Tempelhof Airport, acres of concrete and tarmac
So Just How Hot is Asphalt and Jet Exhaust?
In STEP2 of GIStemp, there is an odd “correction” of the data for Urban Heat Island effects. This is done via looking for “nearby” (up to 1000 km away) “rural” stations. This code spits out a record of the stations it uses as a “rural” reference along with the number of times it was used to “correct” other temperatures. The notion is that if the “rural” station got hotter, then the urban station getting hotter in step with their historical relationship is OK. That is, if the “urban” station runs, on average, 2C hotter than the rural and the rural shows this year 3C hotter than the past while the urban station says it’s 2C hotter than the “rural” station (and presumably 5C hotter than the “rural” station last year), everything is fine. But if the “urban” were 9C hotter than the rural station last year (6 C hotter than the rural station this year), you would suspect 4C of that as UHI.
But what happens when your “rural” reference station is not so “rural”? In particular, airports are known to be rather hot places. The thermometer is often over or very near a large field of black tarmac. There are many cars, trucks, people, etc. nearby. And often there are tons of kerosene being burned up in jet turbines as they taxi and takeoff. Airports are known to be warm places. Your reference would say “No UHI” when in reality it was a little UHI of its own…
Surely GIStemp would filter out “airports” in their UHI correction. No?
No.
While inspecting the output of STEP2, I looked at the file:
PApars.statn.use.GHCN.CL.1000.20
This is a log file from the program PApars.f listing what stations it used as references, and how often, with a radius of 1000 km and a minimum life span of record of 20 years (two “plug numbers” or “parameters” in the PApars code. No reason is given for those particular numbers being chosen. A “dig here” would be to investigate the impact of changing those numbers to see if we have a “cherry pick”.) The file is a list of station IDs, and a count of how often they are used. It has records that look like this:
used station 702910020 3 times used station 702960000 5 times used station 28360003 9 times used station 28690001 13 times used station 29290001 21 times used station 29350003 22 times
Not very informative as it stands. But it has a key that can be used to get information that is useful.
Notice the variable length to the station ID records. The leading “0” is suppressed on printing an INTEGER.
OK, I deleted the redundant “used station “, padded with leading “0” any record that was short one, and deleted the ” times”. Then did a match of those IDs (notice they are 9 digits long, that is, they are the STATION, the Station Mod, and the Modification Record) against the v2.inv file that contains all the useful station info (but only by the first 8 characters, not including the Modification Record flag – since things like LAT and LON don’t change with a different, oh, TOBS history).
The v2.inv file has some interesting things hidden in it’s string of misc. characters. From the program “v2.read.data.f” from NOAA we get this bit of description:
c ic=3 digit country code; the first digit represents WMO region/continent c iwmo=5 digit WMO station number c imod=3 digit modifier; 000 means the station is probably the WMO c station; 001, etc. mean the station is near that WMO station c name=30 character station name c rlat=latitude in degrees.hundredths of degrees, negative = South of Eq. c rlong=longitude in degrees.hundredths of degrees, - = West c ielevs=station elevation in meters, missing is -999 c ielevg=station elevation interpolated from TerrainBase gridded data set c pop=1 character population assessment: R = rural (not associated c with a town of >10,000 population), S = associated with a small c town (10,000-50,000), U = associated with an urban area (>50,000) c ipop=population of the small town or urban area (needs to be multiplied c by 1,000). If rural, no analysis: -9. c topo=general topography around the station: FL flat; HI hilly, c MT mountain top; MV mountainous valley or at least not on the top c of a mountain. c stveg=general vegetation near the station based on Operational c Navigation Charts; MA marsh; FO forested; IC ice; DE desert; c CL clear or open; c not all stations have this information in which case: xx. c stloc=station location based on 3 specific criteria: c Is the station on an island smaller than 100 km**2 or c narrower than 10 km in width at the point of the c station? IS; c Is the station is within 30 km from the coast? CO; c Is the station is next to a large (> 25 km**2) lake? LA; c A station may be all three but only labeled with one with c the priority IS, CO, then LA. If none of the above: no. c iloc=if the station is CO, iloc is the distance in km to the coast. c If station is not coastal: -9. c airstn=A if the station is at an airport; otherwise x c itowndis=the distance in km from the airport to its associated c small town or urban center (not relevant for rural airports c or non airport stations in which case: -9) c grveg=gridded vegetation for the 0.5x0.5 degree grid point closest c to the station from a gridded vegetation data base. 16 characters. c A more complete description of these metadata are available in c other documentation
Though it declines to say just where that “other documentation” is located…
But what really caught my eye was that “airstn” flag 8 lines up from the bottom. So they actually DO know what is an airport and what is not!
OK. After matching the records by station ID, I could see if any of these “rural reference stations” was, in fact an airport. What I found was about 500 of them (out of 2179 total, so a little under 1/4 of the pristine rural stations used to “remove” UHI effect are airports…)
I say “about” because some of the stations that do NOT have an “A” for “airstn” do self describe as an AIRPORT. And not all AIRPORTS self describe. So it is somewhat unclear just how many of the ones with no “A” flag are also airports. These records are a bit ugly, and too long for fixed format on WordPress (i.e. they truncate the interesting bits if you don’t let them wrap and strip out excess blanks.):
2 501 94203000 BROOME AIRPOR -17.95 122.22 9 2R -9FLDECO 1x-9WARM GRASS/SHRUBB 12
75 618 16650000 LIMNOS(AIRPOR 39.92 25.23 4 91R -9HIxxCO 1x-9WATER B 0
5 435 78526014 BORINQUEN/AIRPORT 18.50 -67.13 69 28R -9HIxxCO 4x-9WATER C 27
The interesting bit is that “1x-9WARM GRASS” or 4x-9WATER” with the x saying “not an airport” when the name clearly says AIRPORT. The CO says it’s a “coastal” station and the following digit is km to the water (1 , 1, 4 in these three records). So I’d presume these might be some kind of sea port; though I have no idea what the heat signature is at a sea port.
The ones with an “A” in that field are “airstn” type, so, for example, the first record here is “EMPORIA/FAA AIRPORT” with “no-9A10WARM CROPS” as the description. That A means Air Station (while the “no” says ‘not coastal’ which gets a ‘-9′ for distance to coast (the earlier -9 says “no people); and the WARM CROPS is, um, odd…) Oh, and the “S” in that early part of the record where it says “357S” means “associated with a town of 10,000 to 50,000 population. The top step is “U” for >50,000. I would not expect to find any “U” stations here, but have not searched the whole body of records.
Still, the notion that an airport at a city with 49,000 people would have NO UHI effect seems broken to me.
