What happens when high cold temps are missing?

At 6581 feet in Hawaii it's 140F?

At 6581 feet in Hawaii it’s 140F?

I’m not so worried that this thermometer is “way wrong”, as it will be removed by the QA process as an insane value. I am concerned that the removal of such ‘high cold places’ from the area will bias the homoginizing process. From where will that mountain top be filled in? Hmmm?

From: http://www.wrh.noaa.gov/zoa/temperature.php?map=hawaii&limit=2

reached from (very low to load with lots of data points):
http://www.wrh.noaa.gov/zoa/temperature.php?map=usa

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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 NCDC - GHCN Issues and tagged , , . Bookmark the permalink.

23 Responses to What happens when high cold temps are missing?

  1. E.M.Smith says:

    @Adrian Vance:

    When a temperature is missing, it is recreated via homogenizing in many of the data sets. This value will be tossed as 140 F is a way high value (maybe it’s near some lava…) then be filled in later with {something} from {somewhere}. I’m just pointing that out and wondering what value it might get… and what that does to the ‘grid cell’ average…

    I’ve added links to the original, and you can put your cursor over a temp on the map and it pops up the detail as one of those boxes (name, elevation, time, temp, wind, etc.)

  2. Ian W says:

    This problem reared its ugly head sometime ago as a Willis about Kathmandu Nepal post on WUWT see:
    http://wattsupwiththat.com/2010/08/11/more-gunsmoke-this-time-in-nepal/

    See update 1 and a linked page at http://rankexploits.com/musings/2010/dog-days-in-nepal/

    Not precisely on point – but homogenization of temperatures from lowlands to Nepalese mountains nevertheless.

  3. LG says:

    More info Here : http://www.wrh.noaa.gov/zoa/getobext.php?sid=NENH1

    Readings labeled as “Suspect”.
    Time Temp. Dew Relative Wind Wind Station Solar Solar Precip Precip Precip Precip Quality
    Point Humidity Direction Speed Pressure Radiation Pct Accumulated 1 hour 6 hour 24 hour Control
    (HST) (f) (f) (%) (mph) (W/m*m) of psbl (inches) (inches) (inches) (inches)
    19 Nov 7:23 am HST 140 140 99 NE 7G17 806.98 4 58% 2.72 Suspect
    19 Nov 6:23 am HST 140 133 83 NE 4G09 806.30 0 — 2.72 Suspect
    19 Nov 5:23 am HST 140 140 100 NE 7G15 806.30 0 — 2.72 Suspect
    19 Nov 4:23 am HST 140 140 100 ENE 7G17 806.30 0 — 2.72 Suspect
    19 Nov 3:23 am HST 140 140 100 ENE 6G13 806.30 0 — 2.72 Suspect
    19 Nov 2:23 am HST 140 140 100 ENE 5G12 806.64 0 — 2.72 Suspect
    19 Nov 1:23 am HST 140 140 100 ENE 6G13 806.98 0 — 2.72 Suspect
    19 Nov 12:23 am HST 140 138 95 NE 4G10 806.98 0 — 2.72 Suspect
    18 Nov 11:23 pm HST 140 139 97 NE 5G11 807.32 0 — 2.72 Suspect
    18 Nov 10:23 pm HST 140 133 83 NE 7G12 807.65 0 — 2.72 Suspect
    18 Nov 9:23 pm HST 140 137 93 ENE 7G14 807.32 0 — 2.72 Suspect
    18 Nov 8:23 pm HST 140 140 100 NE 5G11 806.64 0 — 2.72 Suspect
    18 Nov 7:23 pm HST 140 140 100 ENE 5G10 806.30 0 — 2.72 Suspect
    18 Nov 6:23 pm HST 140 140 100 E 6G14 806.30 9 200% 2.72 Suspect
    18 Nov 5:23 pm HST 140 140 100 E 4G11 805.96 23 6% 2.72 Suspect
    18 Nov 4:23 pm HST 140 140 100 ESE 3G08 805.96 27 4% 2.72 Suspect
    18 Nov 3:23 pm HST 140 140 100 ESE 4G11 806.30 38 4% 2.72 Suspect
    18 Nov 2:23 pm HST 140 140 100 ESE 4G09 806.64 63 5% 2.72 Suspect
    18 Nov 1:23 pm HST 140 140 100 SE 6G17 806.98 29 2% 2.72 Suspect
    18 Nov 12:23 pm HST 140 135 88 ESE 7G14 807.32 0 0 2.72 Suspect
    18 Nov 11:23 am HST 140 135 87 ESE 5G12 807.99 0 0 2.72 Suspect
    18 Nov 10:23 am HST 140 131 78 SSE 5G11 807.99 2 0 2.72 Suspect
    18 Nov 9:23 am HST 140 117 54 S 3G07 807.65 0 0 2.72 Suspect
    18 Nov 8:23 am HST 140 107 40 NNE 3G08 806.98 0 0 2.72 Suspect

  4. LG says:

    Looking back @ 7-Day table, it seems the instrument has been stuck there since Nov 7th:

