2010 Thermometer Langoliers Hit List

Well, They Are At It Again

[ UPDATE 12 Feb 2010: Well, I found a CRU letter that describes the update process (in comments below) and it's not pretty. It looks like there is no particular "ready date" for the GHCN data set. While the data are supposed to be ready a few days after the end of the month (the first distribution of monthly data between national meteorology departments is supposed to happen the 4th of the month) the process as described by Phil Jones in one of the ClimateGate emails is far more ersatz and has no specific bounds on when data are to be thought ready enough to use. In particular there is a second distribution that is supposed to happen between the 18th and the 20th of the month (implied as a quality update) that might well have entirely missing records included. So it looks like you never know when the data are 'ready'. It isn't when the data set appears, nor even when it is updated, nor even the end of the month. So at this point we'll have to treat these as "missing in action" lists rather than KIA. At least for another week or two. Also, Dallas Fort Worth has been found in the "failed QA file". It would seem that rather than having a record with a -9999 missing data flag, the record is simply dropped until such month as there is a valid datum, then the record comes back, but with the -9999 flag for the failed month. ]

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 1x-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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About E.M.Smith

A technical managerial sort interested in things from Stonehenge to computer science. My present "hot buttons' are the mythology of Climate Change and ancient metrology; but things change...
This entry was posted in AGW Science and Background, Favorites, NCDC - GHCN Issues and tagged , , . Bookmark the permalink.

41 Responses to 2010 Thermometer Langoliers Hit List

  1. Sean Peake says:

    What happened to Canadian stations?

  2. Ruhroh says:

    And, I’m now pretty sure it goes without saying, that folks are implicitly encouraged to do the time-consuming work of making graphical presentations of the data.

    I’ve come to understand that the ordinarily labor-intensive effort to make plots , at present, involves invisible extra steps for the Cheif at this juncture. I think he’s fully aware that plots would greatly enhance the value of all the unique work he did to make the data available, so no need to pile on.

    Prior to comprehending this, I was among the crew making ‘jelpful’ suggestions that he would ‘reach more folks’ with graphs.

    Just try to channel that energy of ‘gosh a picture would be so great’ into “here’s my chance to make a modest contribution in lieu of hitting the tip jar” .

    Keep good track of that energy for the inevitable
    ‘garsh, why does it take so much effort to make a minimally-imperfect graph?’ moment-of-truth…

    I think it IS okay to report difficulty in operating Cheifio’s ‘beer fund’ thingie.

    Also, for new folks, the Langoliers seems to be a reference to an odd TV movie where the script called for wind, and the film crew obliged with large fans, but the clouds were unimpressed and remained stationary. The directorial shortcut was to add a line to the script; ~ ‘Around here, this kind of wind doesn’t move the clouds’,(?) or something to that effect.
    Hmmm, that ‘explanation’ seems to fall a bit short of a convincing link to thermometer deletions. I understood it at one point, but my longer-lived higher cool mental records seem to have disappeared…

    JMHO
    RR

  3. Bruce says:

    Good job of exposing the unscientific nature of the GHCN. Can I assume that all of these deletions are properly documented in peer-reviewed literature?

    Could you or someone please make a list of information from NCDC, GISS and CRU that is still being withheld? I understand that CRU is still withholding their methodology, stations used and code, and maybe the same for GISS and NCDC. Thanks.

  4. stephen richards says:

    E.M

    One thing I can enlighten on. Strasbourg Its the coldest city in France.

    Aurillac is probably the coldest small town although the alpine town will be colder. Aurillac is in the Massif Centrale Strasbourg is on much lower ground and therefore relatively colder. Its in the east. Brrrrrrrrr very cold in winter and moderatly warm in summer.

  5. e.m.smith says:

    See: http://lukehimself.net/wp-content/uploads/2008/02/langoliers03.jpg

    for what a Langolier is. Steven King book / movie things that ate the past …

  6. 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.

