TRMM and GPM Data Products

G.J. HuffmanNASA/Goddard Space Flight Center

1.Introduction2.TMPA3.IMERG4.Transitioning from TRMM to GPM5.Final Comments

1. Introduction – Basic Products

For both TRMM and GPM there are a variety of products based on the sensors and combinations of sensors

The sensor datasets tend to be used by precipitation specialists

• access is open to all

The multi-satellite datasets tend to be the most useful for non-expert users

sensor TRMM GPM

radiometer 2A12 2AGPROFGMI

radar 2A25 2ADPR

combined radiometer-radar

2B31 2BCMB

multi-satellite3B42/43 3IMERGHH/M*

GSMaP** national algorithms

1. Introduction – Goals

A diverse, changing, uncoordinated set of input precip estimates, with various

• periods of record

• regions of coverage

• sensor-specific strengths andlimitations

infrared microwave

latency 15-60 min 3-4 hr

footprint 4-8 km 5-30+ km

interval 15-30 min 12-24 hr(up to 3 hr) (~3 hr)

“physics” cloud top hydrometeorsweak strong

• additional microwave issues over land include• scattering channels only• issues with orographic precip• no estimates over snow

2. TMPA – Flow Chart (1/2)

Computed in both real and post-real time, on a 3-hr 0.25° grid

Microwave precip:

• intercalibrate to TMI/PRcombination for P

• intercalibrate to TMI for RT

• then combine, conical- scan first, then sounders

IR precip:

• calibrate with microwave

Combined microwave/IR:

• IR fills gaps in microwave

Instant-aneousSSM/ITRMMAMSRAMSUMHS

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 and HQ estimates

Compute monthly satellite-gauge

combination (SG)

30-day IR coefficients

Monthly Climo. Adj.

Coeff.

2. TMPA – Flow Chart (1/2)

Computed in both real and post-real time, on a 3-hr 0.25° grid

Microwave precip:

• intercalibrate to TMI/PRcombination for P

• intercalibrate to TMI for RT

• then combine, conical- scan first, then sounders

IR precip:

• calibrate with microwave

Combined microwave/IR:

• IR fills gaps in microwave

Instant-aneousSSM/ITRMMAMSRAMSUMHS

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 and HQ estimates

Compute monthly satellite-gauge

combination (SG)

30-day IR coefficients

Monthly Climo. Adj.

Coeff.

2. TMPA – Flow Chart (2/2) Production TMPA

• monthly MS and GPCC gauge analysis combined to Satellite-Gauge (SG) product

• weighting by estimated inverse error variance

• 3-hrly MS rescaled to sum to monthly SG

Real-Time TMPA

• 3-hrly MS calibrated using climatological TCI, 3B43 coefficients

RT retrospective processing starts March 2000

• start date due to IR dataset

• driven by user feedback

new

2. TMPA – Dominant Controls on Performance

Each product (not just TMPA) should tend to follow its calibrators

• over land – the GPCC gauge analysis

• over ocean – satellite calibrator

• climatological calibration only sets long-term bias, not month-to-month behavior

• current work with U. Wash. group uncovering regional variations

Fine-scale variations

• land and ocean: occurrence of precipitation in the individual input datasets

• inter-satellite calibration attempts to enforce consistency in distribution

• event-driven statistics depend on satellites, e.g. bias in frequency of occurrence

Differences between sensors tend to be noticeable

• different sensors “see” different aspects of the same scene

• limited opportunities to “fix” problems with the individual inputs on the fly

• satellite sensors tend to be best for tropical ocean

• satellite sensors and rain gauge analyses tend to have more trouble in cold areas and complex terrain

3. IMERG – Introduction

Want to go to finer time scale, but the “good stuff” (microwave) is sparse

• 30 min of data shows lots of gaps

• extra gaps due tosnow in N. Hemi.

• 5 imagers, 3 sounders here

30-min HQ Precip (mm/h) 00Z 15 Jan 2005

TMITCI

AquaAMSR-E

N16AMSU

F13SSMI

F14SSMI

N17AMSU

F15SSMI

AquaAMSR-E

N15AMSU

N15AMSU

N17AMSU

GPM developed the concept of a unified U.S. algorithm that takes advantage of

• Kalman Filter CMORPH (lagrangian time interpolation) – NOAA

• PERSIANN with Cloud Classification System (IR) – U.C. Irvine

• TMPA (inter-satellite calibration, gauge combination) – NASA

• all three have received PMM support

Integrated Multi-satellitE Retrievals for GPM (IMERG)

3. IMERG – Introduction

Want to go to finer time scale, but the “good stuff” (microwave) is sparse

• 30 min of data shows lots of gaps

• extra gaps due tosnow in N. Hemi.