    Time Temp. Dew Relative Wind Wind Station Solar Solar Precip Precip Precip Precip Quality
    Point Humidity Direction Speed Pressure Radiation Pct Accumulated 1 hour 6 hour 24 hour Control
    (HST) (f) (f) (%) (mph) (W/m*m) of psbl (inches) (inches) (inches) (inches)
    19 Nov 8:23 am HST 140 140 100 ENE 9G18 807.32 15 4% 2.72 Suspect
    19 Nov 7:23 am HST 140 140 99 NE 7G17 806.98 4 58% 2.72 Suspect
    19 Nov 6:23 am HST 140 133 83 NE 4G09 806.30 0 — 2.72 Suspect
    19 Nov 5:23 am HST 140 140 100 NE 7G15 806.30 0 — 2.72 Suspect
    19 Nov 4:23 am HST 140 140 100 ENE 7G17 806.30 0 — 2.72 Suspect
    19 Nov 3:23 am HST 140 140 100 ENE 6G13 806.30 0 — 2.72 Suspect
    19 Nov 2:23 am HST 140 140 100 ENE 5G12 806.64 0 — 2.72 Suspect
    19 Nov 1:23 am HST 140 140 100 ENE 6G13 806.98 0 — 2.72 Suspect
    19 Nov 12:23 am HST 140 138 95 NE 4G10 806.98 0 — 2.72 Suspect
    18 Nov 11:23 pm HST 140 139 97 NE 5G11 807.32 0 — 2.72 Suspect
    18 Nov 10:23 pm HST 140 133 83 NE 7G12 807.65 0 — 2.72 Suspect
    18 Nov 9:23 pm HST 140 137 93 ENE 7G14 807.32 0 — 2.72 Suspect
    18 Nov 8:23 pm HST 140 140 100 NE 5G11 806.64 0 — 2.72 Suspect
    18 Nov 7:23 pm HST 140 140 100 ENE 5G10 806.30 0 — 2.72 Suspect
    18 Nov 6:23 pm HST 140 140 100 E 6G14 806.30 9 200% 2.72 Suspect
    18 Nov 5:23 pm HST 140 140 100 E 4G11 805.96 23 6% 2.72 Suspect
    18 Nov 4:23 pm HST 140 140 100 ESE 3G08 805.96 27 4% 2.72 Suspect
    18 Nov 3:23 pm HST 140 140 100 ESE 4G11 806.30 38 4% 2.72 Suspect
    18 Nov 2:23 pm HST 140 140 100 ESE 4G09 806.64 63 5% 2.72 Suspect
    18 Nov 1:23 pm HST 140 140 100 SE 6G17 806.98 29 2% 2.72 Suspect
    18 Nov 12:23 pm HST 140 135 88 ESE 7G14 807.32 0 0 2.72 Suspect
    18 Nov 11:23 am HST 140 135 87 ESE 5G12 807.99 0 0 2.72 Suspect
    18 Nov 10:23 am HST 140 131 78 SSE 5G11 807.99 2 0 2.72 Suspect
    18 Nov 9:23 am HST 140 117 54 S 3G07 807.65 0 0 2.72 Suspect
    18 Nov 8:23 am HST 140 107 40 NNE 3G08 806.98 0 0 2.72 Suspect
    18 Nov 7:23 am HST 140 115 51 NNW 5G10 806.64 2 22% 2.72 Suspect
    18 Nov 6:23 am HST 140 119 57 NNW 4G08 806.30 0 — 2.72 Suspect
    18 Nov 5:23 am HST 140 128 72 NNW 6G08 805.62 0 — 2.72 Suspect
    18 Nov 4:23 am HST 140 126 68 NNW 6G08 805.96 0 — 2.72 Suspect
    18 Nov 3:23 am HST 140 124 65 N 5G07 805.96 0 — 2.72 Suspect
    18 Nov 2:23 am HST 140 123 63 NNE 5G07 806.64 0 — 2.72 Suspect
    18 Nov 1:23 am HST 140 129 74 N 6G09 806.98 0 — 2.72 Suspect
    18 Nov 12:23 am HST 140 137 92 N 5G08 807.32 0 — 2.72 Suspect
    17 Nov 11:23 pm HST 140 140 100 NNW 4G09 807.32 0 — 2.72 Suspect
    17 Nov 10:23 pm HST 140 NNW 3G05 806.98 0 — 2.72 Suspect
    17 Nov 9:23 pm HST 140 NNW 4G06 806.98 0 — 2.72 Suspect
    17 Nov 8:23 pm HST 140 NNW 3G05 806.64 0 — 2.72 Suspect
    17 Nov 7:23 pm HST 140 W 1G03 806.30 0 — 2.72 Suspect
    17 Nov 6:23 pm HST 140 SSW 1G03 805.96 0 0 2.72 Suspect
    17 Nov 5:23 pm HST 140 SSW 3G06 805.62 1 0 2.72 Suspect
    17 Nov 4:23 pm HST 140 SSE 3G07 805.62 1 0 2.72 Suspect
    17 Nov 3:23 pm HST 140 SSE 5G10 805.62 3 0 2.72 Suspect
    17 Nov 2:23 pm HST 140 SE 5G10 805.96 1 0 2.72 Suspect
    17 Nov 1:23 pm HST 140 SE 5G11 806.64 0 0 2.72 Suspect
    17 Nov 12:23 pm HST 140 SSE 6G11 807.32 1 0 2.72 Suspect
    17 Nov 11:23 am HST 140 SE 6G12 807.99 0 0 2.72 Suspect
    17 Nov 10:23 am HST 140 SE 5G12 807.99 0 0 2.72 Suspect
    17 Nov 9:23 am HST 140 E 5G11 807.65 0 0 2.72 Suspect
    17 Nov 8:23 am HST 140 NNE 4G08 807.32 0 0 2.72 Suspect
    17 Nov 7:23 am HST 140 N 5G07 806.64 0 0 2.72 Suspect
    17 Nov 6:23 am HST 140 N 6G09 805.96 0 — 2.72 Suspect
    17 Nov 5:23 am HST 140 NNW 6G08 805.96 0 — 2.72 Suspect
    17 Nov 4:23 am HST 140 NNW 5G08 805.62 0 — 2.72 Suspect
    17 Nov 3:23 am HST 140 NNW 5G07 805.96 0 — 2.72 Suspect
    17 Nov 2:23 am HST 140 140 100 NNW 5G07 806.30 0 — 2.72 Suspect
    17 Nov 1:23 am HST 140 NNW 4G06 806.64 0 — 2.72 Suspect
    17 Nov 12:23 am HST 140 140 100 N 4G07 806.98 0 — 2.72 Suspect
    16 Nov 11:23 pm HST 140 140 100 NNW 4G07 806.98 0 — 2.72 Suspect
    16 Nov 10:23 pm HST 140 140 100 NNE 2G06 806.98 0 — 2.72 Suspect
    16 Nov 9:23 pm HST 140 140 100 NE 2G05 806.98 0 — 2.72 Suspect
    16 Nov 8:23 pm HST 140 140 100 ENE 2G06 806.30 0 — 2.72 Suspect
    16 Nov 7:23 pm HST 140 140 100 E 4G08 805.96 0 — 2.72 Suspect
    16 Nov 6:23 pm HST 140 140 100 E 4G09 805.28 2 35% 2.72 Suspect
    16 Nov 5:23 pm HST 140 140 100 E 4G09 804.61 61 16% 2.72 Suspect
    16 Nov 4:23 pm HST 140 140 100 ESE 4G08 804.27 66 9% 2.72 Suspect
    16 Nov 3:23 pm HST 140 ESE 3G05 804.27 63 7% 2.72 Suspect
    16 Nov 2:23 pm HST 140 SE 3G08 804.27 13 1% 2.72 Suspect
    16 Nov 1:23 pm HST 140 SE 6G12 804.94 1 0 2.72 Suspect
    16 Nov 12:23 pm HST 140 ESE 5G12 805.28 1 0 2.72 Suspect
    16 Nov 11:23 am HST 140 ESE 5G11 805.62 11 1% 2.72 Suspect
    16 Nov 10:23 am HST 140 SE 4G10 805.62 1 0 2.72 Suspect
    16 Nov 9:23 am HST 140 130 76 SSW 2G08 805.28 0 0 2.72 Suspect
    16 Nov 8:23 am HST 140 140 100 NNW 3G08 804.94 3 1% 2.72 Suspect
    16 Nov 7:23 am HST 140 138 94 NNW 6G09 804.27 1 7% 2.72 Suspect
    16 Nov 6:23 am HST 140 140 100 NNW 5G09 803.59 0 — 2.72 Suspect
    16 Nov 5:23 am HST 140 140 100 NNW 5G08 803.25 0 — 2.72 Suspect
    16 Nov 4:23 am HST 140 140 100 NNW 5G07 802.91 0 — 2.72 Suspect
    16 Nov 3:23 am HST 140 140 100 NNW 4G07 803.25 0 — 2.72 Suspect
    16 Nov 2:23 am HST 140 134 85 NNW 3G09 803.25 0 — 2.72 Suspect
    16 Nov 1:23 am HST 140 140 100 N 4G10 803.25 0 — 2.72 Suspect
    16 Nov 12:23 am HST 140 140 100 NNE 3G06 803.59 0 — 2.72 Suspect
    15 Nov 11:23 pm HST 140 140 100 NE 2G04 803.93 0 — 2.72 Suspect
    15 Nov 10:23 pm HST 140 NNE 3G12 803.93 0 — 2.72 Suspect
    15 Nov 9:23 pm HST 140 NNW 8G14 803.93 0 — 2.72 Suspect
