Data Access for
Tropical Rainfall Measuring Mission (TRMM) Data Set
TRMM Data
Contents of the
Readme for the Tropical Rainfall Measuring Mission (TRMM) Data Set
- Summary
Sponsor
Future Updates
Data Flow Description
File Specification Description
Tools for Visualizing Data
Data Access
Points of Contact
References
Appendix A - TRMM Mission
Appendix B - TRMM Platform
Appendix C - TRMM Orbit and Instruments
Appendix D - TRMM Products
Appendix E - HDF
Appendix F - TRMM Algorithm Flow
This document contains descriptions of the standard products of the Tropical
Rainfall Measuring Mission (TRMM) and information on accessing the data.
The TRMM standard products include measurements from satellite and ground-based
sensors and geophysical parameters derived from them. The sensor meaurements
include visible and infrared radiance, microwave brightness temperature, radar
reflectivity, surface radar volume scans, and rain gauge and disdrometer data.
The derived geophysical products include vertical rain and hydrometeor profiles,
rain type, radar backscatter cross section, raindrop size distribution, rain
gauge rain rates, and five-day and monthly average rain rates. A list of the
products can be found in Appendix D. The TRMM satellite,
launched in November 1997 and covering the latitude belt between approximately
38 degree N to 38 degree S, has a designed three-year mission life.
The distribution of this data set is funded by
NASA's Earth
Science Enterprise (ESE).
The data are not copyrighted; however, we request that when you publish data or
results using these data, please send a copy of your publication to Help Desk,
Goddard DAAC, Code 902.2, NASA GSFC, Greenbelt, MD 20771 or email the reference
of your publication to daacuso@daac.gsfc.nasa.gov. Please acknowledge as
follows:
The data used in this study were acquired as part of the Tropical
Rainfall Measuring Mission (TRMM). The algorithms were developed by the TRMM
Science Team. The data were processed by the TRMM Science Data and Information
System (TSDIS) and the TRMM office; they are archived and distributed by the
Goddard Distributed Active Archive Center. TRMM is an international project
jointly sponsored by the Japan National Space Development Agency (NASDA) and
the US National Aeronautics and Space Administration (NASA) Office of Earth
Sciences.
This Readme document (v 1.0) supports the June 1998 interim release of TRMM
standard products.
It is expected that some of the algorithms will be refined or improved as new
measurements are gathered and analyzed by the TRMM science team.
The TRMM data products are expected to be periodically reprocessed by TSDIS
in order to provide the scientific community with the most current and best
available rainfall products. The exact reprocessing schedule will be set by
a team designated by the TRMM project scientist. Currently, it is expected
that reprocessing will occur once per year. This document will be updated in
coordination with the TRMM reprocessing schedule and whenever appropriate
as determined by the GDAAC Hydrology Data Support Team.
The June 1998 interim release includes most level 1 TRMM standard products.
The data flow of all products are described below and diagrammed in
Appendix F.
- Level 1A data
-
The VIRS and TMI Level 1A products (1A-01 and 1A-11, respectively) consist
of two files each: a product file and a Standard Format Data Unit (SFDU) header
file. The Level 1A file is a concatenation of header record, spacecraft
attitude packets, sensor (VIRS/TMI) housekeeping data packets, sensor
(VIRS/TMI) science data packets, Quality and Accounting Capsules (QACs), and
Missing Data Unit List (MDUL). The Level 1A PR file (1A-21) is a concatenation
of a TSDIS header, spacecraft attitude packets, PR housekeeping data packets,
PR science data packets, calibration coefficients, QACs, and MDUL. The Goddard
DAAC does not archive the Level 1A PR product.
- Level 1 algorithms
-
The VIRS calibration algorithm (1B-01) converts the digital counts collected by
VIRS to spectral radiance. Bands 1 and 2 measure solar radiation, and bands 3,
4, and 5 measure emitted thermal (IR) radiance. The calibration of the solar
bands uses the VIRS counts from viewing the moon or the sun through a solar
diffuser, together with zero radiance collected from a space view. Calibration
parameters are calculated once 1 -4 weeks. The IR bands are calibrated for each
scan line using data from the internal blackbody and space view. These two
points and a quadratic term determined pre-launch are used to generate a count
vs. radiance curve for the IR bands. Additional corrections are generated
from differences in emittance of the two sides of the scan mirror, out-of-band
transmittance, scan angle modulation, 1/f noise, black body temperature
variations due to views of the sensor cavity and the Earth, detector
temperature variations, and sensor temperature changes.
The TMI calibration algorithm (1B-11) converts the radiometer counts to antenna
temperatures by applying a linear relationship of the form Ta = c1 + c2 x count.
The coefficients are provided by the instrument contractor. Antenna temperatures
are corrected for cross-polarization and spill over to produce brightness
temperatures (Tb), but no antenna beam pattern correction or sample to pixel
averaging are performed. Temperatures are provided at 104 scan positions for
the low frequency channels and 208 scan positions at 85 GHz. There are four
samples per pixel (3 -dB beamwidth) at 10 GHz, two samples at 19, 22, and 37
GHz, and one sample per pixel for the 85 GHz.