• 4 imagers, 3 sounders here

30-min HQ Precip (mm/h) 00Z 15 Jan 2005

TMITCI

AquaAMSR-E

N16AMSU

F13SSMI

F14SSMI

N17AMSU

F15SSMI

AquaAMSR-E

N15AMSU

N15AMSU

N17AMSU

GPM developed the concept of a unified U.S. algorithm that takes advantage of

• Kalman Filter CMORPH (lagrangian time interpolation) – NOAA

• PERSIANN with Cloud Classification System (IR) – U.C. Irvine

• TMPA (inter-satellite calibration, gauge combination) – NASA

• all three have received PMM support

Integrated Multi-satellitE Retrievals for GPM (IMERG)

Interpolate between PMW overpasses, following the cloud systems. The current state of the art is

• estimate cloud motion fields from geo-IR data

• move PMW swath data using these displacements

• apply Kalman smoothing to combine satellite data displaced from nearby times

Currently being used in CMORPH, GSMaP (Japan)

Introduces additional correlated error

3. IMERG – Heritage

The Adjusted GPI (early ‘90’s) led to GPCP

GPCP concepts were first used in TRMM, then blended with new multi-satellite concepts

IMERG adds morphing, Kalman smoother, and neural-network concepts

TRMM V4,5 3B42

TRMM 3B42RT

Time

thin arrowsdenote heritage TRMM 3B42RTTRMM 3B42RT

CMORPH

IMERG late

IMERG final

IMERG early

GPCP V1SGMAGPI

TRMM V4,5 3B43

GPCP V2,2.1 SG

GPCP V1,1.1 1DD

GPCP V1,1.1 Pentad

V6,7 TRMM 3B42

V6,7 TRMM 3B43

TRMM 3B42RT

GPCP V3

PERSIANN PERSIANN-CCS

KF-CMORPH

3. IMERG – Notional Requirements

Resolution – 0.1° [i.e., roughly the resolution of microwave, IR footprints]

Time interval – 30 min. [i.e., the geo-satellite interval, then aggregated to 3 hr]

Spatial domain – global, initially covering 60°N-60°S

Time domain – 1998-present; later explore entire SSM/I era (1987-present)

Product sequence – early sat. (~4 hr), late sat. (~12 hr), final sat.-gauge (~2 months after month) [more data in longer-latency products] unique in the field

Instantaneous vs. accumulated – accumulation for monthly; instantaneous for half-hour

Sensor precipitation products intercalibrated to TRMM before launch, later to GPM

Global, monthly gauge analyses including retrospective product – explore use in submonthly-to-daily and near-real-time products; unique in the field

Error estimates – still open for definition; nearly unique in the field

Embedded metadata fields showing how the estimates were computed

Operationally feasible, robust to data drop-outs and (strongly) changing constellation

Output in HDF5 v1.8 – compatible with NetCDF4

Archiving and reprocessing for near- and post-RT products; nearly unique in the field

3. IMERG – Box Diagram

The flow chart shown is for the final product

• institutions are shown for module origins, but

• package is an integrated system

• “the devil is in the details”• (near-)RT products willuse a cut-down of thisprocessing

GSFC CPCUC Irvine

prototype6

Receive/storeeven-odd IR

files

1

Import PMW data;grid; calibrate;

combine

2

Compute even-odd IR files(at CPC)