    15 Nov 8:23 pm HST 140 NW 7G12 803.25 0 — 2.72 Suspect
    15 Nov 7:23 pm HST 140 WNW 6G11 802.91 0 — 2.72 Suspect
    15 Nov 6:23 pm HST 140 WNW 6G13 802.24 33 200% 2.72 Suspect
    15 Nov 5:23 pm HST 140 WNW 9G17 801.90 14 4% 2.72 Suspect
    15 Nov 4:23 pm HST 140 WNW 8G16 801.56 1 0 2.72 Suspect
    15 Nov 3:23 pm HST 140 WNW 9G20 801.56 0 0 2.72 Suspect
    15 Nov 2:23 pm HST 140 NW 11G21 801.90 0 0 2.72 Suspect
    15 Nov 1:23 pm HST 140 WNW 11G23 802.57 0 0 2.72 Suspect
    15 Nov 12:23 pm HST 140 WNW 10G21 802.91 0 0 2.72 Suspect
    15 Nov 11:23 am HST 140 WNW 11G21 803.59 0 0 2.72 Suspect
    15 Nov 10:23 am HST 140 WNW 12G23 803.59 0 0 2.72 Suspect
    15 Nov 9:23 am HST 140 WNW 10G21 802.91 0 0 2.72 Suspect
    15 Nov 8:23 am HST 140 NW 7G16 802.57 8 2% 2.72 Suspect
    15 Nov 7:23 am HST 140 NW 8G16 801.90 0 0 2.72 Suspect
    15 Nov 6:23 am HST 140 NW 8G16 801.56 0 — 2.72 Suspect
    15 Nov 5:23 am HST 140 NNW 10G16 801.56 0 — 2.72 Suspect
    15 Nov 4:23 am HST 140 NW 9G15 801.56 0 — 2.72 Suspect
    15 Nov 3:23 am HST 140 NW 8G17 801.90 0 — 2.72 Suspect
    15 Nov 2:23 am HST 140 NNW 4G10 802.24 0 — 2.72 Suspect
    15 Nov 1:23 am HST 140 NNW 3G07 802.57 0 — 2.72 Suspect
    15 Nov 12:23 am HST 140 NNE 3G07 802.91 0 — 2.72 Suspect
    14 Nov 11:23 pm HST 140 140 100 N 3G07 803.25 0 — 2.72 0.03 Suspect
    14 Nov 10:23 pm HST 140 NW 3G08 803.59 0 — 2.72 0.07 Suspect
    14 Nov 9:23 pm HST 140 NNW 3G06 803.59 0 — 2.72 0.07 Suspect
    14 Nov 8:23 pm HST 140 NW 2G05 803.25 0 — 2.72 0.07 Suspect
    14 Nov 7:23 pm HST 140 140 100 SSW 2G07 802.91 0 — 2.72 0.07 Suspect
    14 Nov 6:23 pm HST 92 92 100 SE 5G12 801.90 0 0 2.72 0.07 OK
    14 Nov 5:23 pm HST 64 60 87 N 9G23 801.56 0 0 2.72 0.07 OK
    14 Nov 4:23 pm HST 67 67 100 NNW 11G20 801.56 0 0 2.72 0.07 OK
    14 Nov 3:23 pm HST 69 66 90 NW 11G21 801.56 0 0 2.72 0.07 OK
    14 Nov 2:23 pm HST 70 56 62 WNW 10G19 801.90 0 0 2.72 0.08 OK
    14 Nov 1:23 pm HST 71 71 99 WNW 10G22 802.24 0 0 2.72 0.08 OK
    14 Nov 12:23 pm HST 71 71 100 WNW 12G23 802.91 0 0 2.72 0.11 OK
    14 Nov 11:23 am HST 69 69 100 WNW 12G25 803.59 0 0 2.72 0.13 OK
    14 Nov 10:23 am HST 68 68 100 NW 10G24 803.59 0 0 2.72 0.14 OK
    14 Nov 9:23 am HST 67 67 100 NW 8G18 803.25 0 0 2.72 0.16 OK
    14 Nov 8:23 am HST 62 62 100 WNW 4G12 802.91 0 0 2.72 0.19 OK
    14 Nov 7:23 am HST 57 57 100 NW 4G12 802.57 2 11% 2.72 0.19 OK
    14 Nov 6:23 am HST 58 57 98 NW 10G22 801.90 0 — 2.72 0.19 OK
    14 Nov 5:23 am HST 58 58 100 NW 14G27 801.56 0 — 2.72 0.03 0.20 OK
    14 Nov 4:23 am HST 58 NW 12G21 801.90 0 — 2.72 0.07 0.23 OK
    14 Nov 3:23 am HST 59 57 92 NW 11G20 802.57 0 — 2.72 0.07 0.26 OK
    14 Nov 2:23 am HST 59 NW 10G19 802.91 0 — 2.72 0.07 0.26 OK
    14 Nov 1:23 am HST 59 59 99 NW 10G20 803.59 0 — 2.72 0.07 0.26 OK
    14 Nov 12:23 am HST 59 59 100 WNW 8G16 803.93 0 — 2.72 0.03 0.07 0.27 OK
    13 Nov 11:23 pm HST 59 59 100 WNW 7G14 804.27 0 — 2.69 0.04 0.04 0.24 OK
    13 Nov 10:23 pm HST 59 59 100 WNW 6G13 804.27 0 — 2.65 0.20 OK
    13 Nov 9:23 pm HST 60 60 100 WNW 6G11 803.93 0 — 2.65 0.20 OK
    13 Nov 8:23 pm HST 59 59 100 W 5G10 803.59 0 — 2.65 0.01 0.20 OK
    13 Nov 7:23 pm HST 60 60 100 W 5G12 803.25 0 — 2.65 0.01 0.20 OK
    13 Nov 6:23 pm HST 61 61 100 W 5G12 803.25 0 0 2.65 0.04 0.20 OK
    13 Nov 5:23 pm HST 63 63 100 WSW 6G10 802.57 0 0 2.65 0.06 0.20 OK
    13 Nov 4:23 pm HST 64 64 100 SW 7G13 802.57 0 0 2.65 0.07 0.20 OK
    13 Nov 3:23 pm HST 64 64 100 SW 5G13 802.91 21 2% 2.65 0.01 0.09 0.20 OK
    13 Nov 2:23 pm HST 63 63 100 SW 5G11 803.25 0 0 2.64 0.11 0.21 OK
    13 Nov 1:23 pm HST 62 62 100 SW 4G08 804.27 0 0 2.64 0.03 0.11 0.24 OK
    13 Nov 12:23 pm HST 61 61 100 SW 4G09 804.94 0 0 2.61 0.02 0.08 0.21 OK
    13 Nov 11:23 am HST 61 61 100 SW 3G07 805.28 0 0 2.59 0.01 0.07 0.19 OK
    13 Nov 10:23 am HST 61 61 100 SSW 2G06 805.28 0 0 2.58 0.02 0.09 0.18 OK
    13 Nov 9:23 am HST 60 60 100 WSW 1G06 804.94 6 1% 2.56 0.03 0.10 0.17 OK
    13 Nov 8:23 am HST 59 59 100 S 3G06 804.61 0 0 2.53 0.07 0.19 OK
    13 Nov 7:23 am HST 58 58 100 W 3G07 803.93 0 0 2.53 0.07 0.19 OK
    13 Nov 6:23 am HST 59 59 100 WNW 1G05 803.93 0 — 2.53 0.01 0.08 0.19 OK
    13 Nov 5:23 am HST 59 59 100 G03 803.59 0 — 2.52 0.03 0.07 0.18 OK
    13 Nov 4:23 am HST 58 58 100 SSW 1G02 803.59 0 — 2.49 0.03 0.04 0.15 OK
    13 Nov 3:23 am HST 58 58 100 NNW 1G02 803.93 0 — 2.46 0.01 0.12 OK
    13 Nov 2:23 am HST 58 58 100 N 1G03 804.27 0 — 2.46 0.01 0.12 OK
    13 Nov 1:23 am HST 58 58 100 ESE 2G05 804.61 0 — 2.46 0.01 0.01 0.12 OK
    13 Nov 12:23 am HST 57 57 100 NNE 2G04 804.61 0 — 2.45 0.11 OK
    12 Nov 11:23 pm HST 58 58 100 N 1G04 804.94 0 — 2.45 0.11 OK
    12 Nov 10:23 pm HST 58 58 100 WNW 2G07 804.94 0 — 2.45 0.11 OK
    12 Nov 9:23 pm HST 58 58 100 NW 2G05 804.94 0 — 2.45 0.11 OK
    12 Nov 8:23 pm HST 59 59 100 SSW 2G08 804.61 0 — 2.45 0.02 0.11 OK
    12 Nov 7:23 pm HST 59 59 100 SSW 3G07 803.93 0 — 2.45 0.05 0.11 OK
    12 Nov 6:23 pm HST 60 60 100 S 3G06 803.59 3 20% 2.45 0.05 0.11 OK
    12 Nov 5:23 pm HST 61 61 100 SSE 3G07 803.25 13 3% 2.45 0.05 0.11 Caution
    12 Nov 4:23 pm HST 63 63 100 SSW 6G11 802.91 40 6% 2.45 0.05 0.11 Caution
    12 Nov 3:23 pm HST 63 63 100 SSW 6G11 802.91 45 5% 2.45 0.02 0.06 0.11 Caution
    12 Nov 2:23 pm HST 63 45 52 S 5G09 803.25 19 2% 2.43 0.03 0.09 0.09 Caution
    12 Nov 1:23 pm HST 65 S 6G12 803.93 0 0 2.40 0.06 0.06 Caution
    12 Nov 12:23 pm HST 79 SSW 5G11 804.61 79 6% 2.40 0.06 0.06 Caution
    12 Nov 11:23 am HST 137 137 100 S 4G09 805.28 36 3% 2.40 0.06 0.06 Suspect
    12 Nov 10:23 am HST 140 S 3G06 805.28 68 7% 2.40 0.01 0.06 0.06 Suspect
    12 Nov 9:23 am HST 140 140 100 S 3G07 804.94 3 0 2.39 0.05 0.05 0.05 Suspec