The PR calibration algorithm (1B-21) converts the counts of radar
echoes and noise levels into engineering values (power) and outputs the radar
echo power and noise power separately. The algorithm also detects and flags the
range bin with return power that exceeds a pre-determined threshold value.
The PR reflectivity algorithm (1C-21) converts the power and noise estimates
from 1B-21 to radar reflectivity factors (Z-factors). In order to reduce
output data volume, only pixels with power that exceeds the minimum echo
detected in 1B-21 are converted and stored.
GV product 1B-51 consists of raw radar data in polar coordinate
scans at the original spatial and temporal resolution of the data collected by
the radar. The data are reformatted to the HDF format. Two additional procedures
are applied to reduce data size. Scan data at a range beyond 230 km and
spectral width data available from WSR-88 radars are not archived. Only radar
data for the GV direct data (DD) sites (Houston, TX; Melbourne, FL; Darwin,
Australia; and Kwajalein, Marshal Islands) are available. All volume scans are
kept for this product.
The QC reflectivity algorithm (1C-51) further truncates 1B-51 from a
range of 230 km to 200 km. In addition, a threshold Z-factor (-15 dBZ) is
applied to the reflectivity data, a quality control (QC) mask which
identifies pixels that need further processing is generated, and a
correction mask for the pixels identified above is generated. This product
(1C-51) is archived for all GV radars. All volume scans are
kept when there is a satellite overpass; otherwise, only one volume scan
every 30 minutes is kept. Satellite overpass is defined as the time period
during which the sub-satellite point is within 700 km of the radar, and
the overpass time is within +/-30 minutes.
- Level 2 satellite algorithms
-
There is no Level 2 algorithm for VIRS, only one algorithm for TMI, three
algorithms for PR, and one combined TMI/PR algorithm.
The TMI profiling algorithm (2A-12) generates vertical profiles of hydrometeors
from TMI brightness temperatures by blending the radiometric data with dynamical
cloud models. For each pixel, the algorithm assigns a surface type
(land/ocean/coast) and a freezing height; and computes profiles of hydrometeors
(cloud liquid, cloud ice, water vapor, etc.), comprising 14 vertical levels,
and surface rain rate.
The sigma zero algorithm (2A-21) inputs the PR power (1B-21) and computes an
estimate of the path attenuation and its reliability by using the surface as
reference target. It also computes the spatial and temporal statistics of the
surface scattering cross section and classifies the cross sections into
land/ocean and rain/no rain categories.
The PR qualitative algorithm (2A-23) inputs PR reflectivities (1C-21) and
returns a rain/no rain decision based on echo structure. When rain is present,
a storm height is calculated. If a bright band is detected, the height of the
bright band is also given.
The PR profile algorithm (2A-25) estimates hydrometeor profiles for each radar
beam. Rainfall rates are given for each resolution cell (4 km x 4 km x 250 m)
of the PR. Inputs for this algorithm include PR reflectivity (1C-21), sigma
zero (2A-21), and PR qualitative (2A-23).
The TRMM combined algorithm (2B-31) combines data from the TMI and PR to produce
the best rain estimate for TRMM. Currently, it uses the low frequency channels
of TMI to find the total path attenuation. This information is used to
constrain the radar equation. Inputs for this algorithm include TMI radiance
(1B-11), PR reflectivity (1C-21), and PR qualitative (2A-23).
- Level 2 GV algorithms
- The radar site rain map algorithm (2A-53) produces instantaneous rain rate
maps at 2 km x 2 km resolution from GV Quality controlled radar reflectivity
(1C-51), by using an appropriate reflectivity-rain rate (Z-R) relation.
The radar site convective/stratiform map algorithm (2A-54) classifies the
surface precipitation as convective or stratiform from QC radar reflectivity
(1C-51).
Using the radar site convective/stratiform map (2A-54), the rain existence
algorithm (2A-52) determines the fraction of the radar field of view which has
detectable precipitation.
The radar site 3-D reflectivity algorithm (2A-55) interpolates the
instantaneous reflectivities of a radar volume scan onto a 3-dimensional
Cartesian grid with 1.5 km vertical and 2 km horizontal resolutions. In addition
to the 3-D gridded reflectivity, the product includes mean reflectivity and
distribution at each height. The frequency distribution are provided in the
form of contoured frequency by altitude diagrams (CFADs). Vertical profiles for
the CFADs are further classified into land/ocean, convective/stratiform/anvil
rain types.
In addition to the above four Level 2 GV products, time series of the rain
gauge data are available. The rain gauge algorithm (2A-56)
converts the raw rain gauge data into average and peak minute rain rates,
if rain is present in the minute interval.