One is left to wonder what the actual impact on the temperature history would be from 500 station IDs with thousands of “uses” for “correcting” the history of temperatures… I’ll figure out a benchmark and a way to measure it, eventually, for now I’m just a bit “stunned” at using AIRPORTS as rural surrogates. One hopes that these are small, rural, and maybe even with a few having a grass field or two. But most airports I’ve been at, even the little ones, had tarmac near the temperature station and tended to grow over time…
So here are the 500 records. I’ll see if I can make this more readable, but I think we are stuck with this, er, lumpy presentation:
USE Count, Country, Station ID, Description, Lat, Long, Coded bits, Land use / Veg codes
162 425 74546001 EMPORIA/FAA AIRPORT 38.33 -96.20 370 357S 26FLxxno-9A10WARM CROPS A1 0
136 425 72248003 EL DORADO/FAA AIRPORT 33.22 -92.80 76 68S 23FLxxno-9A10WARM FOR./FIELD A1 0
251 425 72523001 BRADFORD/FAA AIRPORT 41.80 -78.63 645 625S 10HIxxno-9A12WARM MIXED A1 0
176 425 72445001 VICHY/ROLLA NAT’L ARPT 38.12 -91.77 350 294S 14HIxxno-9A17WARM FIELD WOODSA1 0
219 425 72438009 CRAWFORDSVILLE 5S 39.97 -86.93 232 243S 14FLxxno-9A 5WARM FIELD WOODSA1 12
82 425 72765001 DICKINSON/FAA AIRPORT 46.80 -102.80 787 779S 16FLxxno-9A 6COOL CROPS A1 0
41 403 71832002 PAGWA A,ON 50.03 -85.27 189 187R -9FLxxno-9A-9BOGS, BOG WOODS A1 0
17 403 71120000 COLD LAKE,ALT 54.42 -110.28 541 537R -9FLxxno-9A-9BOGS, BOG WOODS C 49
5 141 68614000 VREDENDAL -31.67 18.50 34 130R -9HIxxCO26A-9COASTAL EDGES B 14
40 403 71810002 HAVRE ST PIERRE,QU 50.25 -63.58 6 8R -9FLxxCO 1A-9COASTAL EDGES C 25
26 403 71897002 BELLA COOLA,BC 52.37 -126.68 18 632R -9MVxxCO 1A-9COOL CONIFER A 0
35 403 71104007 BARKERVILLE,BC 53.07 -121.52 1265 1512R -9MTxxno-9A-9COOL CONIFER A 0
54 403 71104002 BIG CREEK,BC 51.72 -123.03 1128 1183R -9MVxxno-9A-9COOL CONIFER A 0
7 403 71944004 GERMANSEN LANDING,BC 55.78 -124.70 747 957R -9MVxxno-9A-9COOL CONIFER A 0
113 425 72683000 BURNS,OR. 43.58 -118.95 1271 1290R -9HIxxno-9A-9COOL CONIFER A1 0
113 425 72683000 BURNS,OR. 43.58 -118.95 1271 1290R -9HIxxno-9A-9COOL CONIFER A1 0
120 425 72789002 STEHEKIN 4NW 48.35 -120.72 387 1279R -9MVxxno-9A-9COOL CONIFER A1 0
63 403 71894001 TOFINO A,BC 49.08 -125.77 24 39R -9HIxxCO 1A-9COOL CONIFER A1 0
86 425 72772011 WHITE SULPHUR SPRINGS #2 46.52 -110.88 1584 1611R -9MVxxno-9A-9COOL CONIFER A1 0
96 425 74531001 LEADVILLE,CO USA 39.20 -106.30 3062 3043R -9MVxxno-9A-9COOL CONIFER A1 0
4 403 71943010 BEATTON RIVER A,BC 57.38 -121.38 840 817R -9HIxxno-9A-9COOL CONIFER A 9
7 403 71068000 PEACE RIVER,A 56.23 -117.43 571 542R -9HIxxno-9A-9COOL CONIFER B 10
51 403 71883000 BLUE RIVER, B 52.13 -119.30 683 924R -9MVxxno-9A-9COOL CONIFER B 11
70 403 71883001 VAVENBY,BC 51.58 -119.78 445 1155R -9MVxxno-9A-9COOL CONIFER B 11
7 403 71807000 ARGENTIA, NFL 47.30 -54.00 16 21R -9HIxxCO 1A-9COOL CONIFER B 11
76 403 71887002 BARRIERE,BC 51.18 -120.12 415 613R -9MVxxno-9A-9COOL CONIFER B 11
9 403 71940004 BEAVERLODGE CDA,AL 55.20 -119.40 745 739R -9HIxxno-9A-9COOL CONIFER B 14
29 403 71888004 ENTRANCE,AL 53.38 -117.68 991 1100R -9MVxxno-9A-9COOL CONIFER B 8
7 403 71944000 MACKENZIE, B. 55.30 -123.13 690 717R -9MVxxLA-9A-9COOL CONIFER C 15
65 403 71474002 100 MILE HOUSE,BC 51.65 -121.30 1059 1035R -9HIxxno-9A-9COOL CONIFER C 16
69 403 71882006 GOLDEN AIRPORT,BC 51.30 -116.98 785 1061R -9MVxxno-9A-9COOL CONIFER C 22
8 403 71950000 SMITHERS,B.C. 54.82 -127.18 523 642R -9MVxxno-9A-9COOL CONIFER C 25
26 403 71881002 EDSON,AL 53.58 -116.42 923 918R -9HIxxno-9A-9COOL CONIFER C 26
39 403 71878009 LACOMBE CDA,AL 52.47 -113.75 847 866R -9HIxxno-9A-9COOL CONIFER C 32
5 425 70387000 WRANGELL 56.48 -132.37 13 99R -9HIxxCO 1A-9COOL CONIFER C 8
13 312 81202000 NICKERIE 5.95 -57.03 2 1R -9FLxxCO 1A-9COOL CROPS A 0
56 403 71863002 QU’APPELLE,SA 50.52 -103.88 650 653R -9HIxxno-9A-9COOL CROPS A1 0
64 403 71135001 ASSINIBOIA,SA 49.73 -105.97 724 722R -9FLxxno-9A-9COOL CROPS A1 0
89 403 71441001 EMERSON,MA 49.07 -97.20 238 236R -9FLxxno-9A-9COOL CROPS A1 0
41 403 71878007 STETTLER,AL 52.30 -112.70 823 822R -9FLxxno-9A-9COOL CROPS A 8
12 312 81225000 ZANDERIJ 5.45 -55.20 15 30R -9FLxxno-9A-9COOL CROPS B 0
13 307 81002000 TIMEHRI 6.50 -58.25 30 51R -9FLxxno-9A-9COOL CROPS B 0
50 403 71129003 ESTON,SA 51.15 -108.77 680 677R -9FLxxno-9A-9COOL CROPS B 10
45 403 71864004 DAVIDSON,SA 51.27 -105.98 619 617R -9FLxxno-9A-9COOL CROPS B 21
16 211 35542000 IRGIZ 48.62 61.27 114 122R -9FLxxLA-9A-9COOL DESERT A 0
25 301 87774000 MAQUINCHAO -41.25 -68.73 888 880R -9HIDEno-9A-9COOL DESERT A 0
4 301 87880000 GOBERNADOR GR -48.78 -70.17 357 408R -9HIDEno-9A-9COOL DESERT A 0
4 301 87909000 SAN JULIAN AE -49.32 -67.75 62 49R -9HIxxCO 1A-9COOL DESERT A 0