  5. Larry Ledwick says:

    Interesting question!
    If a missing temperature at high altitude is infilled using nearby lower elevation temperatures do they dial in lapse rate adjustments due to altitude. For a hypothetical example if a temperature was missing for 14,440 ft Mount Elbert summit using temperatures from Leadville Colorado which is only 10 miles away would they introduce a correction for the elevation difference? Leadville is at 10,152 ft elevation, so the 4288 ft elevation difference would introduce a temperature difference due to elevation of somewhere between the dry adiabatic lapse rate of (10°C/kilometer or 5.5°F/1000 feet) and the wet adiabatic lapse rate of (5.5C°/km, 3F°/1,000 ft).

    Due to the low humidity common in high elevations like the collegiate range in Colorado most likely the correct adjustment would be closer to 5.5 deg F / 1000 ft. In mountain rescue we usually assumed a 5 deg / 1000 ft difference when guesstimating temperatures at higher elevations before leaving base camp. That would make the proper lapse rate adjustment for 4288 ft approximately 21 deg F

    Side note that high of a temperature could also be due to a sensor failure, some digital thermometers I have would pin high at 158 deg F if the sensor wires broke. Another alternative would something reflective nearby illuminating the sensor with 2x solar isolation like a car parked near the sensor reflecting sun off its windshield.

