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Tropical Rainfall Measuring Mission (TRMM)

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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 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. 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.

Acknowledgment:

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 610.2, 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.

 

 

Table of Contents:

1. Data Set Overview:

TRMM is a comprehensive and systematic program designed to increase the extent and accuracy of tropical rainfall measurement. The TRMM science program involving some 3 dozen national and international scientists consists of a broad research effort which includes development of cloud models, rain retrieval algorithms for the space sensors, use of TRMM measurements with other satellite data to improve sampling, a surface-based verification system, and a TRMM science data and information system (TSDIS).

Data Set Contents:

The table below summarizes the TRMM standard products available at the Goddard DAAC. Level 1 products are the VIRS calibrated radiances, the TMI brightness temperatures, and the PR return power and reflectivity measurements. Level 2 products are derived geophysical parameters at the same resolution and location as those of the Level 1 source data. Level 3 products are the time-averaged parameters mapped onto a uniform space-time grid.

 

TRMM Products at the Goddard DAAC

 

 

  Visible Infrared Scanner TRMM Microwave Imager Precipitation Radar Combined Products Ground Validation (GV)
Level 1 Visible & IR radiances Microwave brightness temperatures Radar return power & reflectivity NA Cal. radar reflectivity at each GV site
Level 2 NA TMI profile for CLW, prec. water, cloud ice, prec. ice, latent heat, & surface rain PR surface cross-section & path attenuation, rain type, storm, & freezing height; PR profile for rain rate, reflec., attenuation, & rain top/bottom height Rain rate, drop size dist. parameters, path integrated attenuation Rain existence, rain map, rain type, 3-D reflectivity, rain gauge, disdrometer
Level 3 NA TMI monthly rainfall, rain rate, rain frequency, & freezing height PR monthly surface rain total, rain profile at 2, 4, 6, 10 & 15 km, fractional rain, storm height histogram, snow ice layer, surface rain rate, & path attenuation Monthly surface rainfall, CLW, rain water, cloud ice, & grauples; combined instruments calibration, global gridded rainfall Rain map, 3-D map

 

Related Data Sets:

 

 

 

Global Precipitation Data
(Available via anonymous FTP)
 
Arkin & Janowiak GPI: IR-Based Monthly Rainfall for the GPCP
Chang SSM/I Derived Monthly Rain Indices for the GPCP
GPCC Rain Gauge Analysis for the GPCP
GPCP Version 2 Combined Precipitation Data Set
Jaeger Global Monthly Mean Precipitation
Legates Surface & Ship Observations of Precipitation
NOAA/NASA SSM/I Pathfinder Precipitation
 
Interdisciplinary Hydrology Data Collections
(Available via anonymous FTP)
 
SSM/I Surface Turbulent Fluxes
Satellite Retrieved Sea Surface Radiation Budget
GPCC Global Rain Gauge Analysis

 

Title of Investigation:

Tropical Rainfall Measuring Mission (TRMM)

Investigator(s) Name and Title:

Robert Adler
Jun Awaka
William Barnes
Alfred T.C. Chang
Long Chiu
Brad Ferrier
Bradford Fisher
Toshio iguchi
Christian Kummerow
Robert meneghini
James Shiue
Eric Smith

Technical Contact(s) Name, Address, Telephone, Fax, and E-mail:

HYDROLOGY DATA SUPPORT TEAM
GSFC DAAC
hydrology@daac.gsfc.nasa.gov
Phone: (301) 614-5224
FAX (301) 614-5268

Distributed Active Archive Center
Code 610.2
NASA Goddard Space Flight Center
Greenbelt, MD 20771
USA

2. Applications and Derivation:

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Usage:

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Theory of Measurements:

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Derivation Techniques and Algorithms:

The June 1998 interim release includes most level 1 TRMM standard products.

 

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 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 combined rain structure (2B-31) and VIRS calibration (1B-01) to adjust IR estimates from geosynchronous IR observations. Global estimates are made by adjusting the GOES Precipitation Index (GPI) to the TRMM estimates. The output is rainfall for 1 x 1 degree grid boxes for a 5-day (pentad) period.

The global rainfall algorithm (3B-43) combines the TRMM estimates; the calibrated IR generated by combined instrument rain calibration (3B-42); gridded SSM/I estimates from TRMM algorithm developers; 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).