4 301 87912000 SANTA CRUZ AE -50.02 -68.57 111 72R -9HIxxCO10A-9COOL DESERT A 0
87 425 72583002 LOVELOCK/FAA AIRPORT 40.07 -118.55 1188 1220R -9MVDEno-9A-9COOL DESERT A1 0
10 301 87860001 SARMIENTO ARGENTINA -45.60 -69.10 268 284R -9HIxxLA-9A-9COOL DESERT A 34
6 301 87896000 PUERTO DESEAD -47.73 -65.92 80 16R -9FLDECO 4A-9COOL DESERT B 0
11 222 30949001 MANGUT, SOVIET UNION 49.80 112.70 -999 1055R -9HIxxno-9A-9COOL FIELD/WOODSA 0
21 403 71125002 LOON LAKE CDA EPF,SA 54.05 -109.10 543 535R -9FLxxLA-9A-9COOL FIELD/WOODSA 10
42 403 71871001 CORONATION A,AL 52.07 -111.45 791 786R -9FLxxno-9A-9COOL FIELD/WOODSA 9
56 403 71855000 DAUPHIN,MAN. 51.10 -100.05 305 311R -9HIxxno-9A-9COOL FIELD/WOODSB 10
56 403 71855000 DAUPHIN,MAN. 51.10 -100.05 305 311R -9HIxxno-9A-9COOL FIELD/WOODSB 10
48 403 71104000 WILLIAMS LAKE 52.18 -122.05 940 876R -9HIxxno-9A-9COOL FIELD/WOODSB 12
33 403 71865005 HUMBOLDT,SA 52.20 -105.10 567 562R -9FLxxno-9A-9COOL FIELD/WOODSC 11
25 228 48375000 MAE SOT 16.67 98.55 197 450R -9MVxxno-9A-9COOL FOR./FIELD A 0
55 403 71856003 BISSETT,MA 51.03 -95.68 268 281R -9FLxxno-9A-9COOL FOR./FIELD A 9
164 425 72636005 LUDINGTON USA 43.90 -86.40 210 207R -9FLxxLA-9A-9COOL FOR./FIELD B1 7
45 403 71138004 KAMSACK,SA 51.57 -101.90 440 467R -9HIxxno-9A-9COOL FOR./FIELD B 22
42 403 71443000 SWAN RIVER,MA 52.12 -101.23 335 340R -9FLxxno-9A-9COOL FOR./FIELD C 25
4 205 51848001 QIEMO 38.15 85.55 1248 1220R -9FLDEno-9A-9COOL GRASS/SHRUBA 0
107 425 72561002 LE ROY USA 40.50 -103.00 1362 1354R -9FLxxno-9A-9COOL GRASS/SHRUBA1 0
83 425 72677002 LIVINGSTON/FAA AP 45.70 -110.45 1418 1452R -9MVxxno-9A-9COOL GRASS/SHRUBA1 0
89 425 74413005 GLENNS FERRY 42.93 -115.32 765 862R -9FLxxno-9A-9COOL GRASS/SHRUBA1 0
94 425 72564001 PINE BLUFFS 5W 41.17 -104.15 1578 1589R -9FLxxno-9A-9COOL GRASS/SHRUBA1 0
42 403 71510003 BIGGAR,SA 52.07 -107.98 671 669R -9FLxxno-9A-9COOL GRASS/SHRUBC 24
131 425 74433000 MCCOOK, NE. 40.08 -100.65 800 769R -9FLxxno-9A-9COOL IRRIGATED A1 0
95 425 72464002 TRINIDAD/FAA ARPT 37.25 -104.33 1751 1746R -9MVxxno-9A-9COOL IRRIGATED A1 0
34 403 71707003 NORTHEAST MARGAREE,NS 46.33 -60.97 84 205R -9MVxxCO15A-9COOL MIXED A 0
101 425 72570001 MEEKER USA 40.00 -107.90 1935 2113R -9MVxxno-9A-9COOL MIXED A1 0
229 425 72515004 BAINBRIDGE 2E 42.28 -75.45 302 396R -9HIxxno-9A-9COOL MIXED A1 0
246 425 72514004 PHILIPSBURG/MID-STATE AP 40.90 -78.08 586 553R -9HIxxno-9A-9COOL MIXED A1 0
248 425 72520005 DUBOIS/FAA AIRPORT 41.18 -78.90 552 508R -9HIxxno-9A-9COOL MIXED A1 7
22 614 02935000 JYVASKYLA 62.40 25.68 145 136R -9HIxxno-9A-9COOL MIXED B 19
12 118 64550000 MOUILA -1.87 11.02 89 102R -9HIxxno-9A-9EQ. EVERGREEN A 0
13 118 64552000 MITZIC 0.78 11.53 583 541R -9HIxxno-9A-9EQ. EVERGREEN A 0
14 309 84425000 YURIMAGUAS -5.90 -76.08 184 142R -9FLFOno-9A-9EQ. EVERGREEN A 0
17 503 96171001 RENGAT -0.43 102.45 20 54R -9FLMAno-9A-9EQ. EVERGREEN A 0
5 315 81415000 MARIPASOULA 3.63 -54.03 106 268R -9HIxxno-9A-9EQ. EVERGREEN A 0
5 501 94184000 COEN AIRPORT -13.75 143.12 162 218R -9HIxxno-9A-9EQ. EVERGREEN A 0
6 312 81209000 STOELMANSEILA 4.35 -54.42 52 254R -9HIxxno-9A-9EQ. EVERGREEN A 0
9 312 81250000 TAFELBERG 3.78 -56.15 323 613R -9HIxxno-9A-9EQ. EVERGREEN A 0
14 505 96441000 BINTULU 3.20 113.03 5 0R -9FLxxCO 1A-9EQ. EVERGREEN A 9
14 309 84455000 TARAPOTO -6.45 -76.38 282 995R -9HIFOno-9A-9EQ. EVERGREEN B 0
16 309 84444000 CHACHAPOYAS -6.22 -77.83 2435 2126R -9MVxxno-9A-9EQ. EVERGREEN B 0
3 305 80398000 LETICIA/VASQU -4.17 -69.95 84 74R -9FLFOno-9A-9EQ. EVERGREEN B 0
8 303 82106000 SAO GABRIEL D -0.13 -67.08 90 65R -9HIFOno-9A-9EQ. EVERGREEN B 0
12 113 65557000 GAGNOA 6.13 -5.95 210 197R -9FLFOno-9A-9EQ. EVERGREEN B 14
14 118 64556000 MAKOKOU 0.57 12.87 515 239R -9HIxxno-9A-9EQ. EVERGREEN C 0
7 222 30554000 BAGDARIN 54.47 113.58 903 1206R -9HIFOno-9A-9E. SOUTH. TAIGA A 7
10 501 94659000 WOOMERA AEROD -31.13 136.82 167 164R -9FLDEno-9A-9HIGHLAND SHRUB A 0
1 501 94494000 QUILPIE -26.62 144.27 198 231R -9HIxxno-9A-9HIGHLAND SHRUB A 0
1 501 94510000 CHARLEVILLE A -26.40 146.27 304 295R -9HIxxno-9A-9HIGHLAND SHRUB A 0
17 117 63533000 NEGHELLI 5.28 39.75 1455 1558R -9FLxxno-9A-9HIGHLAND SHRUB A 0
27 414 76258004 TONICHI, SONORA 28.60 -109.57 200 335R -9MVxxno-9A-9HIGHLAND SHRUB A1 0
91 425 72470001 HANKSVILLE 38.37 -110.72 1313 1358R -9HIDEno-9A-9HIGHLAND SHRUB A1 0
2 501 94430000 MEEKATHARRA A -26.60 118.53 518 503R -9HIDEno-9A-9HIGHLAND SHRUB B 0
2 501 94300000 CARNARVON AIR -24.87 113.67 7 2R -9FLxxCO 1A-9HIGHLAND SHRUB C 8
10 104 68024001 GHANZI -21.50 21.75 1131 1096R -9FLDEno-9A-9HOT DESERT A 0
14 141 68624000 FRASERBURG -31.92 21.52 1300 1261R -9HIDEno-9A-9HOT DESERT A 0
1 501 94232000 VICTORIA RIVE -16.40 131.00 82 127R -9HIDEno-9A-9HOT DESERT A 0
1 501 94324000 YUENDUMU -22.25 131.78 668 664R -9HIDEno-9A-9HOT DESERT A 0