- Level 3 satellite algorithms
- The combined instrument rain calibration algorithm (3B-42) uses a combination of 2B-31, 2A-12, SSMI, AMSR and AMSU precipitation estimates (referred to as HQ), to adjust IR estimates from geosynchronous IR observations. Near-global estimates are made by calibrating the IR brightness temperatures to the HQ estimates. The 3B-42 estimates are scaled to approximately match the monthly 3B-43 analyses. The output is rainfall for 0.25x0.25 degree grid boxes every 3 hours. For more details of the algorithm, go to http://trmm.gsfc.nasa.gov/3b42.html.
The global rainfall algorithm (3B-43) combines the estimates generated by combined instrument rain calibration (3B-42) and global gridded rain gauge data from CAMS, produced by NOAA's Climate Prediction Center and/or global rain gauge product, produced by the Global Precipitation Climatology Center (GPCC). The output is rainfall for 0.25x0.25 degree grid boxes for each month. For more details of the algorithm, go to http://trmm.gsfc.nasa.gov/3b43.html
- The TMI monthly surface rainfall algorithm (3A-11) estimates monthly rain
from the histogram of the brightness temperatures obtained from TMI calibration
(1B-11). This histogram is matched to a log-normally
distributed rain rate distribution via a rain rate-brightness temperature
relation. A beam-filling correction is applied to account for the non-uniformly
filled field-of-view of the TMI sensor. Outputs are monthly surface rain rates
and freezing heights for 5 x 5 degree grid boxes.
The PR monthly rain structure algorithm (3A-25) accumulates several important
products derived from PR reflectivities (1C-21) and Level 2 PR products (2A-21,
2A-23, and 2A-25). Its outputs include rain rate accumulation over a month for
5 x 5 degree grid boxes at heights of 2 and 4 km, histograms of radar and
meteorological parameters, fraction of rain and bright band area, and
correlations between the various radar and meteorological parameters.
The PR monthly surface rainfall algorithm (3A-26) computes monthly rainfall,
at the surface and three vertical levels, for 5 x 5 degree grid boxes,
using a statistical technique. This technique uses multiple thresholds of
rain rate that are within the dynamic range of the PR. The rain rate
distribution is then fitted to a log-normal or gamma distribution.
The GV monthly rainfall map algorithm (3A-54) and GV 5-day rainfall map
algorithm (3A-53) accumulate 2 km x 2 km instantaneous rainfall from all
radars at a site for a month and for five days, respectively. The input for
both algorithms is the radar site rain map (2A-53).
The monthly 3-D structure algorithm (3A-55) computes the mean reflectivity and
frequency distribution of reflectivity at each height from the gridded 3-D
reflectivity (2A-55). The frequency distributions, provided in CFADs, are
presented for the total, land/ocean, convective/stratiform/anvil rain.
The combined monthly rainfall structure algorithm (3B-31) uses the output from
the combined rainfall structure (2B-31) to calibrate TMI rainfall structure
(2A-12) on a monthly basis. To accomplish this, the TMI rainfall structure
data are subsampled to the combined rainfall structure (2B-31) and calibration
coefficients computed for the region of overlap. The outputs consist of surface
rainfall, the number of satellite visits to the grid box, confidence, five
classes of hydrometeors, and latent heating at 14 vertical layers.
The combined instrument rain calibration algorithm (3B-42) uses a combination of 2B-31, 2A-12, SSMI, AMSR and AMSU precipitation estimates (referred to as HQ), to adjust IR estimates from geosynchronous IR observations. Near-global estimates are made by calibrating the IR brightness temperatures to the HQ estimates. The 3B-42 estimates are scaled to approximately match the monthly 3B-43 analyses. The output is rainfall for 0.25x0.25 degree grid boxes every 3 hours. For more details of the algorithm, go to http://trmm.gsfc.nasa.gov/3b42.html.
The global rainfall algorithm (3B-43) combines the estimates generated by combined instrument rain calibration (3B-42) and global gridded rain gauge data from CAMS, produced by NOAA's Climate Prediction Center and/or global rain gauge product, produced by the Global Precipitation Climatology Center (GPCC). The output is rainfall for 0.25x0.25 degree grid boxes for each month. For more details of the algorithm, go to http://trmm.gsfc.nasa.gov/3b43.html.
The file formats for TRMM Level 1, Level 2, and level 3 data products are
described below. These products are stored in the Hierarchical Data
Format (HDF) (Appendix E).
The four data structures used for TRMM data products are:
- Swath structure
The swath structure was designed by
EOSDIS (EOS Data and Information System) to store satellite data that
are organized by scans. This data structure is used for Levels 1B, 1C, 2A,
and 2B satellite products. The swath structure is contained in a Vgroup, with
the name SwathData and the class SwathData. The SwathData Vgroup includes
SwathStructure, Scan Time, Geolocation, scan data, and IFOV data. For all
of these objects, the scan dimension has the least rapidly varying index.
SwathStructure is a text block which specifies which geolocations and times
apply to which elements of the IFOV data. The Scan
Time is a Vdata (8-byte float or several integers whose sizes sum to 8 bytes).
The Geolocation is an SDS containing latitude and longitude (4-byte float).