 

Formulae:

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Processing Steps:

TRMM satellite data for each orbit are stored on board and transmitted to the ground via the NASA's Tracking and Data Relay Satellite System (TDRSS.) The TRMM science data are processed by the Precipitation Processing System (PPS) also formerly known as the TRMM Science Data and Information System (TSDIS) into standard products. These products are transferred to the Goddard DAAC for archival and distribution.

Processing Changes:

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SpecialCorrections/Adjustments:

The average operating altitude for TRMM was changed from 350 to 403 kilometers during the period of August 7-24, 2001. Because of this orbit boost maneuver, algorithms of post-boost TRMM PR (Precipitation Radar) products have been changed. Here are the Caveats for Post-boost PR Products from TRMM PR algorithm scientists.

Calculated Variables:

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Graphs and Plots:

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3. Data Description and Access:

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).

Format:

 

 

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.

For detailed information, please see PPS (formerly TSDIS) file specifications (http://pps.gsfc.nasa.gov/tsdis_redesign/SelectedDocs.html).

 

 

Data Organization:

Data Granularity:

A general description of data granularity as it applies to the IMS can be found in the EOSDIS Glossary.

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Volume:

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Cost:

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Data Access:

 

 

TRMM standard products can be ordered from the Goddard DAAC. Data currently available can be accessed from the TRMM Data Search and Order Web Interface. In addition to the standard products, the Goddard DAAC also will provide, as part of its value-added TRMM support to facilitate analysis and processing by users, certain special products, including:

  1. Orbital gridded products, e.g., for the TMI Hydrometeor Profile standard product, a 0.5 degree by 0.5 degree resolution special product
  2. Geographical subsets of the orbital gridded products
  3. Parameter subsets of selected standard products
  4. Satellite and ground radar coincident subsetted products for the Ground Validation sites
Data sets will be distributed via FTP. Potential TRMM data users, especially those with specific needs, are urged to contact the Hydrology Data Support Team:

hydrology@daac.gsfc.nasa.gov .

 

 

Data Archive Center:

Contact for Data Center Access Information:

Help Desk
Goddard DAAC, Code 610.2
NASA Goddard Space Flight Center
Greenbelt, MD 20771
daacuso@daac.gsfc.nasa.gov

Product Availability:

 

 

Quick Looks - OnLine TMI Quick Look images TMI quick-looks available online. Each quick-look is generated at a resolution of 1/4 degree, thus generating an image of 1440x720 pixels, or a file size of about 500k.

TSDIS - TRMM Science Data and Information System

The real-time processing and post-processing of the TRMM science data is performed by the TRMM Science Data and Information System (TSDIS). Working with the TRMM principal investigators and science algorithm developers, TSDIS maintains the operational science data processing system and ensures the timely processing of all TRMM science instrument data. During routine operations, raw instrument data is received in near real-time by TSDIS and then processed by the first tier of science algorithms to produce calibrated, swath-level instrument data. Using this calibrated, swath-level instrument data, the second tier of algorithms are used to compute geophysical parameters, such as precipitation rate, also at the swath-level resolution. At the final stage of TSDIS processings, the third tier algorithms produce gridded geophysical parameters from the first- and second-tier instrument data. All TRMM products are archived and distributed by the Goddard Distributed Active Archive Center (DAAC). For further information concerning TSDIS operations go to ths TSDIS homepage.

DAAC - Distributed Active Archive System

The operational archiving and distribution to the public of all TRMM science data products is provided by the Goddard Distributed Active Archive Center (DAAC). In addition to archiving and distributing the TRMM science data, the DAAC also provides necessary information and support for manipulating these data files, which are provided in NCSA's Hierarchical Data Format (HDF). These files are generally distributed online. FInally, the DAAC provides front-line support for any questions concerning the TRMM science data. TO obtain TRMM science data, go to the Goddard DAAC homepage.

 

Reading the Media:

How to Cite the Data Set:

Tropical Rainfall Measuring Mission (TRMM)

How to Cite the Guide Document:

trmm_dataset.gd.html

4. Data Characteristics:

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Study Area:

global

Spatial Coverage: 38 oN - 38 oS

Spatial Coverage Map:

TRMM coverage map with GV stations
Map of TRMM Satellite Coverage showing permanent ground validation stations

Spatial Resolution:

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Projection:

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Grid Description:

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Temporal Coverage:

Temporal Coverage Map:

The mission had a designed life of three years from launch on November 28, 1997. An orbit boost in August, 2001 has added a projected three additional years to the mission.

