Satellite-derived Rainfall Estimates over the Western U.S.: Fact or Fiction? John Janowiak

1. Satellite-derived Rainfall Estimates over the Western U.S.: Fact or Fiction? John Janowiak Bob Joyce Pingping Xie Phil Arkin Mingyue Chen Yelena Yarosh

2. OUTLINE • Brief review of IR & passive microwave info. • Describe “CMORPH” • Validation (US & Australia) • Simple gauge vs. satellite sampling study • A look at western US precip • Conclusions & on-going work

3. Surface Geostationary & Polar Infrared

4. Passive Microwave “Emission” • most physically direct • over ocean only • polar platform only Detects thermal emission from raindrops Surface

5. Upwelling radiation is scattered by ice particles in the tops of convective clouds Surface Upwelling radiation from Earth’s surface Passive Microwave “Scattering” • land & ocean • polar platform only Freezing Level

6. IR: greatsampling / provides poor estimate of rainfall MW: poor sampling / provides good estimate of rainfall >>>>> Combine them to meld strengths of each Others have done this – IR used to produce precip. estimate when MW data unavailable - Turk (NRL, Monterey), - Adler & Huffman (GSFC), - Gao, Hsu,Sarooshian (U. AZ)

7. 3-hr mosaic: good coverage but time of obs. varies by 3 hrs

8. “CMORPH” uses IR only as a transport vehicle. Underlying assumption is that error in using IR to transport percip. features is < error in using IR to estimate precip. CPC Morphing Technique “CMORPH” Spatial Grid: 0.0728o lat/lon (8 km at equator) Temporal Res’n: 30 minutes Domain: Global (60o N - 60o S) Period of record: Dec. 2002 - present Bob Joyce! http://www.cpc.ncep.noaa.gov/products/janowiak/MW-precip_index.html Paper (Joyce et al.) submitted to J. Hydrometeor.

9. AMSU B (NOAA 15, 16, 17) “CMORPH” is NOT aprecipitation estimation technique but rather a technique that creates spatially & temporally complete information using existing precipitation products that are derived from passive microwave observations. ` At present, precipitation estimates are used from 3 sensor types on 7 platforms: SSM/I (DMSP 13, 14, 15) TMI (TRMM) Soon: AMSR (“ADEOS-II”) & AMSR-E (“Aqua”)

13. http://www.cpc.ncep.noaa.gov/products/janowiak/us_web.html

15. http://www.bom.gov.au/bmrc/wefor/staff/eee/SatRainVal/dailyval_dev.htmlhttp://www.bom.gov.au/bmrc/wefor/staff/eee/SatRainVal/dailyval_dev.html

16. Remote Sensing Errors & Limitations ERRORS: • Indirect estimates inferred radiometrically • Instrument calibration • Conversion from retrieval to rain rate (algo.) • Temporal sampling LIMITATIONS: • Measurements not temporally continuous • Depending on instrument only convective (“scattering’) precip. may be sensed

17. Raingauge Errors & Limitations ERRORS: • Wind & gauge exposure effects • Human element (time, accuracy) • - Automated (calibration, maintenance) • Biological contamination LIMITATIONS: • Representativeness of area (point value) • Spatially incomplete • Available frequency (daily, 6-hr)

18. Box Mean Precip: 0.15” Std. Dev. : 0.22 “ Min. precip : 0 Max. precip. : 0.95” 10 7 5 3 1 0 <0.10 .10-.30 .30-.50 > .50 1o x1o box in s-central TN (July14, 2003) Distribution of Rainfall by Amount

19. BIAS RMSECORR 1 gauge -0.08 0.03 0.87 4 gauge 0.00 0.01 0.97 Radar 0.00 0.02 0.96 Cmorph 0.08 0.04 0.91

20. 50% 40% 57% 62% 66% 70%

21. 44% Gauge 40%

22. : Design: - randomly assign precip to 169 locations (13 x 13 array) - 50% of locations have “0” precip. - Repeat for 1000 ‘days’ - Daily “truth” is the 169 value mean Synthetic Data Sampling Study Question: Are there situations when an estimate from satellite is ‘better’ for assessing area-mean precipitation than a measurement from gauge(s)?