Scan data are data that apply to the whole scan and can take the form of one
or more Vdatas or SDSs. The IFOV data occur at every pixel or at regular
pixel intervals (e.g., every 10 pixels) and take the form of one or more SDSs.
- Planetary grid structure
The planetary grid structure was designed by EOSDIS to store earth-
located grids. A grid is an array of grid boxes, rather than grid points.
This data structure is used for Levels 3A and 3B satellite products. The
planetary grid structure occupies part of a file. This structure is contained
in a Vgroup, with the name PlanetaryGrid and the class PlanetaryGrid. The
PlanetaryGrid Vgroup includes one GridStructure, one or more Data Grids,
and other Data.
GridStructure is a single Vdata which allows the geometric interpretation
of the grids. GridStructure is an object that mimics an attribute,
since HDF has not yet defined attributes for Vgroups. This imitation of
an attribute is implemented as a Vdata with the name GridStructure and
the class "Attr0.0", one field named "VALUES," number type of DFNT_CHAR8,
and order equal to the length of the text. This specification of
GridStructure anticipates the HDF development of attributes for Vgroups.
The maximum expected size for GridStructure is 5000 bytes.
- Radar grid structure
The radar grid structure is a structure used to store TRMM GV Level-2 and
Level-3 grids which are defined by TSDIS. The grid is equivalent to the
grid created by the NCAR SPRINT software and does not correspond to any
standard projection. It is neither equal area nor equal angle. The grid
is an array of grid points, rather than grid boxes. It has a fixed
horizontal distance between grid points and an odd number of grid points
in the x and y directions. The origin is in the central grid point of the
lowest grid plane. Distances along the x and y directions are measured on
a flat plane tangent to the earth's surface directly below the origin. The
z distances are measured from mean sea level, perpendicular to this tangent
plane. The x dimension coincides with east-west only along the x-direction
grid line that intersects the origin. Similarly, the y dimension coincides
with north-south only along the y-direction grid line that intersects the
origin, and the z dimension coincides with vertical only directly above the
origin. The constant z grid planes follow the curvature of the earth.
- Radar structure
The radar structure is simply the navigation information within the
GV Level-1B and Level-1C product formats. The information to geolocate
(radar latitude and longitude and the range, elevation, and azimuth of
each bin) are contained in the formats.
The Goddard DAAC is providing the following tools to help its users
to visualize data which are in the Hierarchical Data Format (HDF).
- EOSView
EOSView is a standalone X-based data visualization tool that displays
HDF files. It can be used to view data ordered from the
Goddard DAAC. In addition, it provides a secondary mechanism
for previewing browse files before ordering data. (The primary mechanism
is the preview feature of the
TRMM data search and order system.)
EOSView serves as a file verification tool. The contents of HDF files
are displayed and individual objects can be selected for display.
Displayable objects include raster images, datasets in tables, pseudocolor
images of datasets, attributes, and annotations. Simple animations
can be performed for a file with multiple raster images.
A unique interface has been provided for handling HDF-EOS data structures.
The Swath/Point/Grid interface uses only HDF-EOS library calls.
The EOSView user will not see the underlying HDF structures but
will be prompted for what parts of the HDF-EOS object they wish to
view. The EOSView requires at least 4 megabytes of memory and a larger
than 24-bit graphics board.
- TSDIS Orbit Viewer
The TSDIS Orbit Viewer is a menu-driven graphical interface for
dynamically generating images from TRMM HDF files. The Orbit Viewer was
developed at TSDIS. It requires IDL version 5
running on a Unix system with at least 50 megabytes of memory.
The Orbit Viewer can display TRMM satellite and Ground Validation (GV)
products, browse products, and Coincidence Subsetted Intermediate
(CSI) products. The source code and installation instructions for
the Orbit Viewer are available from the
TSDIS Web Site
(http://www-tsdis.gsfc.nasa.gov/tsdis/TSDISorbitViewer/release.html).
Please note: TSDIS can provide technical support for the Orbit Viewer
only to certain members of the TRMM Science Team. Other users should
contact the
DAAC's Hydrology Data Support Team (hydrology-disc@listserv.gsfc.nasa.gov).
- DOWNLOAD INSTRUCTIONS FOR THE SOFTWARE:
- These tools can be downloaded via anonymous ftp using a command
line ftp client, available on all Unix machines.
The source code, installation instructions, and documentation for EOSView
and Orbit Viewer are available from the
Goddard DAAC's TRMM ftp site
(ftp://disc2.nascom.nasa.gov/software/trmm_software).
The following files should be downloaded for EOSView:
- EOSView (executable)
- eosview.csc (help)
- eosview.uid (user interface description file)
- eosview.dat (IDL commands file)
- How to start EOSView:
- Start EOSView by typing 'EOSView' at the command prompt.
The current working directory must contain the four
EOSView files.
