1. Use of Climatological Downscaling for Evaluation of Satellite-based Precipitation Analyses over the Continental U.S. Matthew Garcia, UMBC-GEST and NASA-GSFC Christa D. Peters-Lidard, NASA-GSFC John Eylander, AFWA-A8TM Chris Daly, OSU PRISM Group 4 December 2007 Workshop on the Evaluation of High Resolution Precipitation Products World Meteorological Organization Geneva, Switzerland

2. Topography, Soils Land Cover, Vegetation Properties Meteorological Forecasts, Analyses, and/or Observations Snow Soil Moisture Temperature Land Surface Models Data Assimilation Modules Soil Moisture & Temperature Evaporation Sens. Heat Fluxes Runoff Snowpack Properties Inputs Outputs Physics Apps NASA-GSFC Land Information System Weather/ Climate Natural Hazards Agriculture Air Quality Water Resources Homeland Security Military Ops

3. 25km 5km1km The Value of 1-km Land Surface Modeling: Exploit EOS and NPOESS

4. The Parameter-elevation Regressions on Independent Slopes Model (PRISM; Daly et al. 1994, J. Appl. Meteor.) is a knowledge-based system (KBS; Daly et al. 2002, Clim. Res.) used to generate estimates of climate parameters, e.g. T, T d, precip. The PRISM KBS accounts for spatial variations due to: Physiography, including elevation, orientation, and profilePhysiography, including elevation, orientation, and profile Moisture regime, using an orographic trajectory modelMoisture regime, using an orographic trajectory model Coastal proximity, using a coastal wind infiltration modelCoastal proximity, using a coastal wind infiltration model Topographic position, in the occurrence of inversionsTopographic position, in the occurrence of inversions Use of PRISM climatology and analysis dataset

5. Given fields: (1) (2) Aggregate climatology to resolution of observations (1)  (3), at 0.25-degree resolution Find ratio of observations to aggregated climatology field (2) / (3) = (4), at 0.25-degree resolution Downscaling Procedure (ex: TRMM 3B42, CA-NV, May 2005) 0.25-degree resolution4-km resolution

6. Interpolate ratio field to resolution of climatology: inverse-distance-cubed weighting (IDW-3) for interpolation, after Garcia et al. (2008, Wat. Resour. Res.) (4)  (5), at 4-km resolution Multiply high-resolution ratio field by climatology (1) x (5) = (6), at 4-km resolution Downscaling Procedure (example, continued)

7. Regional results: TRMM 3B42 to 4-km resolution 0.25-degree resolution4-km resolution

8. Local results: TRMM 3B42 to 800-m resolution 0.25-degree resolution800-m resolution http://hmt.noaa.gov/figs/fig1.html

9. Local results: CMORPH to 800-m resolution 0.25-degree resolution800-m resolution http://hmt.noaa.gov/figs/fig1.html

10. Downscaling of USAF AGRMET Precipitation Analyses Evaluation Regions CONUS = Conterminous US NW = Pacific Northwest CN = California/Nevada NR = Northern Rocky Mountains SR = Southern Rocky Mountains NP = Northern Great Plains SP = Southern Great Plains NC = Northern Central US LM = Lower Mississippi River NE = Northeast US MA = Mid-Atlantic US SE = Southeast US CONUS topography (m) Surface GTS gauges, Geo-IR, DMSP and NOAA  wave, surface weather obs 0.5-degree spatial resolution, monthly total precipitation, 2004-2006 Simple interpolation vs. climatological downscaling Evaluation against PRISM analyses at 4-km spatial resolution

11. Interpolation vs. Downscaling Results

12. Bias improvement by ~12 mm, MAE by 11.5 mm

13. Interpolation vs. Downscaling Results Bias improvement by ~7 mm, MAE by ~6 mm

14. Interpolation vs. Downscaling Results MAE improvement by ~4 mm, RMSE by ~8 mm, R 2 by 0.12 (~25%)

15. Conclusions Climatological downscaling leads to some improvements over simple interpolation, but cannot correct for inherent biases and errors. Input (AGRMET ~48-km fields) demonstrates seasonal cycles in bias and error: positive bias and large errors (MAE and RMSE) in summer for areas with convective storm climate; small positive bias in winter for all of US with large errors where rain or mixed rain/snow climate dominates. Downscaling using the PRISM-derived monthly climatological dataset produces generally better high-resolution fields (in terms of bias, MAE, RMSE, and spatial correlation) than a simple interpolation method in wet seasons and in regions of high relief such as the Pacific Northwest, Sierra Nevada range, and Rocky Mountains. Climatological downscaling in regions of the U.S. east of the Rocky Mountains produces widely mixed results, and its utility remains uncertain.

16. Findings relevant to HRPP Workshop Goals 1.Recommendations to IPWG and IGWCO Agreement on “truth” for evaluation of product accuracy (GPCP-1DD may be superseded in some areas, esp. the U.S.) Error and uncertainty information disseminated with precipitation products (with recognition that this is an evolving aspect of the product) Routine development and revision of climatology products 2. PEHRPP linkages to international activities USAF interest is not the U.S. (CONUS is just a test area) EOS, GPM, NPOESS, geostationary platforms Global hazard forecasts and warnings 3. Continuation and direction of PEHRPP Catalogs of available products and evaluation results Standard evaluation metrics and procedures Use of physical downscaling in algorithm development Use of interpolation in multi-sensor product development