23. : Assumptions: - gauge measurement is perfect - gauge values are totally representative of the area sampled by satellite ie. area avg. - multiple gauges in an area are distributed optimally

24. Approach (overly?) simplistic: - ‘real-world’ nonzero rainfall distribution characteristics not modeled - on average, the % of locations with rain over an area is < 50% used here - rainfall ‘generators’ exist that more nearly duplicate the statistics of actual rainfall over time-space - much work on aspects of this topic done in hydro. & satellite sampling communities (Bell et al. :1990, 1996, 2003)

25. Samples of synthetic precipitation within a 1o x 1o lat/lon box at satellite resolution Precip amounts of 0 to 1 chosen randomly; impose condition that 50% are = “0”

26. X 1o x 1o lat/lon box containing 169 satellite pixels

27. X X 2 Gauges

28. X X X 3 Gauges

29. X X X X 4 Gauges

30. X X X X X 5 Gauges

31. 9 Gauges X X X X X X X X X

32. 13 Gauges X X X X X X X X X X X X X

33. 25 Gauges X X X X X X X X X X X X X X X X X X X X X X X X X

34. 1 gauge 2 gauges 9 gauges Light blue: satellite with 200% positive bias Dark blue: satellite with 100% positive bias Green: satellite with 50% positive bias Red: satellite with 10% positive bias Time series of absolute error (1st 100 days)

35. 50% error Point of 50% error accumulation 9 5 3 1 Light blue: 9 gauges Dark blue: 5 gauges Green: 3 gauges Red: 1 gauge (90%) “Perfect” Gauge 10%/ error (satellite) 100% error 200% error

36. 1 gauge 2 gauges 9 gauges Light blue: sat.ellite with 0-200% pos. random error Dark blue: satellite with 0-100% pos. random error Green: satellite with 0- 50% pos. random error Red: satellite with 0-10% pos. random error Time series of absolute error

37. 9 5 3 1 Error at 50% point (90%) 0-10% error “Perfect” Gauge 0-50% error 0-100% error 0-200% error Light blue: 9 gauges Dark blue: 5 gauges Green: 3 gauges Red: 1 gauge

38. RMSE 0.251 satellite (200% + bias) 0.103 1 gauge ~10% of earth 0.063 satellite (100% + bias) 0.063 satellite (0-200% random) 0.054 2 gauges 0.033 3 gauges 0.023 4 gauges 0.021 5 gauges 0.016 satellite (50% + bias) 0.016 satellite (0-100% random) 0.011 9 gauges 0.009 13 gauges 0.009 satellite (0-50% random) 0.004 25 gauges 0.001 satellite (10% + bias)

39. Number of HADS/RFC stations per ¼ degree lat/lon grid box (9/10/2003)

40. “CAMS” - 2 or more gauges per 1o Grid “CAMS” - 1 or more gauges per 1o Grid 10% of earth (60N-60S); 29% of land 10% of earth (60N-60S) 29% of land area ( “ )

41. http://www.cpc.ncep.noaa.gov/products/janowiak/us_web.html

42. Crude RH Adjustment to CMORPH (Aug 2003) Scofield, 1987 Rosenfeld and Mintz (1988) McCollum et al. (2000)

43. CMORPH vs. gauge over ‘NAME’ zones

44. CMORPH with RH adjustment vs. gauge over ‘NAME’ zones

45. Evap. adjusted Evap. adjusted Time series of statistics over 9 NAME Zones

46. Conclusions Fact or Fiction? 1. CMORPH estimates compare quite favorably to raadar estimates over the US and to gauge analyses over the US and Australia. 2. Satellite estimates of rainfall can be useful over the western U.S. (and elsewhere) – perhaps better than gauge data in some situations 3. Many satellite techniques overestimate rainfall considerably in semi-arid regions during the warm season, but an RH adjustment is promising.

47. Refine & implement evaporation adjustment • Investigate use of model winds to advect rainfall • - more accurate results? • - allow reprocessing to early 1990’s • - reduce processing time substantially • Incorporate microwave rainfall estimates from new • instruments (AMSR, AMSR-E) 4. Investigate derivation of advection vectors from microwave data - temporal resolution to 10 minutes? Work in Progress

48. ‘0’ precip 0 < precip < 1 Long-term Box Mean = 0.25 So, 200% error = 0.500 100% error = 0.250 50% error = 0.125

49. Correlation with MW availability