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Readme for the Tropical Rainfall Measuring Mission (TRMM) Data Set

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
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

Summary

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 includevisible 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.

Sponsor

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.

Future Updates

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.

Data Flow Description

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.

File Specification Description

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:

  1. 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.

     

  2. 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.

     

  3. 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.

     

  4. 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.

Tools for Visualizing Data

The Goddard DAAC is providing the following tools to help its users to visualize data which are in the Hierarchical Data Format (HDF).

  1. 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.

     

  2. 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

Data Access Information

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/).

Other precipitation data sets are available via anonymous ftp. For more information about data sets on ftp, see our Data Products chart.

References

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.


Appendix A - TRMM Mission

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.


Appendix B - TRMM Platform

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

Appendix C - TRMM Orbit and Instruments

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


Appendix D - TRMM Products

 
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

Appendix E - HDF

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.


Appendix F - TRMM Algorithm Flow

TRMM Algorithm Flow Diagrams can be obtained from the Goddard DAAC's Precipitation Web site (http://disc.sci.gsfc.nasa.gov/precipitation/documentation).

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