15 101 60611000 IN AMENAS 28.05 9.63 562 566R -9HIDEno-9A-9HOT DESERT A 0
36 101 60581000 HASSI-MESSOUD 31.70 2.90 398 630R -9FLDEno-9A-9HOT DESERT A 0
38 115 62459000 EL TOR 28.23 32.62 14 51R -9HIxxCO 1A-9HOT DESERT A 0
6 101 60630001 OUALLEN ALGERIA 24.60 1.30 347 316R -9HIDEno-9A-9HOT DESERT A 0
6 127 61202000 TESSALIT 20.20 0.98 491 545R -9HIDEno-9A-9HOT DESERT A 0
7 128 61450000 TIDJIKJA 18.57 -11.43 402 230R -9FLDEno-9A-9HOT DESERT A 0
8 128 61401000 BIR MOGHREIN 25.23 -11.62 360 393R -9HIDEno-9A-9HOT DESERT A 0
21 124 62131000 HON 29.12 15.95 267 256R -9HIDEno-9A-9HOT DESERT A 25
25 219 41712000 DAL BANDIN 28.88 64.40 850 1134R -9HIDEno-9A-9HOT DESERT A 5
1 501 94238002 TENNANT CREEK -19.63 134.17 376 349R -9HIDEno-9A-9HOT DESERT B 0
15 104 68032000 MAUN -19.98 23.42 900 917R -9FLDEno-9A-9HOT DESERT C 10
20 101 60630002 AOULEF ALGERIA 27.00 1.10 290 266R -9FLDEno-9A-9HOT DESERT C 10
2 218 41314000 THUMRAIT 17.67 54.03 467 493R -9HIDEno-9A-9HOT DESERT C 10
55 223 40356000 TURAIF 31.68 38.73 852 816R -9FLDEno-9A-9HOT DESERT C 17
38 130 60265000 OUARZAZATE 30.93 -6.90 1140 1154R -9HIDEno-9A-9HOT DESERT C 28
8 124 62271000 KUFRA 24.22 23.30 436 406R -9FLDEno-9A-9HOT DESERT C 35
3 403 71964006 BURWASH A,YT 61.37 -139.05 799 989R -9MVxxLA-9A-9ICE A 0
5 425 70296000 CORDOVA/MILE 60.50 -145.50 13 245R -9FLxxCO10A-9ICE A 0
1 222 24817000 ERBOGACEN 61.27 108.02 291 240R -9HIxxno-9A-9MAIN TAIGA A 0
13 638 22583000 KOJNAS 64.75 47.65 64 135R -9FLxxno-9A-9MAIN TAIGA A 0
14 222 23724000 NJAKSIMVOL’ 62.43 60.87 51 150R -9FLxxno-9A-9MAIN TAIGA A 0
2 403 71953000 WATSON LAKE,Y 60.12 -128.82 690 746R -9HIxxLA-9A-9MAIN TAIGA A 0
2 403 71958004 CASSIAR,BC 59.28 -129.83 1077 1496R -9MVxxno-9A-9MAIN TAIGA A 0
3 222 31137000 TOKO 56.28 131.13 850 890R -9HIxxno-9A-9MAIN TAIGA A 0
3 425 70174000 BETTLES 66.92 -151.52 196 288R -9HIxxno-9A-9MAIN TAIGA A 0
3 425 70194001 FT YUKON UNITED 66.58 -145.08 129 120R -9FLMAno-9A-9MAIN TAIGA A 0
4 222 24908000 VANAVARA 60.33 102.27 260 248R -9HIxxno-9A-9MAIN TAIGA A 0
7 222 30372000 CARA 56.90 118.27 711 845R -9MVxxno-9A-9MAIN TAIGA A 0
9 222 23405000 UST’-CIL’MA 65.43 52.27 68 60R -9FLxxno-9A-9MAIN TAIGA A 0
9 614 02836000 SODANKYLA 67.37 26.65 179 191R -9FLFOno-9A-9MAIN TAIGA A 0
66 403 71728005 PASSE DANGEREUSE DAM,QU 49.88 -71.27 457 501R -9HIxxLA-9A-9MAIN TAIGA A1 0
31 403 71845000 PICKLE LAKE,O 51.45 -90.20 386 376R -9FLxxLA-9A-9MAIN TAIGA A 7
2 222 24966000 UST’-MAJA 60.38 134.45 170 152R -9HIxxno-9A-9MAIN TAIGA B 0
28 403 71813000 NATASHQUAN,QU 50.18 -61.82 11 12R -9FLxxCO 1A-9MAIN TAIGA B 0
5 425 70260001 NENANA/MUNICIPAL AIRPORT 64.55 -149.08 109 112R -9HIxxno-9A-9MAIN TAIGA B 0
7 222 23891000 BAJKIT 61.67 96.37 262 441R -9MVxxno-9A-9MAIN TAIGA B 0
2 403 71078000 LYNN LAKE, MA 56.87 -101.08 357 360R -9FLxxno-9A-9MAIN TAIGA B 11
5 403 71958000 DEASE LAKE B. 58.42 -130.00 816 950R -9MVxxLA-9A-9MAIN TAIGA B 11
14 403 71141001 NORWAY HOUSE,MA 53.98 -97.83 217 220R -9FLxxLA-9A-9MAIN TAIGA B 12
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1 425 70133000 KOTZEBUE, RAL 66.87 -162.63 5 0R -9FLMACO 1A-9WATER A 10
211 425 72537001 PELEE ISLAND,ON 41.75 -82.68 175 171R -9FLxxLA-9A-9WATER A1 0
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38 303 83997000 ST.VITORIA DO -33.52 -53.35 24 0R -9FLxxCO18A-9WATER B 24
5 636 08515000 SANTA MARIA ( 36.97 -25.17 100 0R -9HIxxCO 1A-9WATER B 8
9 634 01241000 ORLAND III 63.70 9.60 7 9R -9HIxxCO 1A-9WATER B 8
79 623 16332001 PALASCIA AERO 40.10 18.50 86 47R -9HIxxCO 5A-9WATER B 9
3 521 91765000 PAGO PAGO/INT -14.33 -170.72 3 0R -9HIxxCO 1A-9WATER C 0
49 210 47678000 HACHIJOJIMA 33.10 139.78 80 0R -9MVxxCO 2A-9WATER C 0
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2 620 04018000 KEFLAVIKURFLU 63.97 -22.60 54 6R -9HIxxCO 3A-9WATER C 13
4 636 08509000 LAJES (ACORES 38.77 -27.10 54 0R -9MVxxCO 1A-9WATER C 13
13 429 78384000 OWEN ROBERTS 19.28 -81.35 3 0R -9FLxxCO 1A-9WATER C 17
94 623 16245000 PRATICA DI MA 41.65 12.45 21 29R -9FLxxCO 1A-9WATER C 18
7 434 78866000 JULIANA AIRPO 18.05 -63.12 9 24R -9HIxxCO 1A-9WATER C 22
13 305 80001000 SAN ANDRES (I 12.58 -81.72 6 0R -9FLxxCO 1A-9WATER C 23
1 425 70200000 NOME 64.50 -165.43 7 48R -9HIxxCO 1A-9WATER C 23
11 205 59948000 YAXIAN 18.23 109.52 7 48R -9HIxxCO 1A-9WATER C 29
5 425 70259000 KENAI 60.57 -151.25 29 0R -9FLxxCO 2A-9WATER C 43
4 218 41288000 MASIRAH 20.67 58.90 19 0R -9HIDECO 1A-9WATER C 8
2 218 41316000 SALALAH 17.03 54.08 20 185R -9HIDECO 2A-9WATER C 84
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5 425 70178000 TANANA 65.17 -152.10 67 452R -9HIxxno-9A-9WOODED TUNDRA A 0
5 425 70264002 MCKINLEY PARK 63.72 -148.97 631 870R -9MVxxno-9A-9WOODED TUNDRA A 0
2 403 71902000 NAIN,NFLD 56.55 -61.68 7 53R -9MVxxCO 2A-9WOODED TUNDRA A 10
