The TRMM Multi-satellite Precipitation Analysis (TMPA)
George J. Huffman
1,2
, Robert F. Adler
1,3
, David T. Bolvin
1,2
, Eric J. Nelkin
1,2
1: NASA/GSFC Laboratory for Atmospheres
2: Science Systems and Applications, Inc.
3: Earth System Science Interdisciplinary Center,
Univ. of Maryland College Park
Submitted to Satellite Applications for Surface Hydrology
F. Hossain, M. Gebremichael Editors
December 2008
Corresponding author:
George J. Huffman
NASA/GSFC Code 613.1
Greenbelt, MD 20771
Phone: 301-614-6308
Fax: 301-614-5492
Email: george.j.huffman@nasa.gov
Huffman et al. TMPA – 12/8/08 1
Abstract
The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis
(TMPA) is intended to provide a “best” estimate of quasi-global precipitation from the wide
variety of modern satellite-borne precipitation-related sensors. Estimates are provided at
relatively fine scales (0.25°x0.25°, 3-hourly) in both real and post-real time to accommodate a
wide range of researchers. However, the errors inherent in the finest scale estimates are large.
The most successful use of the TMPA data is when the analysis takes advantage of the fine-scale
data to create time/space averages appropriate to the user’s application. We review the conceptual
basis for the TMPA, summarize the processing sequence, and focus on two new activities. First, a
recent upgrade to the real-time version incorporates several additional satellite data sources and
employs monthly climatological adjustments to approximate the bias characteristics of the
research quality post-real-time product. Second, an upgrade of the research quality post-real-time
TMPA from Version 6 to Version 7 (in beta test at press time) is designed to provide a variety of
improvements that increase the list of input data sets and correct several issues. Future
enhancements for the TMPA will include improved error estimation, extension to higher latitudes,
and a shift to a Lagrangian time interpolation scheme.
Keywords: precipitation, satellite, remote sensing, TRMM, GPM
1. Introduction
As elaborated elsewhere in this book, precipitation is a critical weather element for determining
the habitability of different parts of the Earth, yet is difficult to measure adequately with surface-
based instruments due to its small-scale variability in space and time. Thus, satellite-borne
sensors play a key role in estimating precipitation. The proliferation of precipitation-sensing
satellites in the last 20 years (Fig. 1) has tremendously enhanced our ability to estimate
precipitation over much of the globe, but the critical piece of the puzzle is deciding how to
combine all of these individual estimates to form a single, best estimate. The desired result is a
stable, long, quasi-global time series of precipitation estimates on a uniform time/space grid that
has the finest scale that the data will reasonably support. Several factors work against these
attributes. Starting at the finest granularity, each sensor, and associated algorithms, has strengths
and weaknesses that can affect its accuracy, usually varying by region. A second factor is that the
constellation of precipitation-sensing satellites is uncoordinated, although operational agencies
typically strive to maintain one or two specific satellites in preferred orbits. This dependence on
satellites of opportunity introduces larger gaps in temporal sampling than would be the case for a
coordinated constellation. As well, many of the satellites drift (Fig. 1), giving interannual
changes in the gaps, even for the same complement of satellites. Finally, the number and types of
satellites change over time, implying that the input data cannot be considered homogeneous.
Accordingly, schemes that seek to combine all of these inputs into a “best” dataset must be
designed around, and examined for, these issues.
The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis
(TMPA) was designed with a heritage that includes the Adjusted Geosynchronous Operational
Environmental Satellite (GOES) Precipitation Index (AGPI; Adler et al. 1994), the Global
Precipitation Climatology Project (GPCP) monthly satellite-gauge (SG) combination (Huffman et
al. 1997; Adler et al. 2003), and the GPCP One-Degree Daily (Huffman et al. 2001) combination
estimates of precipitation. In common with these predecessor data sets, we identify a specific
Huffman et al. TMPA – 12/8/08 2
a specific high-quality data set as the calibrator, and then work to make the remaining input data
as consistent as possible with the calibrator data. In contrast to the earlier data sets, the TMPA is
designed to use “all” available data, meaning that we are accepting the potential inhomogeneities
of a time-varying complement of inputs in return for potentially better combination results when
more high-quality data are available. Another difference with the earlier data sets is that the
TMPA is generated twice, first as a real-time (RT) product computed about 6-9 hours after
observation time, and then as a post-real-time research product computed about 15 days after the
end of the month with additional data, including monthly surface rain gauge data.
