1. Geostatistical Mapping of Mountain Precipitation Incorporating Auto-searched Effects of Terrain and Climatic Characteristics Huade Guan, John L. Wilson, Oleg Makhnin New Mexico Institute of Mining and Technology American Meteorological Society 85 th Annual Meeting San Diego, Jan. 11, 2005

2. Why use gauge data for precipitation mapping in mountains? Problems with NEXRAD –Beam blockage –Snow estimation –4km pixel size NEXRAD rainfall New Mexico, July 1999 From Hongjie Xie, 2004

3. Four types of mapping approaches Information incorporated Spatial covariance NoYes Physical process No Theissen polygon, & inverse square distance Kriging Yes Regression, e.g., P-Z Cokriging (P-Z) (examples) Cokriging (P-Z) & De-trended residual kriging today

4. Physical process (1) Orographic effects on precip. P (low Z) < P( high Z) T wind T Elevation (Z) P (windward) > P( leeward) Orographic lifting, & hindranceReduction in virga effect P (low Z) < P( high Z) We use cos (α-ω) to approximate terrain aspect effects wind direction: ω terrain aspect: α terrain aspect

5. Physical process (2) Atmospheric effects on precipitation How does this heterogeneous atmospheric moisture distribution (or gradient in atmospheric moisture) influence precipitation? We use geographic coordinates (Longitude or X, and Latitude or Y) to capture the effect of gradient in atmospheric moisture on precipitation GOES East 4-km, infrared imagery 2001.05.04 Study area

6. Auto-search orographic and atmospheric effects gradient in moist., elevation, aspect & moist. flux direct. Data: Gauge precip: X, Y, P ; Elev. DEM: X, Y, Z, ; Regression: But what about moisture flux direction, ? aspect moist. flux dir.

7. Auto-search orographic and atmospheric effects where b 5 =b 4 cosω, and b 6 =b 4 sinω, implicitly contain the moisture flux direction. And b 1 and b 2 include the information of gradient in atmospheric moisture. Regression turns to: For example, if b 5 >0 and b 6 >0, ω= atan (b 6 /b 5 ) Similarly, if b 1 >0 and b 2 >0, gradient in atmospheric moisture, or the wetter direction = atan (b 1 /b 2 )

8. ASOADeK Auto-Searched Orographic and Atmospheric effects De-trended Kriging Auto-determine moisture gradient, elevation, & moisture direction effects via regressions Included in b 0, b 1, b 2, b 3, b 5, and b 6. Construct regression map from DEM Find residual at each gauge Generate residual (or de-trended) map by kriging Construct the final precipitation map Regression map + residual map

9. Study areas

10. ASOADeK regression improves estimates aspect + moisture flux direction moisture gradient

11. M Moisture flux direction 1 1213 2198 3186 4180 5174 6136 7156 8172 9 10182 11180 12191 ASOADeK inferred moisture flux directions January April JulyNovember Winter: Southwesterly Summer: Southeasterly

12. Two weather patterns in Summer Southwesterly moisture flux related North American Monsoon (picture to the right) Easterly moisture flux ASOADeK: Southeasterly From NOAA Mixture of the two may give apparent southeasterly moisture flux as inferred from ASOADeK

13. Weather pattern related to heaviest winter precipitation Southwesterly moisture flux at the study area, ASOADeK: Southwesterly From Sellers and Hill, 1974

14. M Moisture gradient 1298 2295 3279 460 586 6121 7134 8145 9154 10195 11281 12273 ASOADeK inferred gradient in atm. moisture

15. ASOADeK regression vs. PRISM Model estimates from both models v. measured values Scatter plots and fits (R 2 ) –For three months: Feb, May & Aug. ASOADek regression only! –No residual kriging

16. horizontal axis: observation values

17. ASOADeK vs. PRISM Precipitation maps for both models, and QQ plots, for same three months: Feb, May & Aug. ASOADek regression plus residual map. For ASOADeK lets now include the residual map

18. ASOADeK estimates vs. PRISM

20. Cross validation results: ASOADeK gives better estimates than kriging & cokriging

21. Conclusions ASOADeK detects regional climate settings using only precip. gauge data in mountainous terrain. ASOADeK vs. PRISM –Precipitation maps: ASOADeK PRISM –ASOADeK product has higher spatial resolution ASOADeK vs. other geostat. approaches –Precipitation estimates improved in comparison with krigng and co-kriging.

22. Future work Further testing ASOADeK auto-searching capacity –Event cases –Other geographic regions Applications & Extensions of ASOADeK –Mapping Precipitation in mountainous regions –Studying ENSO/PDO effects on precipitation distribution –Recovering NEXRAD beam-blockage shadow –Downscaling precip. products, e.g., NEXRAD

23. ain Thank you