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Musings from the Chiefio 
I’m stunned at this.
I’ve been doing a little manual checking. I recoginesed that many airports have dual names – e.g London- Gatwick so I took some European records from your Top 3000. Some of these were familar to me, some not. I only started on the dual names.
Of the 105 listed (Geneve, Switzerland to Palermo, Italy) 24 at least are airports. I note none of the names are in your list of 500 above. I’ll send you the list.
@Ellie:
Thanks! I look forward to the list.
Now that I’ve learned the “trick” of pulling “airstn” records out of different points in GIStemp (where it has a GHCN station ID available) I suspect I’ll be doing some more profiles. Things like “Percent of Airports by Quartile of Age”…
For now, I’m with you. A bit stunned. I think it’s time for a nice cup of Earl Grey (I have an interesting one from Ceylon / Sri Lanka that I get at the local “mediterranean food shop” that is quite good. “Zarrin” is the brand. Oddly, the ingredients are listed in English, French, and Russian… )
At any rate: Time for a cup ‘o tea and a bit of a think…
You are doing some fantastic work. This has me gripped like a really good detective novel – I can’t wait to see how it plays out. I’m especially interested to understand how it deals with UHI over and above the ‘rural reference’ you have just described above. I understand from other websites that the historic (individual) record is warmed in some way, which is justified as GIStemp deals with anomaly rather than absolute temperature.
Well, end of the day for me and I’m on wine not tea tonight. Check your email!
@Ellie
Well, it’s still early afternoon here, a bit early for me to justify wine… but later ;-)
OK, I’ll check the email.
GIStemp does the rather exotic thing of adjusting by changing the past, rather than the present. So, for example, if London where found to be 2 C warmer today, rather than subtracting 2C from the present temp, it would in theory add 2C to the past so the slope of temperature over time would be “right”.
This, as I’m sure you can see, has a lot of “issues”. They assert that this is OK since in the end they just deal with anomalies, but in reality, it’s just wrong. The use of airports as a “rural reference” makes it even worse. This may, for example, explain things like the 1.75C drop of the record for PISA in the past. If a nearby airport was warming a lot, but used as a rural reference, it might result in a “negative UHI” which would then cause the past of PISA to be made colder (rather than warmer) which is what we see in the adjustment to the PISA record. At any rate, the exact mechanism is still speculative for PISA (as I’m not done yet…) But I’ll get there soon enough.
Basically, GIStemp looks at a bunch of “nearby records” to figure out what was the relationship of a particular urban station to various rural stations over time. It then uses that relationship to change the past of the Urban station. This is clearly broken in several cases, such as when the “rural” station is not rural, when it is a growing airport, when the “change” at one was only during the 20 year or so overlap or comparison dates, but is applied to 100 years of past history, etc. IMHO, it’s the most likely broken method in GIStemp (though also IMHO, The March of the Thermometers causes most of the bogus “warming”).
One example: If a TOBS or Equipment change in one site in 1990 caused it to move by 2 C relative to it’s neighbors, ALL of the past history of that site would be adjusted by 2C to “fix” it. Exactly what an equipment change in 1990 has to do with a reading taken in 1890 is, well, “the issue”… Why change 100 years of history to fix 19 years of present offset is also an “issue”…
But as I’ve said elsewhere, my opinion is not what matters (other than how it guides my investigation); what matters is the application of clean benchmarks, what the data have to say, and what the code does.
And so far those things are saying that GIStemp is not to be trusted and does a poor job of making a reliable temperature record and anomaly maps.
Just looked at some of the sites in Australia. Some interesting vegetation classifications there.
Roma PO was closed in 1992, now Roma airport
See
http://www.bom.gov.au/climate/averages/
if you need more info for Australian sites
Ian, thank for the pointer to the info source!
On my “dig here” or “someday” list is to go through these 500 sites and figure out just how many of them are nice grass fields in someone’s back 40 and how many are small semi-urban airports with a history of growth / transformation from grass field in, oh, 1920 to tarmac and concrete regional biz jet port today.