Temporal Resolution:

The satellite acquires approximately 16 orbits of data per day for mapping tropical rainfall between latitudes of 38 degrees north and south of the equator.

Parameter or Variable:

Parameter Description:

 

 

Satellite Products
TSDIS ID Description Data Product Product Type
1A-01
1B-01
Raw/Calibrated VIRS(0.63, 1.6, 3.75,10.8, and 12 um)radiances at 2.2 km resolution, over a 720 km swath Visible and Infrared Radiance Raw/Calibrated sensor data
1A-11
1B-11
Calibrated TMI (10.65, 19.35, 21,37, and 85.5 GHz) brightness temperatures at 5-45 km resolution, over a 760 km swath Microwave Brightness Temperature Calibrated sensor data
1B-21 Calibrated PR (13.8 Radar Power GHz) power at 4 km horizontal and 250 m vertical resolutions, over a 220 km swath Radar Power Calibrated sensor data
1B-51 GV Radar Radar Power GV Reflectivity GV Differential Reflectivity ZDR
1C-21 PR (13.8 GHz) reflectivity at 4 km horizontal and 250 m vertical resolutions, over a 220 km swath Radar Reflectivity Calibrated sensor data
2A-12 Hydrometeor (cloud liquid water,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 Hydrometeor Vertical Profile Vertical structure of rainfall
2A-21 PR (13.8 GHz) normalized surface cross section at 4 km horizontal resolution, and path attenuation in case of rain, over a 220 km swath Radar Surface Cross-Section Surface reflectivity [?]
2A-23 Rain type; freezing and bright band heights from PR (13.8 GHz) at 4 km resolution, over a 220 km swath Radar Qualitative Qualitative data
2A-25 PR (13.8 GHz) rain rate, reflectivity, attenuation, profiles at 250 m vertical and 4 km horizontal resolutions, over a 220 km swath Radar Rainfall Vertical Profile Vertical structure of rainfall
2B-31 Combined PR/TMI rain rate and path integrated attenuation at 250 m vertical and 4 km horizontal resolutions, over a 220 km swath Combined Rainfall Vertical Profile Vertical structure of rainfall
3A-11 Rain rate,conditional rain rate, rain frequency and freezing height over latitude band from 38 degree N to 38 degree S from TMI Monthly 5 x 5 Degree Oceanic Rainfall Surface rainfall
3A-25 Total and conditional rain rate, radar reflectivity, path 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 Monthly 5 x 5 Degree and 0.5 x 0.5 Degree Spaceborne Radar Rainfall Vertical structure of rainfall
3A-26 Rain rate probability distribution at surface, 2 and 4 km over latitude band from 38 degree N to 38 degree S from PR Monthly 5 x 5 Degree Surface Rain Accumulation Surface rainfall
3B-31 Rain rate, cloud liquid water, rain water, cloud ice, grauples at 14 levels over latitude band from 38 degree N to 38 degree S from PR and TMI Monthly 5 x 5 Degree Combined Rainfall Vertical structure of rainfall
3B-42 Calibrated geosynchronous IR rain rate using TRMM estimates 5 day 1 x 1 Degree TRMM and Other-GPI Calibration Rainfall TRMM and other sources rainfall
3B-43 Merged rain rate from TRMM, geosynchronous SSM/I, rain gauges IR, 5 day 1 x 1 Degree TRMM and Other Sources Rainfall TRMM and other sources rainfall
Ground Validation Products
TSDIS ID Description Data Product Product Type
1B-51 Volume scan of radar reflectivity, differential reflectivity and mean velocity (if available) truncated at 230 km range Radar Reflectivity atOriginal Radar Resolution, Coordinate, and Sampling Calibrated sensor data
1C-51 Volume scan of calibrated radar reflectivity and differential reflectivity (if available) and corresponding QC masks, truncated at 200 km range [* Volume scans at radar sampling within half hour of TRMM satellite coincidence] Half-Hourly(*)Calibrated Radar Reflectivity at Original Radar Resolution and Coordinate Calibrated sensor data
2A-52 Percent of rain in radar volume scan [* Volume scans at radar sampling within half hour of TRMM satellite coincidence] Half-Hourly(*) Rain Existence Qualitative data
2A-53 Instantaneous rain rate over an 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] Half-Hourly(*) 2 km Radar Site Rain Map Rain map
2A-54 Instantaneous rain type classification over an area of 300 km x 300 km for 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] Half-Hourly(*) 2 km Radar Site Convective/Stratiform Rain Map Convective/stratiform map
2A-55 Instantaneous radar reflectivity and vertical profile statistics over an 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] Half-Hourly(*) 2 km Horizontal and 1.5 km Vertical Radar Site 3-D Reflectivity Vertical structure of radar echo
2A-56 Time series of rain gauge rain rates over the radar site rain gauge network One Minute Average and Peak Rain Gauge Rain Rate In situ data
3A-53 Surface rain total from ground radar 5-Day 2 km Ground Radar Site Rain Map Rain map
3A-54 Surface rain total from ground radar Monthly 2 km Ground Radar Site Rain Map Rain map
3A-55 Vertical profile of reflectivity and contoured frequency by altitude diagrams for stratiform, convective, and anvil rain over land and water Monthly 2 km Radar Site 3-D Rain Map Vertical structure of radar echo

