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Huade Guan, John L. Wilson , Oleg Makhnin New Mexico Institute of Mining and Technology

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

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Huade Guan, John L. Wilson , Oleg Makhnin New Mexico Institute of Mining and Technology

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  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 85th 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 rainfallNew Mexico, July 1999 From Hongjie Xie, 2004

  3. today Four types of mapping approaches (examples) Cokriging (P-Z)& De-trended residual kriging

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

  5. Study area 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

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

  7. Auto-search orographic and atmospheric effects Regression turns to: where b5=b4cosω, and b6=b4 sinω, implicitly contain the moisture flux direction. And b1 and b2include the information of gradient in atmospheric moisture. • For example, if b5 >0 and b6 >0, ω= atan (b6/b5) • Similarly, if b1 >0 and b2 >0, gradient in atmospheric moisture, or the wetter direction = atan (b1/b2)

  8. ASOADeKAuto-Searched Orographic and Atmospheric effects De-trended Kriging • Auto-determine moisture gradient, elevation, & moisture direction effects via regressions • Included in b0, b1, b2, b3, b5, and b6. • 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. ASOADeK inferred moisture flux directions November January April July 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. From Sellers and Hill, 1974 Weather pattern related to heaviest winter precipitation Southwesterly moisture flux at the study area, ASOADeK: Southwesterly

  14. ASOADeK inferred gradient in atm. moisture

  15. ASOADeK regression vs. PRISM • Model estimates from both models v. measured values • Scatter plots and fits (R2) • For three months: Feb, May & Aug. • ASOADek regression only! • No residual kriging

  16. horizontal axis: observation values

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

  18. ASOADeK estimates vs. PRISM

  19. Cross validation results:ASOADeK gives better estimatesthankriging &cokriging

  20. 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.

  21. 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

  22. ain Thank you！

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