Using FAO NewClim to Build Climatologies Primary attempt – May a. verdin 08/20/2010

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)


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



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



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…


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…


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)


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


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


• Mean Absolute Error = 2.01 • R2 = 0.8906• RASTERVALU = predicted Sunshine Gradient values

FAO Sunshine Gradient – Station Representation


• 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


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


• Mean Absolute Error = 0.9831 • R2 = 0.836• RASTERVALU = predicted Windspeed values

FAO Windspeed – Station Representation


• Mean Absolute Error = 0.784924 • R2 = 0.936• RASTERVALU = predicted Windspeed values

FAO Windspeed – Cross-ValidationFIRST ATTEMPT…


FAO Windspeed – Cross-ValidationSECOND ATTEMPT…


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

Documents

Using FAO NewClim to Build Climatologies Primary attempt – May a. verdin 08/20/2010