Outline

1. The TMPA

2. Climate-Oriented Indices of “Extreme”

3. 40°N-S: Results and Issues

4. Status

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/GSFC2: Science Systems and Applications, Inc.3: Univ. of Maryland/ESSIC

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’s precip than we did in 1998!

1998 2000 2004

TMI,PR

2002

SSM/I F14

SSM/I F13

SSM/I F15

AMSU-B N16

AMSU-B N15

AMSU-B N17

AMSR-E

CPC Merged IR

GPCP IR Histograms

2006

SSMIS F16

MHS N18

MHS MetOp

SSMIS F17

2008

Instant-aneousSSM/ITRMMAMSRAMSU

30-day HQ coefficients

3-hourly merged HQ

3-hourly IR Tb

Hourly HQ-calib IR precip

3-hourly multi-satellite (MS)

Monthly gauges

Monthly SG

Rescale 3-hourly MS to monthly SG

Rescaled 3-hourly MS

Calibrate High-Quality (HQ) Estimates to

“Best”

Merge HQ Estimates

Match IR and HQ, generate coeffs

Apply IR coefficients

Merge IR, merged HQ estimates

Compute monthly satellite-gauge

combination (SG)

30-day IR coefficients

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 combinationwith gauge analysis

- rescale 3-hr precip to sumto the monthly sat-gaugecombination

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

2. CLIMATE-ORIENTED INDICES OF “EXTREME” - Selecting Comparisons

17. Rx1day, Monthly maximum 1-day precipitation18. Rx5day, Monthly maximum consecutive 5-day precipitation19. SDII, Simple precipitation intensity index20. R10mm, Annual count of days when PRCP 10mm21. R20mm, Annual count of days when PRCP 20mm22. Rnnmm, Annual count of days when PRCP nnmm23. CDD, Maximum length of dry spell24. CDW, Maximum length of wet spell25. R95pTOT, Annual total PRCP when RR > 95p26. R99pTOT, Annual total PRCP when RR > 99p27. 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

3. RESULTS – Quasi-global TMPA 1998-2007 “Climatology”

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

PRCP (mm/d)

R95p (mm/d)

3. RESULTS – Quasi-global TMPA 1998-2006 “Climatology” (cont.)

PRCP (mm/d)

CDDavg (days)100 200 300 400 500+

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

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

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

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)

3. RESULTS – PRCPTOT Comparison for 747 Stations Around Globe (cont.)

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

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

3. RESULTS – PRCPTOT Comparison for 36 Stations in Argentina

- 1998-2003

- comparing single stations to 0.25° grid boxes

- note consistent overall relationship, spread

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

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

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

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

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

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

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

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.govhttp://trmm.gsfc.nasa.gov

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)

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

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

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

Gauge Source

Number of

comparisons

Avg. Observed

Precipitation

Bias

(3B43-obs.)

RMS

Difference Atolls 1572 7.27 -1.21 (-16.6%) 3.10 (42.4%) All Buoys 1021 3.87 -0.29 (-7.5%) 1.95 (50.4%) W. Pac. Buoys 323 7.26 -0.16 (-2.2%) 2.34 (32.2%) E. Pac. Buoys 698 2.32 -0.35 (-15.2%) 1.74 (75.0%) KWAJ 504 5.40 -0.99 (-18.4%) 1.94 (36.0%) MELB land 441 3.26 +0.23 (+7.1%) 1.17 (35.4%)

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