1. Errors in Satellite-based Precipitation Estimates Over Land
Christa D. Peters-Lidard
Physical Scientist and Head, Hydrological Sciences Branch
NASA/ Goddard Space Flight Center (GSFC), Code 614.3,
Greenbelt, MD 20771
Yudong Tian
UMBC-GEST and NASA-GSFC
John Eylander
Air & Space Models Integration Branch, HQ Air Force Weather Agency
Matthew Garcia
Jing “Lily” Zeng
ERT and NASA-GSFC

2. Objectives
Evaluate satellite rainfall products for land data assimilation applications (e.g., LIS; Kumar et al., 2006; Peters-Lidard et al., 2007)
Extend the work by Gottschalck et al. (2005) and Ebert et al. (2007) to
Include gauge-corrected 3B42, CMORPH & AGRMET; and
Extend the study period from 1 year (2002) to 3 years ( ).
Revisit the CONUS-scale results, and then focus on a smaller scale domain, namely, the Southeast U. S. (SE-US), where TRMM coverage is maximized and the effects of topography are minimized;
Examine performance on sub-daily time scales enabled by their >3 hour temporal resolution

3. Satellite-based products
TRMM 3B42V6: (Huffman 1997; Huffman et al. 2001, 2004)
CMORPH: (Joyce et al. 2004)
AFWA/AGRMET: U.S. Air Force Weather Agency
Agricultural Meteorology model. GEO-IR+SSM/I+Gauge
References
Tian Y., C. D. Peters-Lidard (2007), Systematic anomalies over inland water bodies in satellite-based precipitation estimates, Geophys. Res. Lett., 34, L14403, doi: /2007GL
Tian Y., C. D. Peters-Lidard, B. Choudhury, and M. Garcia (2007), Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications, J. Hydromet, Vol. 8, No. 6, December 2007, 1165–1183.

4. JJA05 POD
TRMM 3B42V6
CMORPH
AFWA AGRMET

5. JJA05 FAR
TRMM 3B42V6
CMORPH
AFWA AGRMET

6. Bias Time Series US-East
TRMM 3B42V6
CMORPH
AFWA AGRMET

7. Bias Time Series US-West
TRMM 3B42V6
CMORPH
AFWA AGRMET

8. Water bodies and FAR Pixels with sub-grid water bodies
Tian and Peters-Lidard,
GRL, 2007

9. Water bodies and PDFs TRMM 3B42V6 CMORPH Stage IV Inland Bodies Water
Only
Difference
Tian and Peters-Lidard, GRL, 2007

10. Multitemporal RMSE
Vs. Stage IV
Vs. Higgins

11. Precipitation PDFS
Precipitating Days
Precipitation Amount

12. Recommendations for IPWG
Bias and error scores important “baseline” but POD/FAR and PDF more relevant for applications.
Land retrievals need focused attention.
Systematic shifts in precipitation PDFs will have significant impacts on surface runoff production.
Integrate Bayesian retrievals, ensemble KF merging techniques, models+DA and hydrologic applications
Constrain applications using multiple data types

13. Backup

14. Summertime Diurnal Cycles

15. SEUS Skill Statistics vs. Time
RMSE
Bias
Correlation
POD