  7. Ian Beale says:

    E.M. For what it is worth

    For South America, Brasil’s list includes a number of state capitals and major cities where I doubt the thermometers are on strike.

  8. Pingback: Green exageration and lies destroys governments! « TWAWKI

  9. 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 your 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 is 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 thermometers at all” Arctica…

  10. Neil Fisher says:

    50194968000 LAUNCESTON AI

    This looks like Launceston Airport.
    http://www.bom.gov.au/climate/averages/tables/cw_091104.shtml
    Suggests that this closed in Jul 2009 and that there is another station nearby to use:
    http://www.bom.gov.au/climate/averages/tables/cw_091237.shtml
    a mere 15.9km (10 miles) away. Of course, it only starts in 1980, whereas the Airport site runs from 1931.

    Launceston sites show some interesting numbers according to BoM.

    site avg min C avg max C length yrs years open
    Ti Tree 7.3 19.4 27 1980-
    Airport 6.2 17.0 70 1931-2009
    Pumping 7.0 18.3 74 1883-1963
    Mt Pleasant 7.1 17.3 21 1962-1989

    Which obviously brings up the questions: Does Launceston contribute to GMST base year, and if so, which one? Airport for the base years, Ti Tree for current? Adjusted how? Hmmm.

    Is this a “dig here”?

  11. Bob K. says:

    (3:58 pm Jan
    So, no thermometers left for Czech Republic at all??)

    You have missed these:

    MILESOVKA
    PRAHA/RUZYNE
    PRAHA-LIBUS
    BRNO/TURANY
    OSTRAVA/MOSNO

    But I couldn’t find any entries for Slovak Republic, and I think that Hungary, Polland and Ukraine stations are missing, too… Who knows how many others…

  12. E.M.Smith says:

    This is a posting of a comment I put up over at WUWT in the thread there that duplicates this article. It’s starting to look like the data constantly change and there is no particular ‘valid day’ when it’s ‘done’:

    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…

  13. Tommy M says:

    Phill tells the BBC

    http://news.bbc.co.uk/2/hi/science/nature/8511701.stm

    Phil Jones, the professor behind the “Climategate” affair, has admitted some of his decades-old weather data was not well enough organised.

    He said this contributed to his refusal to share raw data with critics – a decision he says he regretted

  14. vjones says:

    I’ve just posted this comment on WUWT:

    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.

  15. 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.

  16. AJStrata says:

    RuhRoh,

    Would be happy to make graphs (they don’t take me much time, been doing them way too long). What I need is tab delimited data so I can ingest easily and with quality into Excel. You should have seen the steps I went through on these graphs for Chiefio:

    http://strata-sphere.com/blog/index.php/archives/12736

  17. Ruhroh says:

    AJ

    I enjoy your work and human frailty.

    Hey, I know what you mean about the steps to get a clean ingestion. (!) (some only found after publishing something erroneous, in my case)…

    I eventually decided that the best Excel path was to import as ‘fixed columns’. The only trick there is that Excel default column suggestion is based on the rows visible within the little preview window, and if there are no 2 digit negative numbers in that window, it will pick a column divider that omits the – sign when a 2digit negative number inevitably appears.

    One other refinement was to define the station number column as a fixed point number with nothing to the right of the decimal; otherwise they come through as scientific notation (assuming you wish to retain this ~’metadata’ with your analysis for point-checks. I was trying to make geo-referenced plots).

    One last point; as you probably learned, the wide variation of entries in the station name column is the downfall of any “**” delimited import method, regardless of your choice of “**”.
    So, perhaps you already figured this out.

    I think some of the challenge for el Jefe is the limitations of publishing the tables through WP. But the above methods are relatively simply implemented. Maybe even ‘automated’ through XL tools?

