using fao newclim to build climatologies primary attempt – may a. verdin 08/20/2010
DESCRIPTION
FAO Precipitation – May gridTRANSCRIPT
Using FAO NewClim toBuild Climatologies
Primary attempt – May
a. verdin 08/20/2010
FAO - LTM Precipitation
• > 7300 locations within US/S.Canada/N.Mexico• Predictors used for interpolation:– LST (land surface temperature)– IRM (infrared mean)– IRS (infrared std. dev.)– DEM (digital elevation model)– CMORPH (satellite precip estimate)
• dmax(weighted centers radius; 1degree points): 550• IDW(inverse distance weighting) max integer: 12
FAO Precipitation – May grid
FAO Precip – Station Distribution
FAO Precip – Station Representation
• Mean Absolute Error = 3.821596308 mm• R2 = 0.9771• RASTERVALU = predicted precip
FAO Precip – Cross-Validation
• stn mean val: 79.702905634759 mm• pred. mean val: 80.423012923832 mm• 10th percentile for stn: 17 mm• 50th percentile for stn: 88 mm• 90th percentile for stn: 126 mm• 10th percentiles for pred: 21.85 mm• 50th percentiles for pred: 91.98 mm• 90th percentiles for pred: 120.76 mm• R2: 0.94465
FAO - LTM Minimum Temperature
• > 5100 locations within US/S.Canada/N.Mexico• Predictors used for interpolation:– LST (land surface temperature)– IRM (infrared mean)– IRS (infrared std. dev.)– DEM (digital elevation model)
• dmax(weighted centers radius; 1degree points): 550• IDW(inverse distance weighting) max integer: 12
FAO Min Temp – May grid
FAO Min Temp – Station Distribution
FAO Min Temp – Station Representation
• Mean Absolute Error = 0.684382872 deg. C• R2 = 0.9796• RASTERVALU = predicted min temp
FAO Min Temp – Cross-Validation
• stn mean val: 8.5629 degrees C• pred. mean val: 8.5332 degrees C• 10th percentile for stn: 2.7 degrees C• 50th percentile for stn: 8.1 degrees C• 90th percentile for stn: 15.3 degrees C• 10th percentiles for pred: 3.3838 degrees C• 50th percentiles for pred: 8.0999 degrees C• 90th percentiles for pred: 14.5393 degrees C• R2: 0.9425
FAO - LTM Maximum Temperature
• > 5100 locations within US/S.Canada/N.Mexico• Predictors used for interpolation:– LST (land surface temperature)– IRM (infrared mean)– IRS (infrared std. dev.)– DEM (digital elevation model)
• dmax(weighted centers radius; 1degree points): 550• IDW(inverse distance weighting) max integer: 12
FAO Max Temp – May grid
FAO Max Temp – Station Distribution
FAO Max Temp – Station Representation
• Mean Absolute Error = 0.6935 deg. Celsius• R2 = 0.9785• RASTERVALU = predicted max. temp
FAO Max Temp – Cross-Validation
• stn mean val: 22.8346 degrees• pred. mean val: 22.8443 degrees• 10th percentile for stn: 17.2 degrees• 50th percentile for stn: 22.5 degrees• 90th percentile for stn: 29.2 degrees• 10th percentiles for pred: 18.2273 degrees• 50th percentiles for pred: 22.4521 degrees• 90th percentiles for pred: 28.4061 degrees• R2: 0.9421
FAO - LTM Mean Temperature
• > 5300 locations within US/S.Canada/N.Mexico• Predictors used for interpolation:– LST (land surface temperature)– IRM (infrared mean)– IRS (infrared std. dev.)– DEM (digital elevation model)
• dmax(weighted centers radius; 1degree points): 550• IDW(inverse distance weighting) max integer: 12
FAO Mean Temp – May grid
FAO Mean Temp – Station Distribution
FAO Mean Temp – Station Representation
• Mean Absolute Error = 0.6654 degrees Celsius• R2 = 0.9836• RASTERVALU = predicted mean temp
FAO Mean Temp – Cross-Validation
• stn mean val: 15.8004 degrees• pred. mean val: 15.7848 degrees• 10th percentile for stn: 10.1 degrees• 50th percentile for stn: 15.3 degrees• 90th percentile for stn: 22.6 degrees• 10th percentiles for pred: 10.8643 degrees• 50th percentiles for pred: 15.3594 degrees• 90th percentiles for pred: 21.7689 degrees• R2: 0.9507
FAO PET (potential evapotranspiration)
• < 200 locations within US/S.Canada/N.Mexico• Predictors used for interpolation:
– LST (land surface temperature)– IRM (infrared mean)– IRS (infrared std. dev.)– DEM (digital elevation model)– CMORPH (satellite precip estimate)FIRST ATTEMPT…
• dmax(weighted centers radius; 1degree points): 550• IDW(inverse distance weighting) max integer: 12
SECOND ATTEMPT…• dmax(weighted centers radius; 1degree points): 750• IDW(inverse distance weighting) max integer: 20
FAO PET – May grid (FIRST ATTEMPT…)
Even just visually analyzing this interpolation causes worry… Statistics agree – REDO!