  6. Graeme No.3 says:

    Larry Ledwick:

    10 miles? Pikers! the Australian Bureau of Meteorology uses figures from 500 km. (300+ miles) away. Turns a flat line into a 2℃ rise. For starters see
    http://joannenova.com.au/2014/07/wow-look-at-those-bom-adjustments-trends-up-by-two-degrees-c/
    There are several articles on the same lines about that time. Try Adjustments to data in the archives. Front page LHS, down a bit.

  7. omanuel says:

    Steven Goddard convinced me data manipulation produced most, if not all, evidence for the AGW scare.

    My own research experience shows that data manipulation after 1945 produced:

    1. The Standard Solar Model
    2. The Standard Nuclear Model

    I.e., FEAR of nuclear annihilation in 1945 convinced world leaders to generate a false illusion of reality, just as George Orwell predicted would happen in a book he started to write in 1946: “Nineteen Eighty-Four.”

  8. Jason Calley says:

    I don’t mean to be a wiseacre, but the pragmatic, experience based answer to “what happens when high cold temps are missing?” is that “adjustments are made to show a warming trend.” What happens when a high hot temperature goes missing? In that case, “adjustments are made to show a warming trend.” OK, how about, what happens when all the data is there and none of it is missing? You guessed it — “adjustments are made to show a warming trend.”

    Every year, the fraudulent adjustments get bigger. In every new version of the data sets, the fake warming trend gets more and more outrageous. This is not science — this is politics and politics has nothing to do with the truth. https://stevengoddard.files.wordpress.com/2014/11/screenhunter_4422-nov-08-19-55.gif

    (For any new readers of your blog, those who are not familiar with the extraordinary work you did analyzing CAGW software and procedures, I would suggest that they go back and read your postings from four of five years ago. E.M., the world owes you a gold star for that work.)

  9. DonM says:

    I’d also be interested in the definition of “suspect” data … when do we toss out perceived bad data and replace with approximated data.

    Then based on that definition I’d follow up with the possibility of a bias associated with “too low data” vs. “too high data”; do we throw out the appropriate anomalous data on both ends of the spectrum?

    Also, If our measurement scale extends 100% above the average high and extends 25% below the average low then we have developed a built in bias towards the low … and then the problems with the approximated (homogenized) data methodology will compound the skew.

    Was there ever an accurate measurement that was even close to 140 degrees at this elevation … Why do we use equipment that is calibrated to this scale?

  10. Jason Calley says:

    @DonM “I’d also be interested in the definition of “suspect” data … when do we toss out perceived bad data and replace with approximated data.”

    My understanding is that NOAA has an algorithm that compares station data to other stations near it and then categorizes the data as bad if it exceeds the algorithm parameters. What parameters? Heck, what algorithm?! No one seems to know.. but at present, something like 30% of the NOAA “data” is actually numbers that have been interpolated by the mysterious and never-yet-revealed formula.
    If anyone has any clear information on how it is done or can correct my possibly-incorrect-understanding, please let me know!

    The “adjustments” certainly seem to be, uh, “systematic” in their effects.
    http://stevengoddard.wordpress.com/2014/06/08/more-data-tampering-forensics/

  11. E.M.Smith says:

    @DonM:

    I looked at that a bit here:
    https://chiefio.wordpress.com/2010/04/11/qa-or-tossing-data-you-decide/
    that references this work:
    http://journals.ametsoc.org/doi/full/10.1175/JTECH1963.1

    The big thing for me was that the same size acceptance (or ‘toss’) band was used for both high and low excursions, yet lows are much more volatile than highs… so the inevitable effect will be to toss more low excursions than high excursions… then fill them in with an average of ‘nearby’ ASOS stations… when an average can never has extreme an excursion as a single station and ASOS stations are more strongly influenced by Airport Heat Island Effects. IMHO it is guaranteed to result in a warming bias.

    Why calibrate to 140 F? Because some places in the world get to the 130’s F and you need a little head room for a ‘one size fits all’ device.

    @Jason Calley:

    Thanks for the compliment. I’d love to just spend all my time doing “temp data analytics”, but can’t do it for free (since I have a house and family that expects me to provide for them) and nobody wants to pay me a ‘living wage’ (or any wage, really…) to do that. I’ve got a half dozen ‘bright ideas’ to explore, but no time to do it. Well, at any rate, my current contract ends the first week of December. Then I’m back to “poverty and looking for a job” that means I’ll have no money OR time. (Since free time must go to a job search). Sigh again.

    For one thing, I’ve pack ratted away the old versions of GHCN and even the Daily set (for one or two points in time). I’d love to, for example, splice the new data from GHCN v3 onto the old data from GHCN v1 with the span when GHCN v2 was active in the middle. That way you have the “same data” from the “same instruments”, but minus the every cooling past adjustments. THEN look at the trends. Probably take me about a week of modest focus time. (Less if I learned R first and / or a PC database product – I got a book on SQL and it’s easy, but need the time to set up a Linux box and database and… so it goes.)

    I’d also like to take the original Hansen paper that found anomalies could be filled in from 1200 km away (or maybe it was miles…) and deconstruct it; doing ‘challenge’ examples to show when his ‘proof’ breaks down. Say, for example, during meridional vs zonal flow and when huge temp excursions happen in adjacent areas (like now). Probably take about a month to do it right.

    The list goes on. But without a Ph.D. Climate or related, a post at a college, and a nice grant writing committee getting government money (that clearly does not flow for skeptical examinations) that’s not likely to happen. I’d originally hoped that the ‘example’ work done then (about 2009) would get some funding for that kind of work. Generated a grand total of a $1000 for one study. That is about what my coffee budget for all that work cost.

    Take now, for example. It’s after midnight, and I’m just now getting time to read comments at my own blog. Not current on WUWT at all. Tallbloke talkshop is a week in arrears. I’ve not been to Verity’s site in months, nor JoNova, nor Steve Goddard, nor… And I need to be up in about 6 hours to get ready for work tomorrow.