But I don’t have the time right now… The anecdotal reports from folks like you and Ellie are very helpful for characterizing how a big a problem it really is. Basically, when someone says “Wow, I know that site. It’s not rural.”, that raises the “urgency count” on looking at the whole list and making a real statistic out of it.
I’d expect Australia to be a prime candidate for that kind of analysis, given the growth of the country in the last 100 years and the use of airplanes to get around the outback.
Per the vegetation: Yeah. I’ve noticed that too. The descriptions can be a bit, er, “imaginative” at times 8-) I think my favorite so far is LONDON / GATWIK (not on this list, but on the “by quartile” list) as “warm crops”… Ya think?! And that other airport (who’s name escapes me at the moment) that is CONIFER forest. Good luck landing your plane there! Oh, you mean it’s really a tarmac / concrete airport? Oh… ;-0
The thing that surprises me most in all this, is that just when I think I’ve found “the issue” and that the rest of GHCN and GIStemp will probably be uninteresting, I look at the next bit of code and find another thing. Like this one. There seems to be no end to the strange, questionable, and sometimes just downright bogus things in our “temperature record”…
E.M. Smith, you are amazing. You may start a whole new variety of citizen scientist looking aghast at what the “public servant” scientists are claiming. (We taxpayers are footing their humungous salaries and, at some point, we will be paying their healthy pensions too.)
Your efforts and personal experience seem somewhat like what Anthony Watts must have felt when he looked at the siting of official surface stations for gathering temperature and said WTF! Feeling stunned is the least of it anymore. I don’t have many more “stuns” left in me. Fleeced is more like it. I’m hoping you might be willing to notice some purposefulness for quite a number of years to this charade of “CO2 AGW”.
More power to you and all those who can help you. Your Renaissance mind along with your technological expertise will develop some mighty truths. Anthony found many excellent willing assistants; you will too. If you ever find something for a non-scientist and a technologically challenged person to do, give me a call.
A thought after my post above – have these rural sites in Australia been used to correct other sites?
And, if so, where and why the hell.
Charleville might also be interesting. Post office site was down town close to the river and ran till 1959, so the record overlaps the airport one. The airport was part of the QANTAS initial network about 1922.
The met station there started in 1942 , when Charleville was a major air force base – 3 runways, bodaceous hangers, roads etc and avgas aplenty. Now down to one of the hangars, and one runway. It’s not built out as yet.
But from about 1950 has seen a large increase in woody vegetation cover in its vicinity. This does interesting things to headwinds and engine temperatures when you’re trucking – I’m not sure about met readings on the edges though.
@pyromancer76: BLUSH! Thank you! I just think of it as returning to what Science WAS about 1800… (and what I always thought it was before I went off to college and found out about science as done “professionally” i.e. for money.)
Yes, it is “purposeful” (but I wish I could spend more time being “purposeful” on something involving a nice beach and cold beer ;-) instead of a desk and living room view of a slightly untended garden calling my name…
The biggest “burr under my saddle” about all this is rather like what you described. Here I am, some “nobody”, with no money and a box that started life as a 486 some decades ago; and it’s up to ME to do this basic Quality Control work? With all the $Billions being shoveled at what is called “science” research into AGW? Yes, a certain amount of resentment comes from that. Every so often I think of what good I could do for the world if the present GIStemp head were tossed out and I was in his chair. Then I look at the “crud” that is GIStemp and grit my teeth.
Guess I just don’t have what it takes to be a petty government bureaucrat scientist, I’m encumbered by honesty, integrity, precision, efficiency, thoroughness, …
Per the non-science bit: Feel free to take any tabular data you find here, make a pretty chart of it, post it somewhere and say “Look at this! Thermometers by year march south!” or whatever and just somewhere put a link that says “Original data and research from this site” I’m not all that good at the graphics presentation stuff and frankly need to focus my time in the “battle of the bits” at the code level.
Also feel free to just “google map” or “yahoo map’ or whatever some of the the stations and whenever you find a nice big juicy tarmac jungle of a station, put up a posting somewhere that says “Golly, THIS is a rural reference station?” and feel free to use an article here as your reference material for asserting it is used as a ‘reference station’.
If you don’t have any place to put postings, well, WordPress made it real easy for me. Just hit the wordpress.com tag under “Meta” on the side of the blog and sign up. It was free and relatively painless. (You mostly have to click boxes to select things. Typing in a posting is a little more complicated than typing in a comment, but not much! Mostly just that it lets you choose to see the HTML or see it as straight text.
Basically, I see my job as “down in the bits” and that doesn’t leave much time for “marketing” and “packaging” what I find. So feel free to do what you like to do and feel free to use this site as a resource / reference.
@Ian Beale
A thought after my post above – have these rural sites in Australia been used to correct other
The first digit is the number of times that site was used to correct other records. By definition, if it’s on this list it was used to “correct” some other site. The number tells you how many. It’s a bit hard to see, WordPress stripping some formatting in this view, but the very first number is the “use count” then a space, then the “country code” for three digits (so all of Australia ought to be 501 if ROMA PO is to be believed). And that ROMA record says it was used 12 times.
Putting 501 in the search box of the blog page says it shows up 39 times (but some of them may be embedded in other numbers like the “Station ID”). Many did show up in the country code position in a quick glance.
So (with a bit of “noise” from other numbers with 501 in them) you ought to be able to rapidly look at the Aussy stations and either say “no worries, bit ‘o dirt in the outback” or “WTF, that’s a bloody 1/2 mile of black tarmac near Sidney!”
I can’t do that (too many for me and) I don’t know your turf. But you do.
So anyone can rapidly “add value” just by picking out the stations from your country, or any location, that you know are not rural and flagging them as a “dig here issue”.
Happy Digging!
(Wonder if my “logo” ought to change to a “Spuds” dog digging a hole with a bone end sticking out ;-) dirt flying… Or maybe a bull dog… Or a Mastiff … Or a Badger :-)
Well, here’s a nice “rural” airport for you: Leadville, CO.
I did a “search” with the browser search box for “425” the US country code, then looked at the names to see what I recognized. I’d seen shows about the mining history of Leadville. A quick “google” of “Leadville Airport” turned up their page, with photo gallery.