    In case I didn’t convey it, I appreciate your altruistic efforts .
    RR

  18. Pingback: Climate Gate – All the manipulations and lies revealed 302 « UD/RK Samhälls Debatt

  19. AJStrata says:

    Ruhroh,

    I actually take the data into word first, convert to table (which splashes one column across many) and then copy into Excel were I can re-integrate columns into one.

    When I did the last data I could not simply sum 2-3 columns from Word to get back to the original single column because of bleed over, but I used some Excel tricks to pull good data and skip bad.

    Still, it does take time!

    What I would suggest is web safe excel files if possible in addition to the HTML tables for the posts. I see paths, but not sure how to get to them.

  20. Jan says:

    (10:47 pm Bob K.)
    You have missed these:
    MILESOVKA
    PRAHA/RUZYNE
    PRAHA-LIBUS
    BRNO/TURANY
    OSTRAVA/MOSNO

    Maybe it is a misunderstanding. I checked again the GISS database:
    MILESOVKA – JAN-AUG, DEC 2008, SEP-DEC 2009 data missing
    PRAHA/RUZYNE – DEC 2008, SEP-DEC 2009 data missing
    PRAHA-LIBUS – DEC 2008, SEP-DEC 2009 data missing
    BRNO/TURANY – DEC 2008, SEP-DEC 2009 data missing
    OSTRAVA/MOSNO – DEC 2008, SEP-DEC 2009 data missing

    But all this stations, amazingly, have in the GISS database listed the annual means for 2009:
    MILESOVKA – 6.28
    PRAHA/RUZYNE – 8.75
    PRAHA-LIBUS – 9.48
    BRNO/TURANY – 9.83
    OSTRAVA/MOSNO – 9.21
    How one counts the annual mean when the one have data of 4 months missing from the year??

    Moreover I have (courtesy of Czech Met Office-CHMU) the month mean data for MILESOVKA, PRAHA/RUZYNE, BRNO/TURANY and OSTRAVA/MOSNO – let’s compare them with the (non-missing) GISS data for 2009:
    MILESOVKA (JAN-AUG)
    GISS: -5.4 -3.5 0.6 10.7 11.3 12.2 16.0 16.8
    CHMU:-5,4 -3,4 0,7 10,8 11,3 12,2 15,9 16,8
    PRAHA/RUZYNE (JAN-AUG)
    GISS: -3.6 -0.4 4.2 12.6 13.8 15.1 18.5 19.4
    CHMU:-3,6 -0,3 4,1 13 14,2 15,1 18,6 19,6
    BRNO/TURANY (JAN-AUG)
    GISS: -3.2 0.2 4.7 14.0 15.2 16.9 20.2 20.6
    CHMU: -3 0,3 4,8 14,2 15,4 17,3 20,4 20,9
    OSTRAVA/MOSNO (JAN-AUG)
    GISS: -3.0 -0.4 3.6 12.2 14.4 16.1 20.1 19.4
    CHMU:-2,9 -0,2 3,6 12,4 14,7 16,4 20,3 19,5
    Here you see quite very clearly the GISS data AREN’T original station data, but have some adjustments.
    I don’t know if these adjustments are done by GISS or GHCN. GHCN own adjustments look graphicaly like this:
    PRAHA/RUZYNE:
    http://www.appinsys.com/GlobalWarming/climgraph.aspx?pltparms=GHCNT100AJanDecI194920080900111AR61111518000x
    BRNO/TURANY:
    http://www.appinsys.com/GlobalWarming/climgraph.aspx?pltparms=GHCNT100AJanDecI195120080900111AR61111723000x
    OSTRAVA/MOSNO:
    http://www.appinsys.com/GlobalWarming/climgraph.aspx?pltparms=GHCNT100AJanDecI195020080900111AR61111782000x
    As you can see the early years (used for baseline) are by GHCN systematically adjusted DOWN – in case of PRAHA/RUZYNE for more than 1°C !! – moreover the Praha/Ruzyne is the largest Czech international airport, recently substantially enlarged, the thermometer station is 1km2 mean surface thermal solar input at 50°N + and loads of CO2) every couple of minutes.