FAO PET – May grid (SECOND ATTEMPT…)
Visually speaking, this looks much better. Let us compare the statistics, just to be certain!
FAO PET – Station Distribution
The sparseness of information led to an increase in dmax & IDW values in the second attempt……Statistics to come… NOW!
FAO PET – Station Representation
• FIRST ATTEMPT… • As expected, the lack of information leads to a poor station representation..
• Mean Absolute Error = 8.941436 • R2 = 0.6935• RASTERVALU = predicted PET values
FAO PET – Station Representation
• SECOND ATTEMPT… • The increase in dmax & IDW values improve our station values… STILL CHECK CV!
• Mean Absolute Error = 3.427624 • R2 = 0.9845• RASTERVALU = predicted PET values
FAO PET – Cross-ValidationFIRST ATTEMPT…
• stn mean val: 126.5872• pred. mean val: 124.3854• 10th percentile for stn: 80.56• 50th percentile for stn: 127.2• 90th percentile for stn: 164.23• 10th percentiles for pred: 75.196• 50th percentiles for pred: 127.513• 90th percentiles for pred: 160.132• R2: 0.9389
***Judging solely on the cross-validation summary, we may be deceived into believing the first attempt is acceptable. We know the station representation fails… So let’s take a look at the SECOND ATTEMPT….
FAO PET – Cross-ValidationSECOND ATTEMPT…
• stn mean val: 126.5872• pred. mean val: 126.0917• 10th percentile for stn: 80.56• 50th percentile for stn: 127.2• 90th percentile for stn: 164.23• 10th percentiles for pred: 78.408• 50th percentiles for pred: 127.809• 90th percentiles for pred: 167.507• R2: 0.9598
FAO hPa (water vapor pressure)
• > 300 locations within US/S.Canada/N.Mexico• Predictors used for interpolation:– LST (land surface temperature)– IRM (infrared mean)– IRS (infrared std. dev.)– DEM (digital elevation model)– CMORPH (satellite precip estimate)
• dmax(weighted centers radius; 1degree points): 550• IDW(inverse distance weighting) max integer: 12
FAO hPa – May grid
FAO hPa – Station Distribution
Looks a little sparse as well… Let’s see how the statistics hold up.
FAO hPa – Station Representation
• Mean Absolute Error = 0.57746 • R2 = 0.9698• RASTERVALU = predicted hPa (water vapor pressure)
values
FAO hPa – Cross-Validation
• stn mean val: 10.927• pred. mean val: 10.728• 10th percentile for stn: 6.8• 50th percentile for stn: 10• 90th percentile for stn: 17.78• 10th percentiles for pred: 6.85• 50th percentiles for pred: 9.92• 90th percentiles for pred: 16.57• R2: 0.9228
So, the statistics hold up, although our station distribution may not be the best. :)
FAO Sunshine Gradient
• ~ 200 locations within US/S.Canada/N.Mexico• Predictors used for interpolation:
– LST (land surface temperature)– IRM (infrared mean)– IRS (infrared std. dev.)– DEM (digital elevation model)
FIRST ATTEMPT…• dmax(weighted centers radius; 1degree points): 550• IDW(inverse distance weighting) max integer: 12
SECOND ATTEMPT…• dmax(weighted centers radius; 1degree points): 750• IDW(inverse distance weighting) max integer: 20
FAO Sunshine Gradient – May gridFIRST ATTEMPT…
Even just visually analyzing this interpolation causes worry… Statistics agree – REDO!
FAO Sunshine Gradient – May gridSECOND ATTEMPT…
Visually speaking, this looks much better. Let us compare the statistics, just to be certain!
