trmm and gpm data products g.j. huffman nasa/goddard space flight center 1.introduction 2.tmpa...
TRANSCRIPT
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.