I particularly like this picture of a C130 on the tarmac…
http://www.leadvilleairport.com/pages/05_leadville_airport_photos/pages/M_C-130_a06.html
The whole photo gallery is here:
http://www.leadvilleairport.com/pages/05_leadville_airport_photos/leadville_airport_photos.html
It does look generally like an airport of modest size. Highest in the nation, they say, so probably cold a lot of the time. But I do have to think that keeping the runways cleared of snow will tend to make it a bit warmer than the areas around it (trees and snow much of the year…)
The main page is nice:
http://www.leadvilleairport.com/
Especially the picture of the Biz Jet on the runway. Citation X? 7 rows of passenger windows… Windsock in the background right next to the runway…
Eielson Field sounded familiar too:
http://www.city-data.com/airports/Eielson-Afb-Airport-Fairbanks-Alaska.html
Looks like it’s military. They would never fly big planes out of a military field, would they?…
Wow! The overhead view on wiki makes it look big, and warm:
http://en.wikipedia.org/wiki/Eielson_Air_Force_Base
Hmmm… Gulkana is also in Alaska (as are several others). It’s a few miles outside of town. I’m sure all that bare dirt and tarmac is just as cold as permafrost and snow.
http://en.wikipedia.org/wiki/Gulkana_Airport
Lets see, isn’t the arctic supposed to be warming much faster than further south? Perhaps this is due to their having a higher ratio of airports to non-airport thermometers?… Just Thinking…
Hey, Bethel too: (BET is the airport code)
http://en.wikipedia.org/wiki/Bethel_Airport
Touted as a regional transportation center…
http://www.farecompare.com/flights/Bethel-BET/city.html
says the largest plane is a 737 and with a total of 474 domestic flights a week (though most of those will be smaller aircraft 467 if I read it right). Still, its a commercial airport with all that implies.
OOOhhh:
186 425 72405002 QUANTICO/MCAS 38.50 -77.30 4 22R -9FLxxno-9A-9WARM FOR./FIELD B1 10
Quantico! Marine Corps Air Station! Gotta Love It! Semper Fi!
http://www.globalsecurity.org/military/facility/mcb-quantico.htm
Calls it the “Crossroads of the Marine Corp” and says it’s 100 sq. miles on the Potomac.
Heck, it’s even surrounded by water and those Marines, they don’t even turn lights on at night at airports when they are “at work”… Think how cool (and Green!) that must be! ;-) Go Green! (Especially cammo green 8-)
http://www.quantico.usmc.mil/map_view.htm
Hey, they even have a golf course “near by”!
Not to be outdone (too much ;-) we have the Navy missile test range at Barking Sands Hawaii.
http://www.airnav.com/airport/PHBK
Wonder how it does when they burn off the sugar cane fields behind it? (Last time I was there, that “black” area in the picture was green sugar cane…)
Not like the hardware they “fly” out of there is hot:
http://www.globalsecurity.org/military/facility/pmrf.htm
Rockets & thermometers… Gotta love it!
Now this is just silly. THE main airport on Kauai… My spouse and I flew into that airport on our honeymoon a couple of decades plus a few years ago. It was not exactly a small rural place.
5 425 91165000 LIHUE, KAUAI, 21.98 -159.35 45 86R -9MVxxCO 1A-9WARM FOR./FIELD C 21
http://hawaii.gov/lih
AND it was growing like a weed around there. Turning from a sleepy little backwater island into a major hotels on the beach primary destination…
http://hawaii.gov/lih/airport-information
From Wiki:
http://en.wikipedia.org/wiki/Lihue_Airport
For the 12-month period ending May 31, 2005, the airport had 104,276 aircraft operations, an average of 285 per day: 48% air taxi, 26% scheduled commercial, 23% general aviation and 2% military. At that time there were 28 aircraft based at this airport: 43% single-engine, 7% multi-engine and 50% helicopter.[1]
Hey, looks like Molokai and Maui too!
6 425 91186000 MOLOKAI, MOLO 21.15 -157.10 137 0R -9HIxxCO 5A-9WATER A 0
6 425 91196001 HANA, MAUI HAWAII 20.80 -156.00 37 0R -9MVxxCO 1A-9WATER A 0
http://www.hawaiiweb.com/molokai/html/sites/molokai_airport.html
Wonder if that black stuff behind the terminal in the top picture is black lava or burned off cane? Nice choices, eh?
http://hawaii.gov/mkk/airport-information
Hana is small, but with a nice black runway and a full layer of tarmac around the terminal. Wonder where the thermometer is…
http://hawaii.gov/hnm/airport-information
I’ve got to take another Earl Grey break. This Lihue thing has put me “over the top” again and I need to calm the nerves…
They do appear to be coastal
http://www.alxd.gr/AirportGuide/limnos/limnosE.html
http://www.broomeair.com.au/
http://www.flightstats.com/go/Airport/airportDetails.do?airportCode=BQN
I dumped the list into Open Office and sorted on “501”.
There are 35 that look like Australia.
A lot are rural, and FYI “GUNBALUNYA” is “Oenpelli” in the BOM listings
But some of the sites with high number of use in correction seem suspect on being rural these days. e.g. Richmond AMO is a major airforce base,
But I’m not familiar with SE Australia where a lot of these are located.
I’d be curious about the 12 places(?) that Roma PO was used in correction though, versus the 1 each for Charleville and Quilpie
Ian,
Nice info. I’ll take a look in the code and see if there’s a quick way to get out “what station was changed by which”…
Whats a BOM? Bill of Materials?
Mr. Beale makes a good point. It’s not so much the number of airports in total, as it is the number that were used a lot in correcting other records. Sorting the whole list for that variable first may be a good way to reduce the number needed for your analysis (I don’t know how to get a good fix on “a lot’). The others, at worst, would just represent poor siting and would fit Anthony Watt’s thesis better—-at least for those in the U.S.
This is done via looking for “nearby” (up to 1000 km away) “rural” stations.
The South of France being “nearby” Manchester UK then? The locals (of Manchester not the SOF) will be delighted.
Speaking of Manchester UK
(http://en.wikipedia.org/wiki/Manchester_Airport)
…By 1958 the airport was handling 500,000 passengers annually……..In 2008 21.2 million passengers used Manchester Airport.
Just eyeballing GISS (data set 1) for Manchester Airport it has seen some 1.5+ degC of “warming” since the 1950’s. This is presumably one of those locations that skew the “100 year” trend upward after 1950!
(sorry if this turns up posted twice)
Speaking of Manchester UK
Just eyeballing GISS (data set 1) for Manchester Airport it looks to have seen some 1.5+ degC of “warming” since the 1950’s. This is presumably one of those locations that skew the “100 year” trend upward after 1950!
(http://en.wikipedia.org/wiki/Manchester_Airport)
…By 1958 the airport was handling 500,000 passengers annually……..In 2008 21.2 million passengers used Manchester Airport.
Well, I looked at a few names that jumped out at me:
20 621 03962000 SHANNON AIRPO 52.70 -8.92 20 14R -9FLxxCO 1A-9WARM CROPS B 12
Shannon Airport, Ireland, Ireland’s third Airport ~3M passengers per year. Surrounded by grassland/rough grazing. Close to sea.
42 651 03302000 VALLEY 53.25 -4.53 11 33R -9HIxxCO 1A-9WARM CROPS B 7
RAF Valley – Air Force Base, Wales, UK. Surrounded by grassland/rough grazing. Close to sea.
39 651 03257000 LEEMING 54.30 -1.53 40 42R -9HIxxno-9A-9WARM CROPS B 9
RAF Leeming Airforce Base, Yorkshire, England
Surrounded by grassland, pasture and arable crops. Inland.