FAO Sunshine Gradient – Station Distribution
Hmm… The distribution looks about as sparse as our PET stations… Larger dmax & IDW will fix this as well?
FAO Sunshine Gradient – Station Representation
• FIRST ATTEMPT… • As expected, the lack of information leads to a poor station representation..
• Mean Absolute Error = 2.01 • R2 = 0.8906• RASTERVALU = predicted Sunshine Gradient values
FAO Sunshine Gradient – Station Representation
• SECOND ATTEMPT… • The increase in dmax & IDW values improve our station values… STILL CHECK CV!
• Mean Absolute Error = 1.63• R2 = 0.9235• RASTERVALU = predicted Sunshine Gradient values
FAO Sunshine Gradient – Cross-ValidationFIRST ATTEMPT…
• stn mean val: 59.276• pred. mean val: 58.517• 10th percentile for stn: 45• 50th percentile for stn: 60• 90th percentile for stn: 70• 10th percentiles for pred: 45.105• 50th percentiles for pred: 59.64• 90th percentiles for pred: 69.704• R2: 0.8589***Judging solely on the cross-validation summary, we may be deceived into believing
the first attempt is acceptable. We know the station representation fails… So let’s take a look at the SECOND ATTEMPT….
FAO Sunshine Gradient – Cross-ValidationSECOND ATTEMPT…
• stn mean val: 59.276• pred. mean val: 58.82• 10th percentile for stn: 45• 50th percentile for stn: 60• 90th percentile for stn: 70• 10th percentiles for pred: 45.66• 50th percentiles for pred: 59.525• 90th percentiles for pred: 69.605• R2: 0.9040
Lookin’ good…
FAO Windspeed
• > 300 locations within US/S.Canada/N.Mexico• Predictors used for interpolation:
– LST (land surface temperature)– IRM (infrared mean)– IRS (infrared std. dev.)
FIRST ATTEMPT…• dmax(weighted centers radius; 1degree points): 550• IDW(inverse distance weighting) max integer: 12
SECOND ATTEMPT…• dmax(weighted centers radius; 1degree points): 750• IDW(inverse distance weighting) max integer: 20
FAO Windspeed – May gridFIRST ATTEMPT…
Things seem to be just a little … “off”
FAO Windspeed – May gridSECOND ATTEMPT…
Overall, a better-looking spatial spread of information….
FAO Windspeed – Station Distribution
FAO Windspeed – Station Representation
• FIRST ATTEMPT… • As expected, the lack of information leads to a poor station representation..
• Mean Absolute Error = 0.9831 • R2 = 0.836• RASTERVALU = predicted Windspeed values
FAO Windspeed – Station Representation
• SECOND ATTEMPT… • The increase in dmax & IDW values improve our station values… STILL CHECK CV!
• Mean Absolute Error = 0.784924 • R2 = 0.936• RASTERVALU = predicted Windspeed values
FAO Windspeed – Cross-ValidationFIRST ATTEMPT…
• stn mean val: 15.357• pred. mean val: 15.328• 10th percentile for stn: 10.8• 50th percentile for stn: 15.48• 90th percentile for stn: 20.304• 10th percentiles for pred: 11.883• 50th percentiles for pred: 15.508• 90th percentiles for pred: 19.361• R2: 0.9110
FAO Windspeed – Cross-ValidationSECOND ATTEMPT…
• stn mean val: 15.357• pred. mean val: 15.375• 10th percentile for stn: 10.8• 50th percentile for stn: 15.48• 90th percentile for stn: 20.304• 10th percentiles for pred: 12.204• 50th percentiles for pred: 15.302• 90th percentiles for pred: 19.046• R2: 0.8685
Conclusions…• The large number of observations for the FAO temperature data along
with the strong cross-validation and station representation implies a good fit for these interpolations.
• FAO precip has the most observations for our area of interest, and the statistics imply a good fit
• Problems with the FAO PET seem to be stemmed from the sparseness of our observations, leading to highly skewed interpolation. A greater dmax value will result in more “neighboring” stations, and thus a finer end grid.
• The FAO water vapor pressure grid holds up in cross-validation and station representation. With not even 350 observations, this is surprising.
• The FAO sunshine gradient ALONG WITH the FAO windspeed would be better off with a different set of predictors. Neither LST, IR, CMORPH, nor elevation have a strong relationship with these worldly variables.
End…?
a. verdin 08/20/2010