Outline 1. The TMPA Climate-Oriented Indices of “Extreme” 40°N-S: Results and Issues 4. Status

1. Validation and Analysis of Precipitation Extremes in TMPA G.J. Huffman1,2, R.F. Adler1,3, D.T. Bolvin1,2 , E.J. Nelkin1,2 1: NASA/GSFC 2: Science Systems and Applications, Inc. 3: Univ. of Maryland/ESSIC Outline 1. The TMPA Climate-Oriented Indices of “Extreme” 40°N-S: Results and Issues 4. Status

2. 1998 2000 2008 2002 2006 2004 TMI,PR SSM/I F13 SSM/I F14 SSM/I F15 SSMIS F16 SSMIS F17 AMSR-E AMSU-B N15 AMSU-B N16 AMSU-B N17 MHS N18 MHS MetOp GPCP IR Histograms CPC Merged IR 1. TMPA – Data Sources A diverse, growing set of input precip estimates – various - periods of record - regions of coverage - sensor-specific strengths and limitations Seek the longest, most detailed record of “global“ precip Combine the input estimates into a “best” data set TRMM includes combinations as standard products - a joint mission of NASA and JAXA - heritage in Global Precipitation Climatology Project (GPCP) - we know more about 1998’sprecip than we did in 1998!

3. Calibrate High-Quality (HQ) Estimates to “Best” Instant-aneous SSM/I TRMM AMSR AMSU 30-day HQ coefficients Merge HQ Estimates 3-hourly merged HQ Match IR and HQ, generate coeffs 3-hourly IR Tb 30-day IR coefficients Apply IR coefficients Hourly HQ-calib IR precip Merge IR, merged HQ estimates 3-hourly multi-satellite (MS) Compute monthly satellite-gauge combination (SG) Monthly gauges Monthly SG Rescale 3-hourly MS to monthly SG Rescaled 3-hourly MS 1. TMPA – Combinations Both real-time and post-real-time, on a 3-hr 0.25° grid Microwave precip: - intercalibrate, combine IR precip: - calibrate with microwave Combined microwave/IR: - IR fills gaps in microwave Sat-gauge (post-RT only): - accumulate combined 3- hr precip for the month - weighted combination with gauge analysis - rescale 3-hr precip to sum to the monthly sat-gauge combination

4. 2. CLIMATE-ORIENTED INDICES OF “EXTREME” CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI) - address the objective measurement and characterization of climate variability and change - provide international coordination and help organize collaboration on climate change detection and indices relevant to climate change detection - encourage the comparison of modeled data and observations 27 “core indices” - 16 for temperature - 11 for precipitation - computed for (generally) multi-decade records for stations around the globe • 1960-1990 base period • posted at http://cccma.seos.uvic.ca/ETCCDI/data.shtml • climatologies not posted “Rainy Day” = 1 mm

5. 2. CLIMATE-ORIENTED INDICES OF “EXTREME” - Selecting Comparisons 17. Rx1day, Monthly maximum 1-day precipitation 18. Rx5day, Monthly maximum consecutive 5-day precipitation 19. SDII, Simple precipitation intensity index 20. R10mm, Annual count of days when PRCP  10mm 21. R20mm, Annual count of days when PRCP  20mm 22. Rnnmm, Annual count of days when PRCP  nnmm 23. CDD, Maximum length of dry spell 24. CDW, Maximum length of wet spell 25. R95pTOT, Annual total PRCP when RR > 95p 26. R99pTOT, Annual total PRCP when RR > 99p 27. PRCPTOT, Annual total precipitation Choices made to - represent dry and wet extremes - be less sensitive to artifacts • TMPA tends to under-represent light rain over land • 99th percentile and maximum values easily contaminated

6. 3. RESULTS – Quasi-global TMPA 1998-2007 “Climatology” PRCP (mm/d) R95p (mm/d) The patterns resemble each other, but there are important differences - flip-flop of PRCP and R95p in South America - extra R95p maximum southwest of Mexico

7. 100 200 300 400 500+ 3. RESULTS – Quasi-global TMPA 1998-2006 “Climatology” (cont.) PRCP (mm/d) CDDavg (days) The patterns are nearly inverses, but again with interesting differences - PRCP gradient moving south in SPCZ not reflected in CDDavg - blacked-out areas had rain events in less than half the years

8. 3. RESULTS – Station Distribution PRCP (mm/d) Stations had to have all data in 1998-2003 (6 years) and be in the band 40°N-S - coverage depends on contributing national organization - black dot is 0.25° box with at least one station; red is 3x3 “halo” - reasonable range of climate zones, but missing highest rain areas

9. 3. RESULTS – PRCPTOT Comparison for 747 Stations Around Globe - 1998-2003 - comparing single stations to 0.25° grid boxes • note consistent overall relationship, spread • note wild values (mostly high) Why are some values so high? • Identify all stations with • |Ts-Tg| / (Ts + Tg) > 0.4 • - plot their yearly values in red • - Most wild values are suppressed

10. 3. RESULTS – PRCPTOT Comparison for 747 Stations Around Globe (cont.) Bhutan is a mix of good and very bad difference • The 4 stations along the southern border have much higher gauge (orange) • • foothills of Himalayas • • no GPCC analysis gauges in high-rain band • • climatologies not posted • - 8 other stations are reasonable (green)