87 623 16066000 MILANO/MALPEN 45.62 8.73 211 193R -9HIxxno-9A-9WARM FOR./FIELD C 16
Milano Malpensa Airport is Milan’s largest airport. It is located 24.83 miles northwest of central Milan, Italy. It is one of 3 airports in the Milan metropolitan area.
Yes it is rural but Malpensa handled over 23.8 million passengers in 2007.
Bingo! – significant UHI (IMHO).
11 651 03026000 STORNOWAY 58.22 -6.32 13 2R -9FLxxCO 1A-9WARM GRASS/SHRUBB 7
Small regional airport – Isle of Lewis, Outer Hebrides, Scotland – not a problem.
2 620 04018000 KEFLAVIKURFLU 63.97 -22.60 54 6R -9HIxxCO 3A-9WATER C 13
Keflavík International Airport is the largest airport in Iceland The airport has two runways, ~2 Million passengers per year (2008).
And now my favourites (which I happened to notice had similar latitude/lomgitude to Keflavik.
2 620 04048000 VESTMANNAEYJA 63.40 -20.28 124 0R -9HIxxCO 1A-9WATER A 0
2 620 04065000 GRIMSEY 66.53 -18.02 16 0R -9FLxxCO 1A-9WATER A 0
These are both tiny airstrips on very small islands off the SW and NW coasts of Iceland respectively. So small they don’t even look inhabited.
Now my probelem with all three in Iceland is – if they are used for correction – where the hell are they being used to correct? Somewhere in the UK?
BOM = Australian Bureau of Meterology
(As in the link to station info I posted above)
@Ian
I’d just clicked on the link, but not looked closely at the text of the URL… and ASSUMED that the Aussy BOM would be ABM or something with an A in front. But that’s the conundrum we all face, isn’t it? Go “fast and light” and miss some detail, or go “slow and deep” and not get very far. Oh well, thanks for telling me what I ought to have seen if I’d been going just a little slower…
@Ellie
Well, just put a 1000 km radius around Iceland… But, yeah, it does make you wonder just what in the heck is being “adjusted”. Guess I know where my next “Dig Here” sign goes! A table of just who “does” whom will be rather large (several thousand records, by rough guess: 2k reference stations, 10 ish average uses? 20K A “does” B…) so I’ll need to think of some way to cut down the raw result to a “postable” conclusion… Hmmm… There are only 7k thermometer records by the time you reach this step. And if 2k are doing the adjusting… 5K to be adjusted…
I’ll bet it’s that segment thing!
In the code, a “missing segment” can be filled in, but is not always. So, for a given “urban” station, one decade might look to Iceland AIRPORT A then another decade (when A was not yet in existence) might look at Scotland…
So… 5K of station IDs, with a list, by decade, of ‘Used For “Correcton” IDs’ might work. Still a long list, but might be manageable…
Still, the notion of taking the most bizarre mid ocean incredibly volcanic up their eyeballs in hot water and bare rocks islands and using to correct, oh, Scotland, does seem a bit daft.
@3×2
For some reason WordPress dumped the Manchester comments into the SPAM queue. No idea why… It “has it’s own rules”…
Yes, SOF and Manchester are “nearby”… In fact (using thumb on globe, so may need “refinement” ;-) it looks like Spain is “near” London and Wales! Who Knew? And Rome is practically IN Tunisia…
Maybe check this – just noticed that Nain Canada and Nain Nfld are pretty much the same lats and longs, both listed as wooded tundra.
Looks pretty much like either side of a bay on Google Earth
I wonder what 2 records the NAIN records are modifying?… Any big city near that bay?
Gibraltar North Front is an airport site.
Picture of the weather station on runway is here, just above the plane landing:
The weather station moved to the airfield in 1947, then to the runway in 1955.
More details in here:
http://www.omm.urv.cat/MEDARE/docs/Proceedings_MEDARE.pdf (see Table 1 on page 33).
I was there on holiday in July, and the site is also very close to the sea.
BobR.
Re BOM above – you deal with the code and we’ll help on the fringes!
If you ever needed more info on the airport sites in western Queensland I can probably quiz a pilot friend that uses a lot of them. A questions list would help if we get to that.
Malpensa only became a major airport sometime between 1986 and 1990. I don’t recall exactly when.
Mr. Smith, there is another bias that you might look at. In much of the NH, especially the USA there are several to many sites in a grid box, so that UHI can be diluted by unchanged rural sites. In the SH, especially South America and Africa, many grid boxes have only one site, and in many of these cases, at least for South America, there has been major shifts from small town to small city in the last 30-40 years. I suspect these cases would carry disproportionate weight in a global average. So the shift from cool to warm, and rural to AHI is augmented by SH shift from rural to urban. I don’t have the smarts to contribute to your analysis, but this is another area you might be able to examine. Murray
@Murry
Thanks for the idea, yup, worth looking at. Looked to me like you had “the smarts”!
BTW, on of the things the “warmers” like to do is assert that the anomaly grids and boxes will “fix” the bias in the data. Well, so far, GIStemp has been an amplifier (up to STEP2, the third step…) I like to point out your counter example.
As new thermometers arrive in the S.H., there is high probability that something will end up on an empty grid cell… Voila, instant hot cell..
64 403 71135001 ASSINIBOIA,SA 49.73 -105.97 724 722R -9FLxxno-9A-9COOL CROPS A1 0
The Assiniboia station is here on Google maps.
http://tinyurl.com/y9nk9zq
It’s an old WWII air force training base.
I live in Saskatchewan and have a fairly high provincial blog readership. I could probably get info on the precise location of some of those rural airport stations listed.
@Kate
I have a guest post at the moment looking at GHCN data and examples of adjustments that seem to be unjustified in their magnitude (either increasing cooling or warming). The two worst in each category are rural Canadian Airports, as are many others that suffer strange adjustments.
http://diggingintheclay.blogspot.com/2009/12/physically-unjustifiable-noaa-ghcn.html
We’re starting to dig a bit deeper and local knowledge would certainly be valuable.
Hey Cheif;
If you are in the mood to post the ‘right-truncated’ version of this (similar to the way you did the quartiles), I could import it and make it mappable for you. This would be Point Features.
With the way the text is now posted, I can’t import it as delimited nor as fixed-field data in Excel. This is my main tool for massaging the text into a mappable file.
A bigger question is, how hard would it be to cause the UHI-adjusting step to spit out the LAT/LON of the stations used to adjust on the same line as the lat/lon of the adjusted station?
This could be drawn as arrows (Line features) showing how each station was influenced by another.
So, I’m happy to start with the data you already have, if you would be so kind as to repost it in the ‘fixed-field’ format.
At this juncture I don’t seem to care much about the Topo data, etc. , so you could just truncate them.
Another approach is to just email me the CSV file to minimize the WP-munge factor…
TIA
AC (was/is RR and/or TL)
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