  21. Jan says:

    Some error occured – the end of the post should be as this:
    … the thermometer station is 1km2 mean surface thermal solar input at 50°N + and loads of CO2) every couple of minutes.

  22. Jan says:

    sorry last try: (most probably there’s a problem with the google satelite maps link)
    …the thermometer station is 200m from runway (here I don’t link the google maps) so one would expect not the adjustments down in the early years – when there were just couple of prop-planes starting a day, but the opposite: adjustments down in the recent years for AHI (airport heat island) because now there a jet plane starts or lands (with its ~200MW thermal output – if I count it well – an equivalent of >1km2 mean surface thermal solar input at 50°N + and loads of CO2) every couple of minutes.

  23. Jan says:

    the googlemaps link using tinyurl:
    http://preview.tinyurl.com/ydnxemg

  24. Ruhroh says:

    AJ

    Yes, better to copy/paste the tables into Notepad or similar txt editor.
    It preserves the columns for Excel fixed column import.

    RR

  25. Roger Sowell says:

    AJ, RuhRoh,

    I also cut and paste then import to produce Excel charts. I cut the data from Chiefio’s page, paste that into Word, save it as plain text, then import that plain text file into Excel as space delimited (not tab delimited). Then can do the Excel manipulations to obtain columns for charts. Very few problems having to manually align data.

    This way, the minus signs behave, at least for me.

    Roger

    btw, AJ, very nice graphs on your site.

  26. vjones says:

    @Jan (February 13, 2010 at 6:35 pm)

    Re your comparison between GISS and CHMU data. I have come across this type of mismatch again and again – I think Canada had to be the worst – so much difference that I wondered if I had the same station (the WMO numbers matched).

    I looked at 40+ stations for TonyB (who also comments here), comparing data from GISS, Rimfrost (http://www.rimfrost.no/ – which uses GISS and/or national sources) and national sources where possible. Often the differences between them were very small, but a large number of points altered in this way.

    At the moment I am looking at the GHCN and GISS ajustments to the GHCN ‘raw’ (unadjusted) data. They are very different too.

    I was also interested in what you said earlier about missing data (February 12, 2010 at 3:58 pm) as I am preparing a post about that on my own blog. GISS and GHCN calculate the annual means via seasonal means:

    DJF – MAM – JJA – SON : four seasonal means averaged to a yearly mean. If one month is missing, sometimes the seasonal mean is just average of the two remaining, but sometimes, it has an ‘assumed’ value for the missing month, for which I have found a few examples where they are clearly wrong (warmer than a typical month). I’ll have a look at the examples you list.

    Verity

  27. Jan says:

    (4:42 am vjones)

    Yes, there are unbelievable gaps in the data, and not just 2009.
    The classical example is the station Praha Klementinum – one of the oldest instrumental temperature record in the world (and thus extremely valuable) having uninterupted measurements since 1770 -courtesy of an old monastery – till present.
    And what the “scientists” from GHCN, GISS and CRU did with this valuable record? They raped the record cuting the pre-1850 (or GISS even the pre-1880) values, then cut it again in 1940, leaving the whole 40ties out and then “connecting” to it another station of Praha/Ruzyne beginning 1949 (even the Praha-Klementinum is still there and measuring) never mentioning the pre-1940 data are from different station -not Praha/Ruzyne but Praha Klementinum.
    Go here http://data.giss.nasa.gov/gistemp/station_data/ – search for “Praha/Ruzyne” ,
    the real Praha/Klementinum data -going back 1770 to the 2009 I’ve here: http://xmarinx.sweb.cz//KLEMENTINUM.xls
    You can compare…Mind the warm decade 1790-1799.
    I would just add that the present UHI of Praha Klementinum is now by me estimated (using comparison with other non-urban stations around) >0.44°C and thus the warming during last 200 years in Prague was <0,25°C/century!!! – I don't know if there is a "catastrophic warming around the globe", but in Prague, central Europe, surely not.