- To read a "tar" format file received by FTP, use the command:
- tar -xvf < filename>
- dd if=< dev> of=< filename> bs=65024
TRMM data can be accessed and ordered using the Goddard DAAC's
TRMM Data Search and Order System
(http://disc.sci.gsfc.nasa.gov/data/datapool/TRMM_DP/).
Other precipitation data sets are available via anonymous ftp.
For more information about data sets on ftp, see our Data Products
chart.
- For information about or assistance in using any DAAC data, contact the
DAAC Help Desk at:
- EOS Distributed Active Archive Center (DAAC)
Code 902.2
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Email: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)
General and Mission-Related:
Greenstone, R., 1992: Bibliography of TRMM-related publication through 1991.
On file at TRMM office. Mail code 910.1, Goddard Space Flight Center, NASA,
Greenbelt, MD. 20771.
Simpson, J. (Ed.), 1988: TRMM: A Satellite Mission to Measure Tropical
Rainfall, Report of the Science Steering Group. National Aeronautics and
Space Administration, Goddard Space Flight Center, Greenbelt, MD.
20771, 94 pp.
Simpson, J.S., C. Kummerow, W.-K. Tao, and R.F. Adler, 1996: On the Tropical
Rainfall Measuring Mission (TRMM). Meteorol. Atmo. Phys., 60, 19-36.
TRMM Satellite Products-Related:
Adler, R.F., G.J. Huffman, and P.R. Keehn, 1994: Global rain estimates from
microwave-adjusted geosynchronous IR data. Remote Sens. Rev., 11, 125-152.
Arkin, P.A., and B.N. Meisner, 1987: The relationship between large-scale
convective rainfall and cold cloud over the Western Hemisphere during
1982-1984. Mon. Wea. Rev., 115, 51-74.
Bell, T., L. Abdullah, R. Martin, G. North, 1990: Sampling errors for
satellite derived tropical rainfall: Monte Carlo study using a space-time
stochastic model. J. Geophys. Res., 95D, 2195-2205.
Braun, S.A., and R.A. Houze, Jr., 1995: Diagnosis of hydrometeor profiles
from area-mean vertical-velocity data. Quart. J. Roy. Meteor. Soc., 121, 23-53.
GPCC, 1992: Monthly precipitation estimates based on gauge measurements on the
continents for the year 1987 (preliminary results) and future requirements.
WMO/WCRP and DWD, Rep. No. DWD/K7/WZN-1992/08-1, 20 pp.
Iguchi, T., and R. Meneghini, 1994: Intercomparison of single frequency
methods for retrieving a vertical rain profile from airborne or spaceborne
data. J. Atmos. Oceanic Technol., 11, 1507-1516.
Kummerow, C., and L. Giglio, 1994a: A passive microwave technique for
estimating rainfall and vertical structure information from space,
Part I: Algorithm description. J. Appl. Meteor., 33, 3-18.
Kummerow, C., and L. Giglio, 1994b: A passive microwave technique for
estimating rainfall and vertical structure information from space,
Part II: Application to SSM/I data. J. Appl. Meteor. 33, 19-34.
Raymond, D.J., 1994: Convective processes and tropical atmospheric
circulations. Quart. J. Roy. Meteor. Soc., 120, 1431-1455.
Rosenfeld, D., D.B. Wolff, and E. Amitai, 1994: The window probability
matching method for rainfall measurements with radar. J. Appl. Meteor., 33,
682-693.
Shin, K-S., and G. North, 1988: Sampling error study for rainfall estimate
by satellite using a stochastic model. J. Appl. Meteor. 28, 1218-1231.
Steiner, M., R.A. Houze, Jr., and S.E. Yuter, 1995: Climatological
characterization of three-dimensional storm structure from operational
radar and raingauge data. J. Appl. Meteor., 34, 1978-2007.
Takahashi N., H. Kumagai, H. Hanado, T. Kozu, and K. Okamoto, 1995: The
CRL airborne multiparameter precipitation radar (CAMPR) and the first
observation results, Preprints of the 27th Conference on Radar Meteorology,
pp. 83-85.
Tao, W.K., and J. Simpson, 1993: The Goddard Cumulus Ensemble Model.
Part I: Model description. Terrestrial, Atmospheric and Oceanic
Sciences, 4, 35-72.
Wilheit, T., R. Adler, A. Chang, C. Kummerow, S. Avery, W. Berg, J. Ferriday,
E. Barrett, C. Kidd, D. Kniveton, P. Bauer, N. Grody, S. Goodman, R. Spencer,
A. Mugnai, W. Olson, G. Petty, A. Shibata, and E. Smith, 1994: Algorithms for
the retrieval of rainfall from passive microwave measurements. Remote Sensing
Reviews, 11, 1-32.
Wilheit, T.T., A.T.C. Chang, and L.S. Chiu, 1991: Retrieval of monthly
rainfall indices from microwave radiometric measurements using probability
distribution functions. J. Atmo. Oceanic Tech., 8, 118-136.
TRMM GV Products-Related:
Atlas, D., D. Rosenfeld, and D. Short, 1990: The estimate of convective
rainfall by area integrals. Part I: The theoretical and empirical basis.