    All for what? For an out of control POTUS to mandate whatever he wants for ‘friends and contributors of POTUS and IPCC’ via fiat and for an EPA sock puppet to do anything he asks. Never mind the 100 year cold and snow records falling. Sometimes it gets depressing. Especially when folks like Mann get caught in apparent fraud and gets a $5 million ‘wet kiss’ consolation prize. And POTUS is also promising a big Visa Bump for “high tech” workers. I’ve sent out a dozen resumes and gotten zero response already… so we need MORE competition for the few tech jobs advertising? Really? ( I just saw an ad on the door of a restaurant chain looking for Store General Managers for $70,000 salary plus benefits… that’s close enough to current pay rates to cause me to consider it. Maybe POTUS can grant H1B visas for restaurant managers too…)

    So I work in what I can around the edges. And dream of doing some of the bigger more interesting projects.

    @LG:

    Thanks for the link! I thought of using Google Earth to look at the location, but haven’t had time yet. Might be interesting as a “Dig Here!” for others. Did a quick search on the name and this (slow to load big pictures) link came up. Looks like a decent rural location, but no cabin… http://www.wrcc.dri.edu/cgi-bin/wea_info.pl?hiHNEN

    Ah, found the cabin. Background left of this one:

    @Larry:

    The Hansen method is to take a period of overlap with ‘nearby’ stations (up to 1200 km away) and calculate the typical offset in the anomalies. Then use that to adjust the missing data. So if it is typically 10 C warmer in Orlando than Atlanta, and Orlando is missing, just add 10C to the temp for Atlanta. What about now when Atlanta is under an Arctic blanket and Orlando isn’t quite? Well, it all averages out, I’m sure /sarc;

  12. Jason Calley says:

    @ E.M. “Thanks for the compliment.”

    Well, it is both sincere and honest; you deserve it — which brings me to your comments about having to make a living in the mundane world, etc. For what it is worth, my best friend has been unemployed and under-employed for the last four years or so. Here is the worrisome part; I have known him for twenty plus years and he is a VERY bright guy, honest, hardworking, conscientious, good social skills, etc. He has a long history of work in technical fields, in both sales and training. If I had my own company, he is exactly the kind of employee I would love to have on the payroll. No need to go into details, but in the last four years he has been bounced through a series of jobs that demand 80 hour weeks, supply your own car and expenses, poor or no benefits, poor pay or erratic commission structure, impossible sales goals and deadlines, unethical, dishonest and quasi-legal job requirements, etc. Again, this is someone I have known for decades and in spite of all his skills and integrity the job market is apparently not interested in his skill set.

    Our national marketplace is broken.

    Blame the bankers? The politicians? The merchants? Maybe blame the Powers That Be, or We The People? The truth is, there is probably enough blame that everyone can have a heaping share — but the fact remains, that our nation finds itself in a time where we see a gross misallocation of skills and resources. We see people in the top of their skill set who are unable to get adequate employment or pay — and at the same time we see the well-connected idiots who are pulling down huge pay and bonuses strictly because of who they know and what lies they are willing to disseminate. Businesses are more frightened by the EPA and the IRS than they are by competitors or bad vendors. Sometimes complying with paperwork costs almost as much as raw materials.

    E.M., you are an extraordinarily bright guy — and have the additional rare talent of thinking creatively and outside the proverbial box. When YOU (and my friend) have to struggle to make ends meet, then something is seriously, deeply and fundamentally wrong with the system. In a rational and ethical marketplace you would be snatched up quicker than a hundred dollar bill lying on the sidewalk. But you are not — and neither is my buddy.

    I am sure we all have our own secret and perhaps unspoken ideas of where things have gone bonkers… but regardless of what is causing the current systematically insane usage of human talents, this can not go on forever. The machine will stop, one way or another, and going Galt looks better all the time.

  13. DonM says:

    “… you need a little head room for a ‘one size fits all’ device.”

    Yes, a little head room is necessary. If there is an appropriate “little head room” on the low end, and a crap-load of “head room” on the upper end then there will be more room for “anomalies” that will need to be corrected on the upper end.

    For example, if the lower end has 20 degrees of head room, and the defined anomalous data is defined in some way as being outside of this range most of the time, then there would never be anything to correct. If the high end has a 100 degree head room, then there is more room for data points that need to be corrected.

    If the correction does actually skew the data (and I like the skew), then I certainly like that I have set up the system to compound the skew, and others of like mind will pat me on the back.

  14. omanuel says:

    Thanks, E.M. Smith, for you help restoring society’s contact with reality. That is unfolding rapidly now.

    The 2009 Climategate emails and five years of dishonest official responses helped me decipher and complete a research assignment received in 1960 from a nuclear geo-chemist who secretly took possession of Japan’s atomic bomb plans [1] during unreported CHAOS & FEAR of nuclear annihilation in AUG – SEPT 1945 [2].

    Discover reality from inside a social matrix controlled by FEAR of reality.

    Today Roger Helmer found another new piece of the puzzle:

    http://rogerhelmermep.wordpress.com/2014/11/20/open-letter-to-rt-hon-john-bercow-mp/

    The survival of humanity now depends on our success in escaping the fear-based matrix of reality that frightened world leaders created sixty-nine years (2014 – 1945 = 69 yrs) ago to save the world from nuclear annihilation! [2]

    Nations were united in OCT 1945 to hide this reality [3] from the public from the public.

    1. BBC News, “Atomic plans returned to Japan,” News Front Page, World Edition (3 Aug 2002) http://news.bbc.co.uk/2/hi/asia-pacific/2170881.stm

    2. “Aston’s Promise & Warning (1922); CHAOS and FEAR (Aug-Sept 1945)”. https://dl.dropboxusercontent.com/u/10640850/CHAOS_and_FEAR_August_1945.pdf

    3. “Solar energy,” Advances in Astronomy (submitted 1 Sept 2014) https://dl.dropboxusercontent.com/u/10640850/Solar_Energy.pdf

  15. E.M.Smith says:

    @Jason Calley:

    I’ve “admired the problem” from many angles over the years, and I’ve collected a bit of list of ‘issues’; but it’s a dismal list… Might make a posting, but more likely isn’t of much use. A short list of a few of the bigger bits:

    1) I’m a polymath. My lowest score (Kuder Aptitude) is 87 th percentile and that is in “clerical” the rest were 99.x+ percentile .. (so, of course, I was largely employed in clerical work to put myself through college… sigh… seems my 87 beat the ‘typical applicant’ by a lot and they wanted clerks…) Now in industry today, nobody wants a generalist. They want a skilled monkey with a narrow set. I call this “silo-ing”. Especially now that I.T. has become infested with 1,000 different ‘certs’. Just in ONE narrow silo, you can have a half dozen different certifications. Each costing a few hundred to a few thousand dollars per year to maintain. What happens when you have skill in a dozen areas? Golly, your annual salary will go to maintaining ‘certs’. So call it $5k to each of Cisco, Microsoft, Red Hat, Sun, PMP (Project Management), the Disaster Recovery series, the Security Series, the …. we’re at $30,000 already and haven’t touched 1/2 of the things I’ve worked on. So you are looked at as a peg that fits no silo hole.