    BUT what is maybe even more interesting -is the thing which one can call "PHANTOM DATA"
    - I'll explain: Courtesy of the Phil Jones declaring the CRU raw data "missing" the CRU subsequently contacted among numerous others the Czech Met Office (CHMI) to send them the Czech raw data again. The Czech Met Office climatologist then was ?clever or stupid? enough and so he asked CRU whether the CRU can send them (to CHMI) the data they (in CRU) have. So the CRU (amazingly) did (even before they declared the data "missing", "lost during moving"…)
    Then, because I and others wrote in Czech some popular articles about the global temperature data manipulation in NOAA, GISS – sourcing information from Chiefio, WUWT and ICECAP.
    The Czech Met Office climatologists then hastily published counter-articles, trying to prove the CRU didn't manipulate the Czech data.
    But with their articles they also published the Czech data the CRU have sent to them together with the data the Czech Met Office has.
    you can download here: http://tinyurl.com/y96e7fh -and see the differences.
    And now it comes: I was then looking to the CRU data published by CHMI and immediately discovered they have there some data for stations Cheb, Brno/Turany, Ostrava/Mosno from 50ties -data which even the Czech Met Office (CHMI) doesn't have (for their own stations). Whole decade of data! Subsequently I discovered even more amazing thing: the CRU has the 1953 data for the station Cheb -even for the PERIOD BEFORE THE STATION WAS EVEN FOUND! (late 1954) Subsequently, of course, I asked the Czech Met Office climatologist (I sometimes discuss with him at the internet) where the data come from. After some pressure in public forums he confessed the Czech Met Office has ABSOLUTELY NO IDEA where the data come from. The climatologist promised me to find out what's going on, so far – even after several weeks – no word from him.
    So that's why I call it the "phantom data".
    And I decided to publish the whole story.
    Did CRU fabricated the data? Are there more fabricated data in their files? Is it the reason why the CRU doesn't want to publish the data and declared them "missing"? (to avoid selfincrimination?) Or the data come originally from NOAA/GHCN and were fabricated there and CRU just tryies to whitewash the whole thing?
    I would think this question maybe should be posed at the British parliament inquiry into Climategate….

  28. vjones says:

    Jan,

    I am absolutely shocked by what you are saying. You are right this needs to be more widely known.

    The database/mapping I am involved here in has the GISS and GHCN data – these two sets treat Praha/Ruzyne very differently (they both start with the GHCN raw data:
    NOAA/GHCN:http://82.42.138.62/NOAAMaps/showstation.asp?wmostationcodeid=365&wmoflag=0
    GISS: http://82.42.138.62/GISSMaps/showstation.asp?wmostationcodeid=365&wmoflag=0

    This is bad enough, but with the data in your Excel spreadsheet we can compare them. It is supposed that GHCN is the source of much of the data for CRU as well.

    Is this something you would consider writing up or contributing to as a full blog post? Perhaps it would get wider attention that way.

  29. Rod Smith says:

    It is beginning to look as if “peer” review of the written word is insufficient, and that validation of both data and software should be required.