J. Geophys. Res., 95, 2153-2160.
Chiu, L.S., 1988: Rain estimation from satellites: Areal rainfall-rain area
relation. Proceedings, The 3rd Conference on Satellite Meteorology and
Oceanography. AMS, Anaheim, Calif., Feb. 1-5, 1988, 363-369.
Churchill, D.D., and R.A. Houze, 1984: Development and structure of winter
Monsoon cloud clusters on 10 December 1978. J. Atmos. Sci., 41, 933-960.
Doneaud, A.A., P.L. Smith, A.S. Dennis, and S. Sengupta, 1981: A simple
method for estimating convective rain volume over an area. Water Resour.
Res., 17, 1676-1682.
Kedem, B., L.S. Chiu, and Z. Karni, 1990: An analysis of the threshold method
for measuring area average rainfall. J. Appl. Meteor., 29, 3-20.
Kedem, B., H. Paxiopoulos, X. Guan, and D.A. Short, 1994: A probability
distribution model for rain rate. J. Appl. Meteor., 33, 1486-1493.
Krajewski, W.F., and J.A. Smith, 1991: On the estimation of climatological
Z-R relationships. J. Appl. Met., 30, 1436-1445.
Morrissey, M.L., and J.S. Greene, 1991: The Pacific atoll raingauge data set.
Planetary Geosci. Div. Contrib. 648, Univ. of Hawaii, Honolulu, HI, USA, 45 pp.
Rogers, R.R., 1971: The effect of variable target reflectivity on weather radar
measurements. Quart. J. Roy. Met. Soc., 97, 154-167.
Rosenfeld, D., E. Amitai, and D.B. Wolff, 1995: Improved accuracy of radar
WPMM estimated rainfall upon application of objective classification criteria
with radar. J. Appl. Meteor., 34, 212-223.
Rosenfeld, D., D. Atlas, and D.A. Short, 1990: The estimation of convective
rainfall by area integrals. Part II: The height-area threshold (HART) method.
J. Geophys. Res. Atmos., 95, 2161-2176.
Short, D.A., K. Shimizu, and B. Kedem, 1993: Optimal thresholds for the
estimation of area rain rate moments by the threshold method. J. Appl. Meteor.,
32, 182-192.
Steiner, M., R.A. Houze, Jr., 1993: Three-dimensional validation at TRMM
ground truth sites: Some early results from Darwin, Australia. Preprints, 26th
Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 417-420.
Thiele, O.W. (Ed.), 1987: On requirements for a satellite mission to measure
tropical rainfall. National Aeronautics and Space Administration, Research
Publication, NASA RP-1183, Washington, DC, 49 pp.
Tokay, A., and D.A. Short, 1996: Evidence from tropical raindrop spectra of
the origin rain from stratiform versus convective clouds. J. Appl. Meteor., 35,
355-371.
The Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan
mission to monitor tropical and subtropical precipitation and to
estimate its associated latent heating. The TRMM rainfall data
will be particularly important for studies of the global hydrological
cycle and for testing the realism of climate models, and their ability
to simulate and predict climate accurately on the seasonal time scale.
Other scientific issues such as the effects of El Nino on climate could
be addressed with a reliable, extended time series of tropical rainfall
observations as well.
The TRMM satellite was developed and constructed at the Goddard Space
Flight Center. The satellite was launched on November 27, 1997, on
a Japan National Space Development Agency (NASDA) H-II rocket, from the
Tanegashima Space Center. The satellite will acquire approximately 16
orbits of data per day for mapping tropical rainfall between latitudes of
38 degrees north and south of the equator. The mission has a designed life
of three years.
Orbit parameters for the TRMM satellite:
Orbit Injection 380 km, +/- 10 km
Nominal Mission Altitude 350 km, +/- 1.25 km
Inclination 35 deg, +/- 0.1 deg
Orbital Period 91.538 +/- 0.026 min
Eccentricity .00054, +/- 2.0 x 10^-6 nominal
Argument of Perigee 90 deg, +/- 2.0 deg
The TRMM satellite's low inclination (35 degrees), non-sun-synchronous, and
highly precessing orbit allows it to fly over each position on the Earth's
surface at a different local time each day. This kind of sampling allows
the examination of the diurnal cycle of precipitation. The orbit is
maintained at approximately 350 km. The characteristics of the three rain
instruments and associated science applications are described in the
Goddard DAAC's Precipitation Web site
(http://disc.sci.gsfc.nasa.gov/precipitation/trmm_intro.shtml).
In addition, a Lightning Imaging Sensor (LIS) and a Clouds and Earth's
Radiant Energy System (CERES) are carried on the TRMM satellite. The LIS
s a calibrated optical sensor operating at 0.7774 micron and observes
distribution and variability of lightning. The horizontal resolution of LIS
at nadir is 5 km and the swath width is 590 km. The CERES is a
visible/infrared sensor which measures emitted and reflected radiative
energy from the surface of the Earth and the atmosphere and its
constituents. The TRMM CERES operates at 0.3 to 5.0 microns in the
shortwave range and 8.0 to 12.0 microns in the longwave range. LIS data
are available from the
Global Hydrology Resource Center
(http://ghrc.msfc.nasa.gov), and CERES data are available from the
NASA Langley DAAC
(http://eosweb.larc.nasa.gov).
