to know how this set of 600 stations compares.
“Unadjusted data of long period stations in GISS show a virtually flat century scale trend
Posted on October 24, 2011 by Anthony Watts”
http://wattsupwiththat.com/2011/10/24/unadjusted-data-of-long-period-stations-in-giss-show-a-virtually-flat-century-scale-trend/
“There are several examples of long-running temperature records that fail to show any
substantial long-term warming signal; examples are the Central England Temperature record and the one from Hohenpeissenberg, Bavaria. It therefore seemed of interest to look for long-running US stations in the GISS dataset. Here, I selected for stations that had continuously reported at least one monthly average value (but usually many more) for each year between 1900 and 2000. This criterion yielded 335 rural stations and 278 non-rural ones. “
Kev-in-UK,
To extend your toy example a bit, if you were comparing heights in two different countries and sampled 100 people in each country in a non-random manner, you would have to make sure to control for factors like age (or altitude) that are correlated with height (or absolute temperature), otherwise you wouldn’t really be comparing like groups and would draw incorrect conclusions from your data.
I have to say Anthony, that I find your responses, and the responses of your moderators to be disappointing. You are getting defensive and irrational. You simply have to admit that the two networks really are not comparable via absolute temperature measurements, nonsense about not knowing the adjustments done to one or the other of the networks doesn’t help you any. If you know less about how one number was made, I’d think that would make it even less comparable to another number.
What you’ve done is the equivalent to answer the claim “2012 was the hottest month on record in Chicago” – with “No it isn’t, it’s two degrees cooler in Springfield!”. It’s a non-sequiter – so what if a different network produces a different absolute number?
Also, all the conspiratorial stuff about NOAA not using the new network really makes you look bad. The new network simply doesn’t have enough data to be of much use. If you have some evidence that it is producing a statistically significant lower warming trends, which is being ignored, by all means produce that evidence.
I agree that I think it’s odd that NOAA is publishing absolute temperatures for one month, for 2% of the landmass of the world, when they’ve spent all this time telling us global warming is long term, and well, global, and about changes in temperature.
REPLY: The CRN has enough data to do one month, and that’s all I’m talking about in this post. All these other issues are overreaching. I’ll be thrilled to adjust my method once we can find out how NOAA creates their CONUS Tavg. Until they do, all concerns about matching networks are speculative. Bear in mind that Gavin Schmidt once said that all we need is about 50 stations. At 111, I think we have more than enough to get a good CONUS reading. My interest is getting the procedure from NOAA, and then we’ll revisit. – Anthony
I have a naive question about this: Anthony is comparing a supposed near-perfect sighting climate station data and its results to the temperature record that NOAA said it was. I sense something wrong here. Anthony takes good “unbiased” data that says it was 75F degrees. He then compares that to the historical record of BIASED temperatures over the last 100 years. He then tries to plug his temperature in that BIASED record to see where the “unbiased” value will fit.
Would not a better way to handle the “unbiased” data is to compare it to the data pulled from the Watts et al 2012 paper?
.. just a thought.
Kev-in-UK says:
August 9, 2012 at 1:28 pm
Tom in Florida says:
August 9, 2012 at 5:29 am
>>Stokes does make a valid point. Comparing raw data from different sets is not
correct when looking for changes>>
“Excuse me? – so if…..”
Kev,
First of all that was only the lead in line of my post. I hope you went back and read the whole thing. Now, you cannot look for anomalies by comparing corrupted data to uncorrupted data when the data was taken by different methods, which is what Stokes was saying. That is a valid point. What Stokes was also implying was that it is OK to use corrupted data for anomalies as long as the corruption continues throughout the whole data set. Now that is silly when looking for what is actually happening in the real world as Anthony was doing. I went on to say that when a data set is as corrupted as the old COOP/USHCN network is, it cannot be relied upon to be used for anything.
Who is/was responsible for creating USCRN? How is he doing these days?
It will be really interesting to see the twists, turns, and flips as Hansen and friends do whatever they must to make these readings from USCRN go away.
REPLY: The USCRN was created by Tom Karl of NCDC, the current director. – Anthony
When I went looking for the USCRN sites as per Anthony’s attached pdf file. I couldn’t find even one after looking at about a dozen of them. Then it dawned on me that the positions given for the sites are as useless as hip pockets on a singlet, as 45.2 N 113.0 W (Bannack State Park (Old Freight Road Site)) for example, is six nautical miles south of 45.1 N 113.0 W. So just rounding to one decimal place, not to mention typos, can put the position miles out.
As the furore on temperatures is down to hundredths of a degree, why on earth can’t geograhical positions be indicated in (at least) hundredths of a degree?
Great article, BTW, Anthony. Thank you!
Anthony’s brilliant achievement is the following:
“The most important point here is that they aren’t using this network to try to provide any sort of sanity check to the poorly sited mishmashed highly adjusted train wreck that is the COOP/USHCN/GHCN networks – Anthony”
The ball is in NOAA/NCDC’s court. Until they respond to Anthony’s question, there is no point in attempting to debate the standards for comparison of the two networks. NOAA/NCDC has to clearly define USCRN and then state the important relationships between the two networks.
Shame on anyone for reporting a record high temperature for July when some conflicting data are omitted on purpose. Shame on NOAA/NCDC for their tardiness with USCRN. The person responsible for the decisions not to report these matters should explain himself/herself.
Appears to me if NOAA is not altitude adjusting EVERY station – regardless of CRN or USHCN then there is no accurate temp record at all.
And even this, as others have noted, isn’t really sufficient as true altitude is not the accurate measure – rather density altitude on any given day and hour will be true measure
See the update to the story, I’ve requested replication methods from NCDC. Will post correspondence if they reply.
Tom in Florida says:
August 9, 2012 at 2:21 pm
Yes Tom – I realised your argument, but I simply did not agree with your opening statement.
@Zeke,
thats correct of course, I’m sure you realise I was trying to use a single simple metric analogy (ignoring other variables) in order to illustrate that different datasets can be used to see ‘trends’ – especially, as in this case, they are supposed to be ‘seeing’ the same darned metric i.e. Tmax/Tmin !
It matters not if one set of data is altitude corrected – if the trend is there it will still be there after correction as all corrections on any given station will (or should) be the same…..unless they move the thing! The whole metric is supposed to be average record temp increases – any half decent rural station would be expected to show this (if it’s there!), so surely a top quality dataset of top quality well sited stations and top quality instruments MUST be expected to show this simple alleged UNDERLYING trend? Yet they don’t and are NOT reported…..