  30. Jan says:

    (11:08 am vjones)
    Thanks a lot for looking into the issue.
    Yes, the treatment of the data for Praha/Klementinum/Ruzyne is different. But always wrong.
    What I think is that this stations shouldn’t be connected in one dataset at first place. -Because the Praha/Klementinum station is just in the very very center of Prague, at the elevation of 192m above sea level, in immediate vicinity of the river, surrounded by the always heated buildings -the Klementinum -the old jesuit convent- is now Czech National and University Library (I remember long days spent in the study there)
    googlemaps:
    http://tinyurl.com/yffvz9c
    In contrast Praha/Ruzyne has elevation of 380m and the station is just <200m from the widely used runway 31 at the Prague internationl airport.
    http://tinyurl.com/yknfy36
    That's why the record is so different before and after 1940ies, with the large step – and the raw data then look like there is such a cooling between 1939-1949 during that "missing data decade".
    200m elevation difference itself would make ~1.8°C in the continental climate…
    But the adjustments made by GHCN are absolutely inappropriate. Instead of elevation homogenization, they adjust the 50ties-90ties DOWN -that's the baseline for the global anomaly! – instead of doing the opposite – correcting 90ties-2009 DOWN, because of the Airport Heat Island. They shouldn't adjust 50ties-90ties at all – because in that time there were not much airplanes taking-off at the time by fields surrounded airstrip in a communist country – in contrast with today when the airport is very frequented, recently twice enlarged for the ever rising low-budget flights.
    The connection of Klementinum with Ruzyne is methodological nonsense. But they did it probably because 1. the Ruzyne data are easily available through ICAO airtraffic meteorological network and also 2. because it introduces a confusion – in fact the pre-1950 data are irelevant, because anyway they don't use them for the 1951-1980 default AGW baseline.
    In fact you can find the warming trend at Klementinum record itself. But if you consider estimated UHI and compare the decade 2000-2009 with the decade 1790-1799, then you en up with just <0.25°C/century. But what the warmistas want? They want steep warming in last 60 years. -So they cut pre-1880 values, adjust 50ties-90ties ~2°C DOWN, put it in the baseline, then compare baseline with present …and here you have the warming.
    They probably thought nobody will ever scrutinize it. They probably do this elsewhere too – there is the notorious example of Darwin in Australia and probably more…

    Back to the phantom data.
    Here:
    http://tinyurl.com/yl6k2h2
    you can see the GHCN has even the Cheb (the place where duke of Wallenstein was murdered) data going sparsely back to even 1950 – although the station was even according to the Czech Met Office publicly available record found in 1954:
    http://www.chmi.cz/meteo/opss/stanice.php?ukazatel=cheb
    So where they got the incomplete data in NOAA/GHCN for 1950-1953 and subsequently the CRU for whole 1953 – if even the Czech Met office itself has the data only since december 1954?

    Where from the GHCN has the data from Brno/Turany 1951-1960
    http://tinyurl.com/ykhgo9e
    the CRU has the same without adjustments:
    http://tinyurl.com/yjlgzag
    - when even the Czech Met Office has it only beginning 1961?

    Where from the GHCN has the data from Ostrava/Mosno
    1951-9/1959
    http://tinyurl.com/ykpzwzj
    the CRU has the same without adjustments:
    http://tinyurl.com/yd8lfjo
    - when even the Czech Met Office has it only since 10/1959
    - again, for all see the record of datasets published officially by the Czech Met Office :
    http://tinyurl.com/y96e7fh

    I would like to make a blog post about this not only at my Czech blog, but in english – although I'm not very well at it, so I would need probably some proofreading :), because somebody should I think pose this question at the british parliamentary inquiry – as it fits in their question 3: ""to what extent the CRU and GHCN and GISS datasets are independent"" – which I think largely aren't and even the "phantom data" look like they mostly overlap and come originally from GHCN datasets.
    And anyway -There are so many met stations in Czech Republic, so why the GHCN, CRU, GISS always chose the same, mostly airports, even "engrafting" the record at one of the most worlds unique instrumental record as the Klementinum record undoubtedly is?…

  31. vjones says:

    @Jan,

    I found some of your story using Google Translate to follow some Czech blogs, including your site. However the translations are not very clear.