TRMM satellite data for each orbit are stored on board and transmitted to
the ground via the TDRSS. The TRMM science data are processed by the
TRMM Science Data and Information System (TSDIS) into standard products.
These products are transferred to the Goddard DAAC for archival and
distribution.
The satellite data are compared with ground validation (GV) data
collected at the following sites: Darwin, Australia; Thailand; Israel;
Taiwan; Sao Paolo, Brazil; Guam; Kwajalein, Marshal Islands; Melbourne,
Miami, Key West, and Tempa Bay in Florida,; Lake Charles in Louisiana;
and New Braunfels, Corpus Christi, and the Texas A&M research radar in
Texas. The radar types for these GV sites are listed in Table C1.
Table C1: Ground Validation Radar Sites and Types
Site Name Radar Type
Darwin, Australia C band dual-polarized Doppler
Melbourne, FL NEXRAD
Houston, TX NEXRAD
Kwajalein, Marshall Islands NEXRAD
Florida all NEXRAD
Guam NEXRAD
Taiwan S band Doppler
Sao Paolo, Brazil S band simple
Jerusalem, Israel S band simple
Thailand NEXRAD
These GV sites are designated as Data Product (DP) or Direct Data
(DD) sites. The DP sites will process the radar data using the TRMM
Science Team approved algorithms and send the data to TSDIS for processing
of the corresponding browse data. The raw radar data from the DD sites are
sent directly to TSDIS for processing. The TRMM GV DD sites and their
radar characteristics are summarized below:
Characteristics of TRMM Ground Validation Direct Data Site Radars
Houston, TX Melbourne, FL Kwajalein Darwin
Radar Type WSR-88D WSR-88D S band C band
Research Research Doppler
Polarization Horizontal Horizontal # Dual
Dual ##
Wavelength 10 cm 10 cm 10 cm 5 cm
Beam Width 1.0 degree 1.0 degree 1.0 degree 1.0 degree
VOS per hour 6-12 6-12 6-12 5-18
Number of
Elevation Angles 9-14 9-14 Up to 25 Up to 25
Range Interval 1 km 1 km 250 m 250 m
(#) Horizontal from Nov. 97 through Mar. 1998
(##) Horizontal from Nov. 97 through 9 Feb. 1998
TSDIS ID Description Data Product Product Type
Satellite
Products
1B-01 Calibrated VIRS Visible and Infrared Calibrated sensor data
(0.63, 1.6, 3.75, Radiance
10.8, and 12 um)
radiances at 2.2 km
resolution, over a
720 km swath
1B-11 Calibrated TMI Microwave Brightness Calibrated sensor data
(10.65, 19.35, 21, Temperature
37, and 85.5 GHz)
brightness
temperatures at 5-45
km resolution, over
a 760 km swath
1B-21 Calibrated PR (13.8 Radar Power Calibrated sensor data
GHz) power at 4 km
horizontal and 250 m
vertical
resolutions, over a
220 km swath
1C-21 PR (13.8 GHz) Radar Reflectivity Calibrated sensor data
reflectivity at 4 km
horizontal and 250 m
vertical
resolutions, over a
220 km swath
2A-21 PR (13.8 GHz) Radar Surface Surface reflectivity [?]
