TRMM and GPM Data Products

1

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

2

• 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-satellite 3B42/43 3IMERGHH/M
multi-satellite GSMaP GSMaP
national algorithms
3

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

4

• 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

5

• 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

6

• 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
7
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-dri
ven 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
8

• 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

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)
9

• 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
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)
10

• 3. IMERG Heritage
• The Adjusted GPI (early 90s) 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

11

• 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
  60N-60S
• 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

12

• 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 will use a cut-down of
  this processing

GSFC
CPC
UC Irvine
prototype6
13

• 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 (lt 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

14

• 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 precipitation error 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
15
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
16

• 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

17

• 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

18

• 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

19

• 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 35N-S 65N-S
data coverage 50N-S 60N-S (later 90N-S)
primary format binary / HDF4 HDF5
subsetting by parameter, region
calibrator 2A12RT / 2B31 2BCMB
input algorithms GPROF(s), MSPPS GPROF2014
20

• 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_at_nasa.gov