  32. Jan says:

    (9:40 am vjones)
    I don’t know if you see my last post about more phantom data from 3:13 today, because it shows to me that it still is “awaiting moderation”
    But with the translations from Czech into english it is I think bad. Czech is quite different from English unfortunately – I was looking at the czech-english translation here:
    http://translate.google.cz/translate?js=y&prev=_t&hl=cs&ie=UTF-8&layout=1&eotf=1&u=http%3A%2F%2Fjanzeman.blog.idnes.cz%2F&sl=cs&tl=en
    and it is really funny what nonsenses it sometimes generates :))))

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  34. Jan says:

    I just finished an explanatory paper about the Klementinum record, history, details of the UHI and Local Warming estimation to 0.25°C/century and sent it to the climategate.com as an addendum to the Mr. O’Sullivan’s article.
    The working draft pdf copy with pictures can be found here:
    http://xmarinx.sweb.cz//KlementinumUHI.pdf
    enjoy!

  35. EW says:

    About Klementinum temperatures – maybe it would be good to read first the paper

    “An urban bias in air temperature fluctuations at the Klementinum, Prague, The Czech Republic
    Atmospheric Environment, Volume 33, Issues 24-25, October 1999, Pages 4211-4217
    Rudolf Brázdil, Marie Budíková”

    where the UHI effect, details about the history of this station and comparisons with neighboring stations including Ruzyne are described.

    In the conclusions they say:

    “In a previous paper by Brazdil (1993), the warming at
    the Klementinum due to the intensification of the UHI
    was estimated to be 0.07-0.083C/10 yr from the beginning
    of the century up to about 1940, increasing afterwards
    to about 0.13C/10 yr.
    In this paper, the values of
    warming are somewhat lower. Higher values of warming
    following from the comparison with Milesovka can be
    explained by the fact that at mountain stations, the linear
    upward trend of air temperature in our century is lower
    than at stations at lower elevations.”

    However, the UHI effect ceased to rise in the first half of 60′s and does not change since then.

  36. EW says:

    And about the homogenizations – surely they can be done, but with care and looking at the metadata to find the sources of inhomogenities, not slapping a universal algorithm over the whole globe!
    Here’s a short article about homogenizations in Czech Rep. – it’s a shortened version of the PhD Thesis.

    http://www.met.hu/omsz.php?almenu_id=omsz&pid=seminars&pri=12&mpx=1&sm0=0&tfi=stepanek

    “Adjustment was made for those temperature series in which years of statistically significant inhomogeneities, as indicated by the tests, were clearly related to station metadata (such as relocation).
    Metadata, however, seldom include all the changes taking place at a given station. So adjustment was also carried out for cases of clearly “undoubted” inhomogeneities, which, although not evident in the metadata, were unambiguously indicated by the results of tests and were physically justified (see Brázdil and Stepánek, 1998)”

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  39. Jan says:

    Yeah I know the Brazdils article. Unfortunately he does the comparisons with a bit far and different elevation staions using classical approach. Anyway his figures combined through 20th century and the first decade of 21st century in fact imply very simmilar results as I came to. The issue is the paper doesn’t yet cover the 2000ies, so he doesn’t for example see the additional ~0.05°C/decade UHI surge again recently.
    In fact my figure 0.49°C of overall Klementinum UHI bias against the state in 1790ies is still rather a quite conservative estimation made nevertheless using different, more heuristic datamining approach, because I don’t much care about the trends, what is important to me is just the total bias at present for the temperature comparison with relatively very far past, which is what really counts in climatological sense. So we maybe differ in exact figure, but the order of the magnitude of several tenths of centigrade is the same and again that’s what really counts, when we talk about several tenths of centigrade/century of possible temperature surge in climatological sense, because the real climate from logic depends on absolute temperature values, not the even best way estimated trends.
    If I would go deeper and robustly estimate also the Ruzyne AHI bias I’m almost sure, that I would come to the figures of “no climatologically significant warming in last 200 years at all” in Prague, which would staunchly bury any credibility of the CAGW scam – at least for the people who are able to understand what it is all about. But I leave this for now to others who are paid for making such analyses.

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