    2) Computer screening. So the polymath shows up without the mandated certs and doesn’t get past the computer screen at the gate. No cert, bit bucket. Never mind that I’ve lived on Unix for about 35 years now, built all kinds of it “from scratch” (BSD, Linux – several, Sun Os, Solaris, even the Unix from DEC back when NOBODY got the source code… we got it at Apple…) and make Raspberry Pi Debian Linux gadgets for fun… and made a Beowulf cluster for fun… and was build master for a BSD based appliance… and was QA / Documentation manager for the compiler tool chain (that got rolled into Red Hat / Linux). No cert, eh? FLUSH! (Oh, and I have a State of California Teaching Credential at the community college level – lifetime [one of the last issued] but can’t find a teaching gig as they now want vendor ‘certs’ too. So why did I get that year of post degree work to get that credential… ) Never mind that I’ve been managing things for about 40 years and have a dozen years of intense project work at VARs (value added re-sellers) and more. No PMP? FLUSH! Sure, I could ‘pick a silo’ and specialize in some tiny corner and get a cert. And be bored out of my mind… ( I DID get a lifetime CDP cert from ICCP back in the ’80s… that now nobody cares about and even ICCP don’t issue any more. It was a ‘capstone’ cert to show you know all areas and were suitable for middle / upper management.) Having sunk a couple of years into ‘capstone’ certs and credentials, I’m a bit leery of vendor certs now…

    3) Long work experience. Resumes get cut off at 10 years. Certs look at the last 10 years experience (AND require current employment in the silo…) So I spent a bunch of years not working for wages but doing this climate stuff. (Had enough income from trading and the spouse was working, so why not do something interesting ‘for society’?) Now the spouse is not working, and trading was slim… Back to work… But, about that experience. Apparently you can’t be expected to remember how to do Unix Sys Admin if nobody was paying you to do it in the last decade, nor can you remember how to manage… The process is not set up for people who remember being 3 years old and learning how to run on a dirt road, or 2 and encountering stairs for the first time, or every major project they managed, or… You are only your last job or two. Not a lifetime. No long memory allowed, or expected.

    4) Dilbert Problems. I have a Dilbert saved where The Manager is saying folks are complaining about Dilbert being “cynical”, and Dilbert responds that he has a reality based perspective, then the Boss says “Then why are people upset?” and Dilbert responds “Says the angry guy to the one who isn’t…” (paraphrase, but close). There is another Mensan at work with the same “problem”. Keeps doing a good job, and getting complained at for it. Why? Recognizes the reality instead of indulging in the shared fantasy that is the Political Workplace. It is better to spend 6 months on something that is guaranteed to fail (after the manager gets a bonus, one presumes) and have it crash and burn spectacularly for all to see (but with an appropriate scapegoat) than to say “That will fail for this reason: {Reason FOO shown clearly} and we ought to do {BAR} instead.” I’ve had my boss fired when he did exactly that. It is important to have skills in Grubering even inside the workplace. Whatever the boss wants, or believes, is reality. NEVER say something isn’t going to work, and CERTAINLY never be right about it. Hard to keep that in mind while being honest and non-cynical…

    IMHO, those are the ‘big lumps’ that are wrong with ‘the system’ at present. There are others, but not as important. So far I’ve been able to hide my ‘Dilbert side’ enough to stay employed at places, and I’ve been able to sell the Credential and CDP as “enough certs”, but getting past the computer screen has now made that much much harder. Also as the resume gets longer and the more interesting work gets older, it’s a harder sell. “So you have been doing PM work?”… ignoring the Director slot on page two pushing 20 years back… “We want a PMP to show you have the needed skills…”

    Oh Well… We’ll see if my usual mild despair during the job search fades with the first interviews. It usually does…

    Oh, and things got harder when the H1B visa flood gates opened. It now helps to understand that peculiar dialect of Indian English in the I.T. workplace. Why I recommended that my (Honors Math) kid not go into computing. And they wonder why there are not more computer science graduates…. (Lets see, what part of “driving down wages and opportunities” sounds like “market will attract more people to the hard technical fields”?…) In substantially every company they see the I.T. department as unwanted overhead, not as a strategic asset and guardian of the corporate intellectual property / security. It’s all about cutting costs, and little else. So little desire to hire the best and brightest. Rather two “slow and hard to understand but cheap” than one expensive guy who can do a better job. (Don’t get me wrong, many of the guys from India are quite good. The problem is the bunch that are only so-so and the communications issues. You can get a Ph.D. in computer science in India and not have touched a router or major computer. I interviewed one once for a slot I was trying to fill. It’s just “books and tests”… likely better now that computers are much cheaper.)

    So tie that in with The Dilbert POV, and you can see where asking “Why are we outsourcing that function when it is working well right now and that will just break it?” is a bad idea…

    Oh Well, time will tell.

  16. Larry Ledwick says:

    @EM Your above list is remarkably similar to my experience in nearly every point stated. With only a few minor personal details varied it could be my story. Same situation, always tested at the very top of the test scores in the high 97+% percentiles except clerical and language skills. U.S. Navy GCTARI scores meet MENSA requirements, Very broad range of interests, and lots of practical work experience, but missing those useless certificates and “check the box” items that they use for first screening on resume filtering. After I got laid off from IBM in 2008 as they pushed all their IT work in our department to India, Brazil, Singapore Vietnam and China, I was unemployed for 18 months, could not get a call back or response of any kind on a job listing in my field because I did not fit the narrow silo screening criteria for the first level cut to evaluate a pile of resumes in hard times. Luckily I finally got a referral from a friend that got me through the door and now work for a great company that values hard work, dependability, and the ability to avoid problems rather than recover from mistakes. They also don’t mind hiring competent older employees who will actually show up for work or will stick with a problem until 5:00 in the morning if that is what it takes to get production running before the worker bees show up.

    There are a few good companies out there that will take a good look at skilled employees that don’t fit the mold, but it can take a while to find one.

  17. w.w.wygart says:

    E.M.

    I had something similar happen to me a number of years ago, 2008/2009. During the winter I noticed that the maximum snow depth metric published at the National Operational Hydrologic
    Remote Sensing Center website was reading something in excess of 20meters for several weeks in a row. So I sent them an email asking was-up-wit-dad? This is the rather informative reply I got from one Tim Szeliga at NOAA [in its entirety].