normalized surface Cross-Section
cross section at 4
km horizontal
resolution, and path
attenuation in case
of rain, over a 220
km swath
2A-23 Rain type; freezing Radar Qualitative Qualitative data
and bright band
heights from PR
(13.8 GHz) at 4 km
resolution, over a
220 km swath
3A-11 Rain rate, Monthly 5 x 5 Degree Surface rainfall
conditional rain Oceanic Rainfall
rate, rain frequency
and freezing height
over latitude band
from 38 degree N
to 38 degree S
from TMI
3A-26 Rain rate Monthly 5 x 5 Degree Surface rainfall
probability Surface Rain
distribution at Accumulation
surface, 2 and 4 km
over latitude band
from 38 degree N
to 38 degree S
from PR
2A-12 Hydrometeor (cloud Hydrometeor Vertical Vertical structure of
liquid water, Profile rainfall
precipitation water,
cloud ice,
precipitation ice)
profiles in 14
layers at 21 km
horizontal
resolution along
with latent heat and
surface rain, over a
760 km swath
2A-25 PR (13.8 GHz) rain Radar Rainfall Vertical structure of
rate, reflectivity, Vertical Profile rainfall
attenuation,
profiles at 250 m
vertical and 4 km
horizontal
resolutions, over a
220 km swath
2B-31 Combined PR/TMI Combined Rainfall Vertical structure of
rain rate and Vertical Profile rainfall
path integrated
attenuation at 250 m
vertical and 4 km
horizontal
resolutions, over a
220 km swath
3A-25 Total and Monthly 5 x 5 Degree Vertical structure of
conditional rain and 0.5 x 0.5 Degree rainfall
rate, radar Spaceborne Radar
reflectivity, path Rainfall
integrated
attenuation at 2, 4,
6, 10, 15 km for
convective and
stratiform rain;
storm, freezing and
bright band heights
and snow-ice layer
depth over latitude
band from 38 degree
N to 38 degree S
from PR
3B-31 Rain rate, cloud Monthly 5 x 5 Degree Vertical structure of
liquid water, rain Combined Rainfall rainfall
water, cloud ice,
grauples at 14
levels over latitude
band from 38 degree
N to 38 degree S
from PR and TMI
3B-42 Calibrated IR 3-hourly 0.25x0.25 TRMM and other satellites
merged with TRMM merged TRMM and other rainfall
and other satellite satellite estimates
data
3B-43 Merged 3B-42 Monthly 0.25x0.25 TRMM and other sources
and rain gauge degree merged TRMM rainfall
estimates and other sources
estimates
Ground Validation
Products
1B-51 Volume scan of radar Radar Reflectivity at Calibrated sensor data
reflectivity, Original Radar
differential Resolution,
reflectivity and Coordinate, and
mean velocity (if Sampling
available) truncated
at 230 km range
1C-51 Volume scan of Half-Hourly(*) Calibrated sensor data
calibrated radar Calibrated Radar
reflectivity and Reflectivity at
differential Original Radar
reflectivity (if Resolution and
available) and Coordinate
corresponding QC
masks, truncated at
200 km range
[* Volume scans at
radar sampling
within half hour of
TRMM satellite
coincidence]
2A-52 Percent of rain in Half-Hourly(*) Rain Qualitative data
radar volume scan Existence
[* Volume scans
at radar sampling
within half hour of
TRMM satellite
coincidence]
2A-54 Instantaneous rain Half-Hourly(*) 2 km Convective/stratiform
type classification Radar Site map
over an area of Convective/Stratiform
300 km x 300 km for Rain Map
single radar sites,
and 724 km x 658 km
for Texas and 512km
x 704 km for Florida
multiple radar sites
[* Volume scans
at radar sampling
within half hour of
TRMM satellite
coincidence]
2A-55 Instantaneous radar Half-Hourly(*) 2 km Vertical structure of
reflectivity and Horizontal and 1.5 km radar echo
vertical profile Vertical Radar Site
statistics over an 3-D Reflectivity
area of 300 km x 300
km for single radar
sites, and 724 km x
658 km for Texas and
512 km x 704 km for
Florida multiple
radar sites
[* Volume scans at
radar sampling
within half hour
of TRMM satellite
coincidence]
3A-55 Vertical profile of Monthly 2 km Radar Vertical structure of
reflectivity and Site 3-D Rain Map radar echo
contoured frequency
by altitude diagrams
for stratiform,
convective, and
anvil rain over land
and water
2A-53 Instantaneous rain Half-Hourly(*) 2 km Rain map
rate over an area Radar Site Rain Map
of 300 km x 300 km
for single radar
sites, and 724 km x
658 km for Texas and
512 km x 704 km for
Florida multiple
radar sites
[* Volume scans at
radar sampling
within half hour
of TRMM satellite
coincidence]
3A-53 Surface rain total 5-Day 2 km Ground Rain map
from ground radar Radar Site Rain Map
3A-54 Surface rain total Monthly 2 km Ground Rain map
from ground radar Radar Site Rain Map
2A-56 Time series of rain One Minute Average In situ data
gauge rain rates and Peak Rain Gauge
over the radar site Rain Rate
rain gauge network
2A-57 Time series of Rain Drop Size In situ data
disdrometer rain Distribution at
drop size Disdrometer Sampling
distribution over
the radar site
HDF is the standard data format for the entire EOSDIS, including the TRMM
Project. HDF was developed by the National Center for Supercomputing
Applications (NCSA) Software Development Group. Additional explanation
of HDF can be found at the
NCSA HDF Web site (http://hdf.ncsa.uiuc.edu/).
HDF provides several different "data models" which can be used to store
data products, including
Scientific Data Sets (SDS), 8-bit and 24-bit Raster Image Sets (RIS),
Vdatas, and Vgroups. An SDS is a multi-dimensional array, and a Vdata
is a binary table. In addition to the data models, HDF allows the
inclusion of metadata with each data file. Metadata are referred to
as Global Attributes and include such information as the mission
and sensor characteristics, and when and how the data were processed.
Along with that information, the Global Attributes also
describe the start and end times of a data file, geographic location,
and data quality.
TRMM Algorithm Flow Diagrams can be obtained from the Goddard DAAC's
Precipitation Web site
(http://disc.sci.gsfc.nasa.gov/precipitation/documentation.shtml).
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Atmos Composition