The spatial resolution was chosen as 0.25°x0.25° latitude/longitude to ensure that the grid box
is somewhat larger than the typical footprint size for passive microwave (hereafter “microwave”)
precipitation estimates, which are the coarsest estimates in common use. The spatial domain was
set to 50°N-50°S because all of the microwave and infrared (IR) estimates we are using tend to
lose skill at higher latitudes. The temporal resolution was chosen as three hours because 1) it
allows us to resolve the diurnal cycle, 2) it matches the mandated interval for full-disk images
from the international constellation of geosynchronous (geo) satellites, and 3) it provides a
reasonable compromise between spatial coverage and temporal frequency for gridding the
asynoptic microwave estimates from low-Earth-orbit (leo) satellites. The time spans covered by
the TMPA data sets are currently determined by the start of TRMM for the research product and
the start of processing for the RT product, respectively.
The following sections briefly address the instruments and input datasets that are used in the
TMPA (Section 2), the methodology used to combine them (Section 3), and their current status
(Section 4). Then we display some comparisons and examples (Section 5) and end by discussing
future plans (Section 6).
2. Instruments and Input Datasets
The TMPA depends on input from two different types of satellite sensors, namely microwave
and IR. First, precipitation-related microwave data are being collected by a variety of leo
satellites (Fig. 1), including the TRMM Microwave Imager (TMI) on TRMM, Special Sensor
Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) on Defense
Meteorological Satellite Program (DMSP) satellites, Advanced Microwave Scanning Radiometer
for the Earth Observing System (AMSR-E) on Aqua, the Advanced Microwave Sounding Unit B
(AMSU-B) on the National Oceanic and Atmospheric Administration (NOAA) satellite series,
and the Microwave Humidity Sounders (MHS) on later NOAA-series satellites and the European
Operational Meteorological (MetOp) satellite. All of these data have a direct physical connection
to the hydrometeor profiles above the surface, but each individual satellite provides a very sparse
sampling of the time-space occurrence of precipitation. Even when composited into 3-hour
datasets, the current “full” microwave coverage averages about 80% of the Earth’s surface in the
latitude band 50°N-S and amounted to about 40% at the beginning of the TMPA record in 1998
with three satellites. Not all of the data shown in Fig. 1 can be used in the TMPA. For example, a
signal contamination problem on the F15 DMSP that began in August 2006 suspended its use,
while various new sensors have not yet been incorporated into the products, including the SSMIS
(DMSP F16 and F17) and MHS (NOAA 18, except in the new real-time, and MetOp).
Each microwave pixel-level observation from TMI, AMSR-E, and SSM/I is converted to a
precipitation estimate with sensor-specific versions of the Goddard Profiling Algorithm (GPROF;
Kummerow et al. 1996, Olson et al. 1999) for subsequent use in the TMPA. This takes place at
Huffman et al. TMPA – 12/8/08 3
takes place at the Precipitation Measurement Missions’ (PMM) Precipitation Processing System
(PPS), formerly known as the TRMM Science Data and Information System (TSDIS). GPROF is
a physically-based algorithm that applies a Bayesian least-squares fit scheme to reconstruct the
observed radiances for each pixel by selecting the “best” combination of thousands of numerical-
model-generated microwave channel upwelling radiances. As part of the processing the
microwave data are screened for contamination by surface effects.
Microwave pixel-level from AMSU-B are converted to precipitation estimates at the National
Environmental Satellite Data and Information Service (NESDIS) using operational versions of the
Zhao and Weng (2002) and Weng et al. (2003) algorithm. Ice Water Path (IWP) is computed
from the 89- and 150-Ghz channels, with a surface screening that employs ancillary data.
Precipitation rate is then computed based on the IWP and precipitation rate relations derived from
cloud model data based on the NCAR/PSU Mesoscale Model Version 5 (MM5).
The AMSU-B algorithm detects solid hydrometeors, but not liquid. The multi-channel conical-
scan passive microwave sensors (TMI, AMSR, SSM/I) similarly sense only solid hydrometeors
over land, so the AMSU-B estimates are roughly comparable for land areas. However, over ocean
the conical scanners also sense liquid hydrometeors, providing additional sensitivity, including to
precipitation from clouds that lack the ice phase. As a result, the AMSU-B estimates over ocean
are relatively less capable in detecting precipitation over ocean. [Despite the over-land focus of
this book, some background on “coast” and “ocean” will be given for completeness.]
The second major data source for the TMPA is the geo-IR data, which provide excellent time-
space coverage, in contrast to the microwave data. However, all IR-based precipitation estimates
share the limitation that the IR brightness temperatures (T
b
) primarily represent cloud-top
temperature, and implicitly cloud height. Arkin and Meisner (1987) showed that IR estimates are
poorly correlated to precipitation at fine time/space scales, but relatively well-correlated at scales
larger than about 1 day and 2.5°x2.5° of lat./long. The Climate Prediction Center (CPC) of the
National Weather Service/NOAA merges geo-IR data from the five main international geo
satellites into half-hourly 4x4-km-equivalent lat./long. grids for the domain 60°N-60°S (hereafter
the “CPC merged IR”; Janowiak et al. 2001). This dataset contains IR T
b
’s corrected for zenith-
angle viewing effects and inter-satellite calibration differences. At present, the research TMPA
estimates generated prior to the start of the CPC merged IR data set in early 2000 are computed
using a GPCP data set (also produced at CPC) that contains 24-class histograms of geo-IR T
b
data
on a 3-hourly, 1°x1° lat./long. grid covering the latitude band 40°N-S (hereafter the “GPCP IR
histograms”; Huffman et al. 2001). This data set also includes GOES Precipitation Index (GPI;
Arkin and Meisner 1987) estimates computed from leo-IR data recorded by the NOAA satellite
series, averaged to the 1°x1° grid. The TMPA fills gaps in the geo-IR coverage with these data,
most notably before June 1998 in the Indian Ocean sector.