11. 3. RESULTS – Station Distribution, Take 2 PRCP (mm/d) Stations have all 6 years of data in 1998-2003 and be in the band 40°N-S - coverage depends on contributing national organization - black dot is 0.25° box with at least one station; red is 3x3 “halo” - reasonable range of climate zones, but missing highest rain areas Stations with normalized 6-yr difference > 40% have a yellow “halo” - mostly coastal, orographic, or dry

12. 3. RESULTS – PRCPTOT Comparison for 747 Stations Around Globe (cont.) • Global PRCPTOT comparison • - tests using data from Argentina, U.S. were good, not like this • gauge variables were computed individually by member countries • incompatibility between my software and some files caused an inconsistency in the years extracted between gauge and satellite data • more work underway • meanwhile, want to reiterate results in Argentina that are representative of other countries without the year problem

13. 3. RESULTS – PRCPTOT Comparison for 36 Stations in Argentina - 1998-2003 - comparing single stations to 0.25° grid boxes - note consistent overall relationship, spread

14. 3. RESULTS – PRCPTOT Comparison for 36 Stations in Argentina (cont.) • interannual correlations at individual stations generally high • stations with < 6 yr of data tend to deviate from general cloud of points • the 3 stations with CC<0.3 have suspect time series • remaining stations tend to show better CC for higher annual rainfall

15. 3. RESULTS – R95pTOT Comparison for 36 Stations in Argentina - 1998-2003 - comparing single stations to grid boxes • typically 2-4 days per year • - more spread due to less sampling - higher TMPA values at high end, perhaps due to lack of light precipitation

16. 3. RESULTS – R95pTOT Comparison for 36 Stations in Argentina (cont.) • lower correlations, as expected • stations with data problems tend to have lower correlations • rest of stations still show a trend towards higher correlation for higher R95p accumulations

17. 3. RESULTS – Consistency between Indices for 36 Stations in Argentina - scatter of R95pTOT against PRCPTOT for stations and TMPA - high, similar interannual correlations - as before, higher R95pTOT for TMPA

18. 3. RESULTS – CDD Comparison for 36 Stations in Argentina - 1998-2003 - comparing single stations to grid boxes - more spread due to less sampling, isolated events - TMPA tends to have longer runs of dry days, likely due to under-representing light precipitation over land

19. 3. RESULTS – CDD Comparison for 36 Stations in Argentina (cont.) • lower correlations, as expected • stations with data problems tend to have lower correlations • rest of stations show a trend towards lower correlation for higher CDD – fewer longer runs can be sampled in a year • but, a whole population of stations at the low end has serious disagreement

20. 3. RESULTS – Consistency between Indices for 36 Stations in Argentina - scatter of CDD against R95pTOT for stations and TMPA - larger spread at low rain totals likely reflects differences in seasonality - is there a rainy season or light precip year-round? - as before, higher R95pTOT for TMPA, so red points tend to extend to right at all values

21. 4. CONCLUDING REMARKS Satellite-based “high-resolution” precipitation datasets are being used to investigate extreme events We can use existing definitions of climate statistics to enhance communication Diverse origins of data sets means analysis software has to be tested country-by-country In first two tests, TMPA’s PRCPTOT, R95pTOT, CDD compare relatively well to gauge data in this study - level of interannual correlation at a location depends on relative range of interannual variation - TMPA-based R95pTOT and CDD both tend to be high - reflects under-representation of light precip in the TMPA over land - Think we can use large-scale variable (PRCPTOT) as marker for extremes quality george.j.huffman@nasa.gov http://precip.gsfc.nasa.gov http://trmm.gsfc.nasa.gov

23. 3. RESULTS – PRCPTOT Comparison for 747 Stations Around Globe (cont.) • bad results don’t match good results in testing with Argentina, U.S. • gauge data computed individually by member countries • • plotted results have China off by 1 year (red)

24. 3. RESULTS – PRCPTOT Comparison for 747 Stations Around Globe (cont.) • bad results don’t match good results in testing with Argentina, U.S. • gauge data computed individually by member countries • • plotted results have China off by 1 year (red) • Temporarily dropping bad match-ups gives a much cleaner picture

25. 1. TMPA - Implementation Composites of individual overpasses (i.e., “instantaneous”) 0.25°, 3-hourly - spatial scale > typical satellite pixel size - resolve diurnal cycle Intercalibrate microwave sensors with “TRMM Best” - TRMM Combined Inst. best vs. atolls over ocean in V.6 - TMI only choice in RT - apply histogram matching (no constraint on pattern) Non-trivial differences due to - time of observation - sensor characteristics

26. 2. RESULTS – 3-hourly Statistics Rain rate histograms at 0.25°, 3-hr look good compared to KWAJ radar 1999-2004 At the same time, the skill is still modest in most cases at 0.5°, daily against TAO/TRITON gauges 1998-2004 - tendency for better skill at high rates is fairly typical of such estimates

27. 2. RESULTS – Monthly Statistics (cont.) Monthly comparisons for V.6 3B43 at 0.5° - None of the gauges have wind adjustment - W. Pac. buoys and atolls are roughly comparable (note averages); we believe the buoys have much higher wind loss - KWAJ radar calibrated by gauge, and % bias is comparable to atolls - V.6 uses gauges, with wind correction, so the positive bias for MELB land is reasonable - RMS % difference lower for radar comparisons (which are area-average) - RMS % differences are higher for lighter rain