3

Compute IRdisplacement vectors

4

Build IR-PMW precip calibration

10

IR Image segmentationfeature extraction

patch classificationprecip estimation

9

Apply Kalman

filter

8Build

Kalmanfilter

weights

7

Forward/backward

propagation

5

Import mon. gauge; mon. sat.-gauge

combo.; rescale short-interval datasets to monthly

Apply climo. cal.RT

Pos

t-R

T

13

12

11Recalibrateprecip rate

3. IMERG – Multiple Runs

Multiple runs serve different users’ needs for timeliness• more delay usually yields a

better product• pioneered in TMPA

Early – first approximation;flood, now-casting users

• current input data latencies at PPS support ~4-hr delay

• truly operational users (< 3 hr) not well-addressed

Late – wait for full multi-satellite; crop, flood, drought analysts

• driver is the wait for microwave data for backward propagation

• expect delay of 12-18 hr

Final – after the best data are assembled; research users

• driver is precip gauge analysis

• GPCC gauge analysis is finished ~2 months after the month

3. IMERG – Output Data Fields

Output dataset includes intermediate data fields

• users and developers require • processing traceability • support for algorithm

studies

0.1° global CED grid

• 3600x1800 = 6.2M boxes

• files are big

• but dataset compression means smaller disk files

• PPS will provide subsetting

“User” fields in italics, darker shading, similar to TMPA

Half-hourly data file (early, late, final)Size (MB) 96 / 161

1 Calibrated multi-satellite precipitation 12 / 25

2 Uncalibrated multi-satellite precipitation 12 / 25

3 Calibrated multi-satellite precipitationerror

12 / 25

4 PMW precipitation 12 / 25

5 PMW source 1 identifier 6

6 PMW source 1 time 6

7 PMW source 2 identifier 6

8 PMW source 2 time 6

9 IR precipitation 12 / 25

10 IR KF weight 6

11 Probability of liquid-phase precipitation 6

Monthly data file (final)Size (MB)

36 / 62

1 Satellite-Gauge precipitation 12 / 25

2 Satellite-Gauge precipitation error 12 / 25

3 Gauge relative weighting 6

4 Probability of liquid-phase precipitation 6

3. IMERG – Sample Day

The fine time resolution is intended to provide adequate sampling for fast systems

Some “flashing” illustrates that we still need to tune the coefficients

3. IMERG – Probability of Liquid Phase

Several GPM products are providing precipitation phase

• all are diagnostic, driven by ancillary data (likely JMA forecast for RT, GANAL product for post-real time)

This first example is based on Kienzle (2008) – temperature-only

• Day-1 IMERG will consider surface temperature and humidity

PPLP 1 March 2011

3. IMERG – Testing

“Baseline” code delivered November 2011

“Launch-ready” code delivered November 2012

“Frozen” code delivered September 2013

Changes to input algorithms are delaying operational testing to December 2013

• shake out bugs and conceptual problems

• start quasi-operational production of “proxy” GPM data

• likely we can release parallel TMPA and IMERG products

PMM GV is key to

• establishing calibration and confidence in the individual sensor retrievals and the IMERG processing

• long-term evaluation of IMERG performance in a variety of climate zones and landform cases

4. Transitioning from TRMM to GPM – Plan

IMERG will be computed at launch (February 2014) with TRMM-based coefficients

6-12 months after launch expect to re-compute coefficients and run a fully GPM-based IMERG

• compute the first-generation TRMM/GPM-based IMERG archive, 1998-present

• all runs will be processed for the entire data record

• when should we shut down the TMPA legacy code?

Contingency plan if TRMM ends before GPM is fully operational:

• institute climatological calibration coefficients for the legacy TMPA code and TRMM-based IMERG

• continue running

• particularly true for Early, Late

• NEW! TRMM fuel is now forecast to last into 2016

4. Transitioning from TRMM to GPM – Data Set Differences

The same satellite “counts” data are used, but differences in

• radiance computations (Level 1C)

• retrieval algorithms

TMPA IMERG

grid 0.25°, 3-hour 0.1°, 0.5-hour

latency 8 hours / 2 months 4 hours / 12 hours / 2 months

calibrator coverage 35°N-S 65°N-S

data coverage 50°N-S 60°N-S (later 90°N-S)

primary format binary / HDF4 HDF5

subsetting – by parameter, region

calibrator 2A12RT / 2B31 2BCMB

input algorithms GPROF(s), MSPPS GPROF2014

5. Final Comments

The TMPA continues to run until IMERG is “ready”

IMERG beta (TRMM-calibrated) versions will need early test users

• we expect some start-up issues, given the changes in calibration and input data

Full GPM-based IMERG should be available Q4 2014

• we plan to cover the entire TRMM/GPM era in the first retrospective processing

The discussion continues in the “Meet the Developer Brownbag” at 1 p.m.

george.j.huffman@nasa.gov