    I’m looking at today’s Snow Depth image. I’ll include links
    to some of our pages, so you can follow along with my reasoning.

    The scale is quasi-logarithmic, split along increasing
    multiples of five centimeters. The top end of the scale is
    2000 cm (787 inches). We don’t expect to find depths of
    twenty meters, but a check on today’s Snow Depth Observations
    http://www.nohrsc.noaa.gov/nsa/reports.html?region=National&var=snowdepth&dy=2009&dm=12&dd=07&units=e&sort=value&filter=0
    shows many values above 100cm. However, most of the
    top ten reports look bogus.

    This is a known problem. These come from a few automated
    weather stations in remote mountain sites. They tend to
    have problems at the beginning and end of the snow season
    and while taking measurements during intense snowfall activity.
    http://www.nohrsc.noaa.gov/interactive/html/graph.html?ey=2009&em=12&ed=7&eh=6&units=0&station=ARBO3

    The Arbuckle Mtn station jumped from 14″ of snow to 199″ this week.
    The Columbus Basin station in Colorado (CMBC2) leapt from 14″ to 191″.
    http://www.nohrsc.noaa.gov/interactive/html/graph.html?station=CMBC2&w=600&h=400&uc=0&by=2009&bm=11&bd=30&bh=6&ey=2009&em=12&ed=7&eh=6&data=1&units=0&x=0&y=0
    But note that the SWE, the Snow Water Equivalent, did not make
    a similar jump and that our modeled depth remained at 8.38″.

    At NOHRSC we model SWE as a nation-wide 1km grid, with an
    estimated SWE value calculated for each cell. We monitor
    temperature and additional snowfall from info from the
    supercomputers in Boulder and modify our model each hour.
    We tend to trust SWE reports more than Snow Depth as generally
    more reliable.

    These snow reports are used as a check for the model, after
    the fact. Each day we compare the new reports to our model
    predictions. If there are enough reports of difference
    centered in an area, e.g. Colorado, we’ll re-run the model
    on that section, using selected reports as “ground truth”.

    This small piece runs much faster than the many hours needed
    to compute the whole USA. We patch the newly computed
    section into the nation-wide model — this is the “assimilation”
    process we refer to and the cause for many of the sudden
    jumps in the graphs on our web pages.

    The analysts here are familiar with the periodic problems
    of these sensors. Like genial small-town newspaper publishers
    looking over today’s pile of “Letters to the Editor”, they
    will ignore unjustifiably extreme values and wait for the
    sensor to settle down and start reporting realistic values.

    Tim Szeliga
    National Operational Hydrologic Remote Sensing Center (NOHRSC)
    National Weather Service, NOAA

    Your case is different though maybe the same. Have you tried emailing to ask for the answer to your question? Might get an informative reply.

    Regards,
    W^3

  18. David A says:

    E.M. says…” It is important to have skills in Grubering even inside the workplace. Whatever the boss wants, or believes, is reality. NEVER say something isn’t going to work, and CERTAINLY never be right about it. Hard to keep that in mind while being honest and non-cynical
    ====================================================================
    Curious, and sadly so true from my experience with a fortune 500 company. If I had been management I would have been fired several times. I was Union but was essentially management, hiring 5 to 150 people per day, depending on our labor needs, while planning the logistics and set up requirements of large trade shows. (I only had two years of collage before doing this for a career, so no real education compliments)

    However I knew this industry very well, and helped design and implement national best practice policies that saved millions annually. Our labor to revenue ratio was the lowest by far compared to our 30 plus other satellite offices)

    As technology grew throughout the 70s – the 90s, it enabled communication in many ways, but also enabled ever more central national control, and less autonomy for individual satellite operations, like my San Diego location. As our national company hired ever more Ivy League MBA’s and rotated new CEO.s every five or so years, they had to make their mark with the latest buzz words used. The net affect was to almost level our profits to the others, while lowering national profit margins. (we all became more equal, to a lower common denominator.)

    These educated folk would come to our office with their latest idea. As one example they wanted to implement a time keeping process called KRONOS. It could have had uses but what they wanted required a major increase in labor, beyond the costs. (about 2 %) I explained this in detail to the gentleman in charge He told me, “but we have ghost workers (high seniority union journeyman, paid, but not at work) in towns like New York, and Philadelphia. This will help control that.” I told him I had traveled to those venues, and knew of the problems.

    I explained that people mange people, not systems, and the ghost worker problems were a people problem, that a system would not solve. I then asked for the his example “KRONOS” badge. He handed it to me and I told him to go take a vacation for a week, come back and collect his pay check. He looked quizzical, but a bit confused. I explained that this system had made the Ghost Worker problem worse Now all I had to do was scan his card, and all was done by the computer program, and I once again emphasized, people mange people.

    Long story short, this system was implemented at over a million dollars in cost, and dropped one year later. This happened with three other national programs. I wrote up detailed reports of the problems I expected. The were ignored, programs implemented, then dropped. Money wasted, Ivy league CEOs, and Ivy League MBAs then moved on to other companies at high salaries. (The corporate royalty of the US)

    Had I been management I would have been released a long time before this. My position as Union actually protected me, although the Union resented our labor efficiency, no matter how I tried to explain to them that we, the workers, not our union representatives, were successfully negotiating our contract every day by our efficiency on the convention floor.

    (I am glad to be retired now)

  19. p.g.sharrow says:

    @EMSmith; I have no idea how wonderful your “Music” post IS, because I can’t load it! Just too much for my old machine. pg

  20. E.M.Smith says:

    @P.G.: More likely it’s the slow link, not the computer… Datacom is an order of magnetude or two slower / more limiting than the box…

    @David A.:

    I’ve seen a lot of similar things and heard a lot of similar stories. I grew up working in a family business (literally! helped my Dad build the restaurant at 7 years old and worked in it after that) and just have it in my DNA to make sure things are run for most profit… No profit, no dinner…

    Oh well.

    @W.W.Wygart:

    While it might be interesting to get the answer (most likely sensor died high) I don’t have the time right now. Have to find a job…

    I find the explanation you got interesting. To me it seems to say “Our data gathering is crap so we use computer models for most of it and for a Q.A. screen, but do put in some real data if it conforms to our belief system.” Not exactly what I’d call a quality data program…

    @Larry:

    I think I need to buy you a beer ;-) Sounds like you could use one …

  21. Larry Ledwick says:

    @EM if you ever find your way to Colorado please let me know and maybe I can take you up on that. ;-)

Comments are closed.