Finally, the research TMPA employs three additional data sources: the TRMM Combined
Instrument (TCI) estimate, which combines data from both TMI and the TRMM Precipitation
Radar (PR; TRMM product 2B31; Haddad et al. 1997a,b); the Global Precipitation Climatology
Centre (GPCC) monthly rain gauge analysis (Rudolf 1993); and the Climate Assessment and
Monitoring System (CAMS) monthly rain gauge analysis developed by CPC (Xie and Arkin
1996).
3. General Methodology
Huffman et al. TMPA – 12/8/08 4
The research-quality TMPA is computed in four stages; (1) the microwave precipitation
estimates are inter-calibrated and combined, (2) IR precipitation estimates are created using the
calibrated microwave precipitation, (3) the microwave and IR estimates are combined, and (4)
rain gauge data are integrated. The real-time TMPA lacks the fourth step and has a few
simplifications, as outlined in Section 3.3. Each TMPA precipitation field is expressed as the pre-
cipitation rate effective at the nominal observation time because most gridboxes contain data from
one snapshot of satellite data.
3.1 Combined microwave estimates
Each microwave precipitation data set is averaged to the 0.25° spatial grid over the time range
±90 minutes from the nominal 3-hourly observation times (00Z, 03Z, …, 21Z). Probability
matching to a “best” estimate using coincident matchups is used to adjust each sensor, similar to
Miller (1972) and Krajewski and Smith (1991). Although we wish to adopt the TCI as the
calibrating data source, the coincidence of TCI with any of the sensors other than TMI is sparse,
so we establish a TCI–TMI calibration, then apply that to TMI. The remaining sensor data are all
calibrated to TMI, and then adjusted to the TCI using the TCI-TMI calibration. The TCI–TMI
relationship is computed on a 1°x1° grid for each month with the coincident data aggregated on
overlapping 3°x3° windows. The TCI-TMI calibration interval for the research product is a
calendar month, and the resulting adjustments are applied to data for the same calendar month.
This choice is intended to keep the dependent and independent data sets for the calibrations as
close as possible in time.
Preliminary work showed that the TMI calibrations of the other sensors’ estimates are
adequately represented by climatologically-based coefficients representing large areas. In the
case of the TMI–SSM/I calibration, separate calibrations are used for five oceanic latitude bands
(40-30°N, 30-10°N, 10°N-10°S, 10-30°S, 30-40°S) and a single land-area calibration for each of
four three-month seasons. The TMI–AMSR-E and TMI–AMSU-B calibrations are given one
climatological adjustment for land and another for ocean. The AMSU-B calibration has two
additional issues. First, the NESDIS algorithm changed on 31 July 2003 and 31 May 2007, so
separate sets of calibrations are provided for the data periods. Second, in all periods the AMSU-B
fractional occurrence of precipitation in the subtropical highs is notably deficient. After extensive
preliminary testing, the authors judged it best to develop the ocean calibration as a single region,
recognizing that the resulting fields would have a somewhat low bias. Huffman et al. (2007)
show that the low bias is somewhat larger than expected, but this does not affect the over-land
hydrology applications on which this book focuses. In all cases the calibrations in the 40°-50°
latitude belts in both hemispheres are simply taken to be the calibrations that apply just Equator-
ward of 40°.
Once the microwave estimates are calibrated for each satellite and quality-controlled, the grid
is filled using the “best” available data to produce the High Quality (HQ) microwave
combination. The TCI alone is used, if available. Otherwise, if there are one or more overpasses
from TCI-adjusted TMI, TCI-adjusted AMSR-E, and TCI-adjusted SSM/I in the 3-hr window for
a given grid box, all of these data are used (averaging as necessary). The histogram of
precipitation rate is somewhat sensitive to the number of overpasses averaged together, so it
would be more consistent to take the single “best” overpass in the data window period. The TCI-
adjusted AMSU-B estimates are only used if none of the other microwave estimates are available
for the grid box, due to the detectability deficiency in the AMSU-B estimates over ocean
discussed above. Detectability is equally problematic over land for AMSU-B and conical-scan
sensors, so this rule is unnecessarily restrictive, but likely not a serious problem.
Huffman et al. TMPA – 12/8/08 5