 

 

Unit of Measurement:

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Parameter Source:

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Parameter Range:

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Sample Data Record:

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Error Sources:

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Quality Assessment:

Validation by Source:

A broad and integrated observational program of precipitation and related climate research designed to meet the specific science validation objectives established by the TRMM science team and consistent with program requirements established by NASA Headquarters was designed and implemented by the TRMM Office and specifically includes these objectives:

  • organize a Global Validation Program (GVP) consisting of 10 or more ground validation sites throughout the tropics (see map below);
  • improve measurement techniques (e.g., rain gauges, disdrometers, acoustics);
  • improve radar rain estimation techniques (e.g., radar/reflectivity algorithms, dual polarization and dual frequency applications);
  • investigate microwave attenuation techniques (e.g., from satellite transponders and surface links);
  • precipitation research (e.g., characterization of precipitating systems, precipitation physics, etc);
  • develop representative rainfall climatologies from existing sources of data;
  • development of quality control, processing and analysis software for radar, rain gauge and disdrometer data;
  • support the ground validation science team, the global validation sites, and the TRMM science team;
  • quality control, catalog, and archive all acquired radar, rain gauge, and related data;
  • participate in the planning and execution of all TRMM sponsored field experiments related to the validation of TRMM observations.

Confidence Level or Accuracy Judgment:

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Measurement Error for Parameters:

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Additional Quality Assessments:

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Verification by Data Center:

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5. Usage Guidance:

Limitations of the Data:

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Known Problems with the Data:

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Other Relevant Information about the Study:

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Future Modifications and Plans:

In order to satisfy opposing requirements for early data distribution and the highest possible data quality, TRMM has reprocessed all products with improved algorithms approximately once per year. This section, aside from presenting general product information, updates the performance of each algorithm as information becomes available to the science team. Data users should check this site before working with any TRMM data, and occasionally thereafter as more informaton becomes available. All information is tied to the data version number as distributed by the DAAC.

6. Acquisition Materials and Methods:

Field Collection Environment:

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Field Campaign Mission Objectives:

Designed for TRMM Validation, this series of experiments provides ground truth for use in algorithm development for the Tropical Rainfall Measuring Mission (TRMM), a NASA and National Space Development Agency of Japan (NASDA) coordinated mission that launched the TRMM satellite on 28 November 1997 with a unique complement of sensors to remotely observe rainfall throughout the global tropics.

Field Campaign Program Management:

The TRMM global validation effort consisted of five field campaigns, TEFLUNA, SCSMEX, TEFLUNB, TRMM-LBA, and KWAJEX that were designed and implemented by the TRMM Office.

Or

Source or Platform Collection Environment:

 

 

Initial 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

 

Post-boost Orbit parameters for the TRMM satellite:
Orbit Injection 380 km, +/- 10 km
Nominal Mission Altitude 403 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

 

 

Source or Platform Mission Objectives:

The Tropical Rainfall Measuring Mission (TRMM) is a joint space project with Japan. TRMM is designed to measure tropical precipitation and its variation from a low-inclination orbit combining a suite of sensors to overcome many of the limitations of remote sensors previously used for such measurements from space. The TRMM Science Team, under the leadership of a Goddard Project Scientist, determined that a fundamental objective of TRMM was to understand the role of latent heat in driving the circulation of the global atmosphere. With the inclusion of a rain radar, TRMM will provide the first opportunity to estimate the vertical profile of the latent heat that is released through condensation. The tropical precipitation and consequent heating are concentrated in the Equatorial Intertropical Convergence Zone (ITCZ), (Figure 1). The TRMM rainfall data is 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 Nio on climate could be addressed with a reliable, extended time series of tropical rainfall observations as well.

