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

2. 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

3. FAO Precipitation – May grid

4. FAO Precip – Station Distribution

5. FAO Precip – Station Representation Mean Absolute Error = 3.821596308 mm R2 = 0.9771 RASTERVALU = predicted precip

6. 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

7. 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

8. FAO Min Temp – May grid

9. FAO Min Temp – Station Distribution

10. FAO Min Temp – Station Representation Mean Absolute Error = 0.684382872 deg. C R2 = 0.9796 RASTERVALU = predicted min temp

11. 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

12. 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

13. FAO Max Temp – May grid

14. FAO Max Temp – Station Distribution

15. FAO Max Temp – Station Representation Mean Absolute Error = 0.6935 deg. Celsius R2 = 0.9785 RASTERVALU = predicted max. temp

16. 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

17. 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

18. FAO Mean Temp – May grid

19. FAO Mean Temp – Station Distribution

20. FAO Mean Temp – Station Representation Mean Absolute Error = 0.6654 degrees Celsius R2 = 0.9836 RASTERVALU = predicted mean temp

21. 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

22. 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

23. FAO PET – May grid (FIRST ATTEMPT…) Even just visually analyzing this interpolation causes worry… Statistics agree – REDO!

24. FAO PET – May grid (SECOND ATTEMPT…) Visually speaking, this looks much better. Let us compare the statistics, just to be certain!

25. FAO PET – Station Distribution The sparseness of information led to an increase in dmax & IDW values in the second attempt… …Statistics to come… NOW!

26. 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

27. 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

28. FAO PET – Cross-Validation FIRST 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….

29. FAO PET – Cross-Validation SECOND 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

30. 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

31. FAO hPa – May grid

32. FAO hPa – Station Distribution Looks a little sparse as well… Let’s see how the statistics hold up.

33. FAO hPa – Station Representation Mean Absolute Error = 0.57746 R2 = 0.9698 RASTERVALU = predicted hPa (water vapor pressure) values

34. 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. :)

35. 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

36. FAO Sunshine Gradient – May grid FIRST ATTEMPT… Even just visually analyzing this interpolation causes worry… Statistics agree – REDO!

37. FAO Sunshine Gradient – May grid SECOND ATTEMPT… Visually speaking, this looks much better. Let us compare the statistics, just to be certain!

38. FAO Sunshine Gradient – Station Distribution Hmm… The distribution looks about as sparse as our PET stations… Larger dmax & IDW will fix this as well?

39. 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

40. 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

41. FAO Sunshine Gradient – Cross-Validation FIRST 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….

42. FAO Sunshine Gradient – Cross-Validation SECOND 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…

43. 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

44. FAO Windspeed – May grid FIRST ATTEMPT… Things seem to be just a little … “off”

45. FAO Windspeed – May grid SECOND ATTEMPT… Overall, a better-looking spatial spread of information….

46. FAO Windspeed – Station Distribution

47. 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

48. 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

49. FAO Windspeed – Cross-Validation FIRST 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

50. FAO Windspeed – Cross-Validation SECOND 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

51. 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.

52. End…? a. verdin 08/20/2010