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TRMM and GPM Data Products G.J. Huffman NASA/Goddard Space Flight Center Introduction TMPA IMERG Transitioning from TRMM to GPM 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

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slide1
TRMM and GPM Data Products
  • G.J. Huffman
  • NASA/Goddard Space Flight Center
  • Introduction
  • TMPA
  • IMERG
  • Transitioning from TRMM to GPM
  • Final Comments
slide2
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

* national algorithms

slide3
1. Introduction – Goals

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

• periods of record

• regions of coverage

• sensor-specific strengths and limitations

infraredmicrowave

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 hydrometeors

weak strong

• additional microwave issues over land include

• scattering channels only

• issues with orographic precip

• no estimates over snow

slide4
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/PR combination 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

slide5
Calibrate High-Quality (HQ) Estimates to “Best”

Instant-aneous

SSM/I

TRMM

AMSR

AMSU

MHS

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 and HQ estimates

3-hourly multi-satellite (MS)

Compute monthly satellite-gauge combination (SG)

Monthly gauges

Monthly SG

Monthly Climo. Adj. Coeff.

Rescale 3-hourly MS to monthly SG

Rescaled 3-hourly MS

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/PR combination 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

slide6
Calibrate High-Quality (HQ) Estimates to “Best”

Instant-aneous

SSM/I

TRMM

AMSR

AMSU

MHS

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 and HQ estimates

3-hourly multi-satellite (MS)

Compute monthly satellite-gauge combination (SG)

Monthly gauges

Monthly SG

Monthly Climo. Adj. Coeff.

Rescale 3-hourly MS to monthly SG

Rescaled 3-hourly MS

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

slide7
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

slide8
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 to snow in N. Hemi.

• 5 imagers, 3 sounders here

Aqua

AMSR-E

F15

SSMI

N17

AMSU

Aqua

AMSR-E

TMI

F13

SSMI

F14

SSMI

TCI

N16

AMSU

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

N15

AMSU

N15

AMSU

N17

AMSU

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)

slide9
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 to snow in N. Hemi.

• 4 imagers, 3 sounders here

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

Aqua

AMSR-E

F15

SSMI

N17

AMSU

Aqua

AMSR-E

TMI

F13

SSMI

F14

SSMI

TCI

N16

AMSU

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

N15

AMSU

N15

AMSU

N17

AMSU

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)

slide10
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

AGPI

SGM

GPCP V1

GPCP V2,2.1 SG

GPCP V3

GPCP V1,1.1 1DD

GPCP V1,1.1 Pentad

TRMM V4,5 3B43

V6,7 TRMM 3B43

IMERG final

TRMM V4,5 3B42

V6,7 TRMM 3B42

IMERG late

thin arrows

denote

heritage

IMERG early

TRMM 3B42RT

TRMM 3B42RT

TRMM 3B42RT

TRMM 3B42RT

CMORPH

KF-CMORPH

Time

PERSIANN

PERSIANN-CCS

slide11
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 intercalibratedto 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 fieldsshowing 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

slide12
3. IMERG – Box Diagram

The flow chart shown is for the final product

• institutions are shown for module origins, but

• package is an integratedsystem

• “the devil is in the details”

• (near-)RT products will use a cut-down of this processing

GSFC

UC Irvine

CPC

3

Compute even-odd IR files

(at CPC)

9

IR Image segmentation

feature extraction

patch classification

precip estimation

1

Receive/store

even-odd IR

files

4

Compute IR

displacement vectors

10

2

Build IR-PMW precip calibration

Import PMW data;

grid; calibrate; combine

5

Forward/backward

propagation

11

Recalibrate

precip rate

12

Import mon. gauge;

mon. sat.-gauge

combo.;

rescale short-interval

datasets to monthly

8

7

Apply Kalman

filter

Build Kalman

filter weights

Post-RT

13

RT

Apply climo. cal.

prototype6

slide13
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

slide14
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

slide15
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

slide16
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

slide17
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

slide18
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

slide19
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

slide20
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.

[email protected]

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