Research and Operational Application of TRMM-Based, Fine Time Scale Precipitation Analyses

R.F. Adler1, G.J. Huffman1,2, D.T. Bolvin1,2, S. Curtis3, G. Gu1,4, E.J. Nelkin1,2

1: NASA/GSFC Laboratory for Atmospheres2: Science Systems and Applications, Inc.3: East Carolina University4: UMBC Goddard Earth Sciences and Technology Center

Outline

1. TRMM Status

2. The MPA

3. Examples

4. Planned Enhancements

1. TRMM STATUS – TRMM Version 5 Ocean Rainfall

Previous operational version for TRMM standard products

~20% range between radar and passive microwave ocean estimates

1. TRMM STATUS – TRMM Version 6 Ocean Rainfall Test Results (Not Final Versions)

~5% range between radar and passive microwave ocean estimates

Processing/reprocessing for Version 6 began in May 2004

May 1998

August 1998

1. TRMM STATUS – Future Operations

TRMM has evolved from an experimental mission into the core satellite for a constellation preceding and evolving into GPM.

PR, TMI, VIRS, LIS all working nominally; spacecraft systems in excellent shape.

In Spring 2004 NASA decided to end TRMM; however, outcry from science community resulted in continuation until end of 2004 and review by National Academy of Science (8-10 Nov.).

National Academy will recommend TRMM future based on value of data for science and operational forecasting for

1) TRMM ops. through 2005, with a controlled re-entry using134 kg of fuel; or

2) use of all 134 kg of fuel for science operation, which would extend potential mission life to ~2011; overlap with GPM possible (GPM launch in 2010).

Flight operations cost for TRMM: $4M/yr (full cost).

NASA safety organization does not require TRMM controlled re-entry.

2. THE MULTI-SATELLITE PRECIPITATION ANALYSIS (MPA)

TRMM by itself yields sparse coverage:TRMM PR (red)

+ TRMM TMI (cyan)

Long-term microwave record covers

~40% of 20°N-S in 3 hr: + SSM/I (3 sat.; yellow)

New microwave satellites raise + AMSR-E (blue)coverage to ~80% of 20°N-S in 3 hr: + AMSU-B (3 sat.; green)

IR 50°N-S, missing at higher latitudes (black)

Instant-aneousSSM/ITRMMAMSRAMSU

30-day HQ coefficients

3-hourly merged HQ

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

2. THE MPA – Flow Diagram

“Best” in HQ is TMI for real-time; TCI for non-real-time

Green shading done asyncin real time, trailing avg.

Blue shading only donenon-real time, adds value

Cyan boxes are inputs

Yellow boxes are calibration coefficients

Orange boxes are products

2. THE MPA – Detailed ExampleHurricane Isabel; microwave swaths in light grey

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3. EXAMPLES – Accumulation from Tropical Storms Affecting the U.S. Mainland in 2004

Order of appearance: Alex, Bonnie, Charlie, Gaston, Frances, Ivan, Jeanne, Matthew

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3. EXAMPLES – Near-real-time analysis of flooding event in Hispaniola, 22-25 May 2004

A minor low pressure tracked north, unleashing >450 mm of rain in south-central Hispaniola.

Kevin Laws (NOAA FEWS-Net) had reviewed the MPA-RT and CMORPH analyses by 12Z 26 May.

Hourly IR product

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3. EXAMPLES – Hispaniola flooding (cont.)

Further analysis showed the following;

• correctly alerted to a problem• amounts likely too high by a factor of ~2

Re-doing the IR calibration by including data from the event days gave a more reasonable answer.

3. EXAMPLES – Potential-Flood Monitoring Product

• simple thresholds for 1-, 3-, 7-day accumulations

• oceans are masked out• focuses attention on potential

problems• updated on the Web daily

7-day

3-day

1-day

Typhoon Tokage

• snapshots from 22 October 2004

• flooding and mudslides in western Japan

• 67 deaths, 21 missing• >23,000 dwellings

destroyed

3. EXAMPLES – Updates to the El Niño Onset Index (EOI) in Real Time

The basic index is a measure of the spectral power in the 30-60-day band of a wavelet analysis in the gradient of precipitation in the east-central Indian Ocean. [Gradient is taken as the difference of the averages over the two boxes shown below; white is low climatological precipitation, yellows and reds are high.]

The index is only considered (black) when the trailing 6-month-average gradient is positive (higher precip near Sumatra).

The EOI has correctly foreshadowed 6 of last 7 El Niño events with no false alarms.

The EOI is computed with the MPA, then with the GPCP Satellite-Gauge combination when available.

NINO3,4 ENSO Index EOI

3. EXAMPLES – West African Monsoon

The seasonal cycle of precipitation is shown, with zonal winds at three levels for comparison.

Latitudinal values are averaged over 10°W-10°E for the years 1998-2003.

WestwardWestwardWestwardWestward EastwardEastwardEastwardEastward WestwardWestwardWestwardWestward EastwardEastwardEastwardEastward

3. EXAMPLES – West African Monsoon (cont.)

Spectral analysis of West African rainfall as a function of latitude for (a) early, and (b) late monsoon season.

Emphasis shifts north and to westward-propagating waves, but a stationary component is still important.

4. PLANNED ENHANCEMENTS

The Real-Time MPA IR calibration will be done more frequently(every time?).

The Real-Time MPA will get a bias adjustment scheme, likelybased on prior gauge data.

We need to make progress on practical approaches to

• providing gridded error estimates, and• combining disparate precip estimates.

TOVS and AIRS will be used to extend the MPA concept to higherlatitudes.

• prior success in GPCP One-Degree Daily product

We plan to apply MPA concepts to the GPCP products.

We plan to apply the MPA to GPM-era data.

Web sites:

RT: ftp://aeolus.nascom.nasa.gov/pub/merged or http://precip.gsfc.nasa.govRT imagery: http://trmm.gsfc.nasa.gov3B42RT subsetting: http://lake.nascom.nasa.gov/tovas/Version 6: http://lake.nascom.nasa.gov/data/dataset/TRMM/index.html

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3. EXAMPLES – Hispaniola flooding (cont.)

Potential flood monitoring product

• simple thresholds for 1-, 3-, 7-day accumulations• oceans are masked out• focuses attention on potential problems• updated on the Web daily

7-day

3-day

1-day

3. EXAMPLES – USAID, USGS Use the Real-Time for Crop Forecasts

• the USAID Famine Early Warning System Network (USAID/FEWS-Net) is a joint program of DoS, USGS, NOAA

• goal is crop and weather assessment around the world

• TRMM real-time Multi-Satellite Precipitation Analysis (MPA) tested in 2003; first results are promising

• MPA now in quasi-operational use in Central America, Africa, and western Asia

deficits in Water Requirement Satisfaction Index match field reports of reduced yields

figure courtesy of G. Senay, USGS/EDC

timing of rain arrivals match field reports

figure courtesy of G. Senay, USGS/EDC

3. WHAT’S LEFT TO DO? (cont.)

Example CPC daily validation page (Janowiak); note variety of statistical measures.

1. INTRODUCTION (cont.)

Bowman, Phillips, North (2003, GRL) validation by TOGA TAO gauges

• 4 years of Version 5 TRMM TMI and PR• 1°x1° satellite, 12-hr gauge, each centered on the

other• each point is a buoy • the behavior seems nearly linear over the entire range• wind bias in the gauges is not corrected

Slope = 0.96 Slope = 0.68