Source or Platform Program Management:

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Coverage Information:

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Attitude Characteristics:

The TRMM orbit is circular and is at an altitude of 218 nautical miles (350 km) and an inclination of 35 degrees to the Equator

Data Collection System:

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Communication Links:

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List of Sensors or Instruments:

Precipitation Radar (PR)
TRMM Microwave Imager (TMI)
Visible Infrared Scanner (VIRS)
Clouds and Earths Radiant Energy System (CERES)
Lightning Imaging Sensor (LIS)

Ground Segment Information:

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Data Acquisition and Processing:

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Latitude Crossing Times:

Sensor or Instrument Description:

 

The TRMM satellite carries five sensors:
  • The Precipitation Radar (PR) determines the vertical distribution of precipitation by measuring the "radar reflectivity" of the cloud system and the weakening of a signal as it passes through the precipitation. A unique feature of the PR is the measurement of rain over land, where passive microwave channels are less effective.

  • The TRMM Microwave Imager (TMI) is a multichannel radiometer, whose signals in combination can measure rainfall quite accurately over oceans and somewhat less accurately over the land. The TMI and PR data yields the primary precipitation data sets.

  • The Visible Infrared Scanner (VIRS) measures radiance in five bandwidths from the visible through the infrared spectral regions. Scientists use Infrared (IR) data to make rough estimates of tropical precipitation. The VIRS, PR and TMI data improve the techniques by which scientists use IR data from other satellites to calculate rainfall. This is the third component of TRMM's rain package.

  • The Lightning Imaging Sensor (LIS) is an optical telescope and filter imaging system that investigates the distribution and variability of both atmospheric and cloud-to-ground lightning over the Earth. These instruments contribute to our understanding of storm dynamics and correlate to levels of precipitation and the release of latent heat.

  • The Clouds and the Earth's Radiant Energy System (CERES) is a visible/infrared sensor designed especially to measure energy rising from the surface of the Earth and the atmosphere including its constituents (e.g., clouds and aerosols). This energy, when balanced with the energy received by the Earth from the Sun, constitutes the Earth's radiation budget. Understanding the radiation budget from the top of the atmosphere to the Earth's surface, is important to understanding climate and its variability.

Figure 2. The TRMM satellite and scanning geometries of the precipitation radar (PR), the visible and infrared radiometer (VIRS) and the TRMM microwave imager (TMI) are illustrated along with an outline of the mission profile. The three sensors comprise the "rain package." The Earth radiation and lightning sensors (CERES and LIS, respectively) are located behind the two-meter-square PR antenna. (Illustration courtesy of Japan's National Space Development Agency - NASDA).

 

Key Variables:

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Principles of Operation:

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Sensor or Instrument Measurement Geometry:

 

  Visible Infrared Scanner TRMM Microwave Imager Precipitation Radar
Frequency/Wavelength 0.63, 1.6, 3.75, 10.8, 12 um 10.65, 19.35, 37.0, 85.5 GHz dual polarization, 22.235 GHz vertical polarization 13.8 GHz horizontal polarization
Scanning Mode Cross track Conical Cross track
Ground Resolution 2.1 km Ranges from 5 km at 85.5 GHz to 45 km at 10.65 GHz 4.3 km at nadir
Swath Width 720 km 760 km 220 km

 

Manufacturer of Sensor or Instrument:

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Calibration:

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Specifications:

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Tolerance:

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Frequency of Calibration:

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Other Calibration Information:

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Data Acquisition Methods:

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Observations:

Data Notes:

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Field Notes:

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7. References:

 

 

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.

 

 

8. Glossary and Acronyms:

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9. Document Information:

Document Revision Date:Thu Jun 6 15:26:56 EDT 2002

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(This will be filled in when the guide is ready to be released.)

Document Revision Date:Thu Jun 6 15:26:56 EDT 2002
Document Review Date: 5/31/2002
Document Author: Pat Hrubiak

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