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GPM, F17, SSMI Level 2A Radiometer Profiling VV03A

Summary

Type of release
a one-off release of a single dataset

Data Licence
Not Applicable

Content Licence
Creative Commons CCZero

Verification
automatically awarded

Release Date
26 November 2014
Modified Date
8 July 2015
Update Frequency
irregular
Publishers
National Aeronautics and Space Administration
Keywords
atmosphere, atmospheric-water-vapor, earth-science, precipitation
Identifier
gpm-f17-ssmi-level-2a-radiometer-profiling-vv03a
Landing Page
http://reverb.echo.nasa.gov/reverb?selected=C212626083-GSFCS4PA
Maintainers
PLEASE CONTACT PASSIVE MICROWAVE ALGORITHM TEAM FACILITY helpdesk@pps-mail.nascom.nasa.gov
Theme
GPM
Language
en-US

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Description

The 2AGPROF (also known as, GPM GPROF (Level 2)) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors: GMI, SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18) AMSR2 (GCOM-W1), TMI MHS (NOAA 18&19, METOP A&B), ATMS (NPP), SAPHIR (MT1) This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are near-realtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided. The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an a-priori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty. ABSTRACT

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This dataset has been created by US Government which means it is required to be in the public domain. However US copyright law only allows open access by US citizens, we have assumed the data is equivalently licensed as CC0 for the rest of the world as this is in the spirit of the US Government’s Open Data policy.

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