1. Combining CMORPH with Gauge Analysis over 2010.05.20.

2. Objective: To report our recent progress on the development of a new technique –to remove bias in the CMORPH high-resolution precipitation estimates over land on daily time scale and –to combine the bias corrected CMORPH with gauge analysis

3. Data: CMORPH: –Currently operational version CMORPH –2000 – 2009 (in process of backward extension to Jan.1998) –Integrated to 0.25 o lat/lon / daily Gauge Data –CPC Unified Global Daily Gauge Analysis –Interpolation of QCed gauge reports from ~30K stations –1979 – present –Integrated to 0.25 o lat/lon from its original grid of 0.125 o lat/lon resolution –Analysis released on 0.5 o lat/lon grid over globe and 0.25 o lat/lon over CONUS

4. CMORPH Bias [1] Global Distribution 2000-2009 10-yr annual mean precip CMORPH captures the spatial distribution patterns very well BIAS exists –Over-estimates over tropical / sub-tropical areas –Under-estimates over mid- and hi-latitudes

5. CMORPH Bias [2] Time Scales of the Bias Bias over CONUS Bias presents substantial variations of –seasonal (top), –sub-monthly (middle), and –year-to-year (bottom) time scales

6. CMORPH Bias [3] Range Dependence Bias as a function of CMORPH Rainfall Intensity over CONUS Bias exhibits strong range dependence

7. Bias Correction [1] General Strategy Seasonal / Year-to-year variations in bias  correction coefficients change with time Sub-monthly variations in bias  against sub-monthly gauge data Range-dependence in bias  PDF matching

8. Bias Correction [2] Conceptual Model for Daily Precip. Over China Principal –Match the PDF of the CMORPH against that of daily gauge to define and remove the bias, assuming PDF of the gauge analysis represents that of the truth Implementation –Collect co-located pairs of gauge and CMORPH over grid boxes with >=1 reporting stations within a spatial window centering at the target grid box and a time period ending at the target date (30-day); –A minimum of 300 pairs to ensure stability of PDFs –Define PDF for the CMORPH and gauge analysis

9. Bias Correction [3] Cross-Validation Results Over China CMORPHBias (%)Correlation Original-9.7%0.706 Adjusted-0.0%0.785

10. Bias Correction [4] Spatial Patterns of Remaining Biases  bias patterns caused by large spatial domain required to collect data pairs of sufficient number

11. Bias Correction [5] Global Implementation for Daily Bias Correction Step 1: Correction using Historical Data –Establish PDF matching tables for each 0.25 o lat/lon grid for each calendar date using data over nearby regions and over a period of +/- 15 days centering at the target date Step 2: Correction using Real-Time Data –Perform PDF matching using data over a 30-day period ending at the target date Step 3: Combining Results from HIS/RT –Linear combination with weights inversely proportional to the error variance

12. Bias Correction [6] Correction Using Real-Time Gauge Data Data pairs collected from a very large domain over gauge sparse areas (e.g. Africa)

13. Bias Correction [7] Correction Using Historical Gauge Data Spatial patterns of remaining bias much smaller

14. Bias Correction [8] Defining Error for Bias-Corr. CMORPH Using HIS/RT Data Assuming error variance proportional to the rainfall intensity and inversely proportional to the size of data collecting domain Determining the coefficients using real data over the 10-yr period

15. Bias Correction [9] Bias Corrected CMORPG Using HIS/RT Data Bias corrected CMORPH with estimated error

16. Performance [1] 10-yr Mean Annual Mean Bias

17. Performance [2] Remaining Bias & Gauge Network Remaining ‘bias’ appears over gauge sparse regions  Less desirable correction due to large data collection domain Poor quality in the gauge analysis

18. Performance [3] Correlation of Daily Precip. Over the 10-yr Period

19. Performance [4] Comparison over the Entire Global Land

20. Performance [5] Comparison over Africa

21. Performance [6] Comparison over CONUS

22. Performance [7] PDF over Africa

23. Combining Gauge and CMORPH [ 1 ] Combining Bias-corrected Satellite Estimates with Daily gauge over the Several Regions –This is only possible for several regions due to different daily ending time in the gauge reports Africa (06Z) CONUS/MEX (12Z) S. America(12Z) Australia(00Z) China(00Z) –Combining the bias-corrected CMORPH with gauge observations through the Optimal Interpolation (OI) over selected regions where gauge observations have the same daily ending time in which the CMORPH and gauge data are used as the first guess and observations, respectively

24. Combining Gauge and CMORPH [ 2 ] Example over China

25. Summary Prototype algorithm is developed and test products are constructed for the gauge-satellite merged global precipitation analyses Two sets of gauge-satellite precipitation analyses Bias-corrected Satellite Estimates Global 8kmx8km; 30-min 1998 to the present Gauge-satellite combined analyses Regional 0.25 o lat/lon; daily 1998 to the present Unified gauge – satellite precipitation analyses useful for climate monitoring, model verifications, hydrological studies, et al.