Paper Info
Title
Bayesian Algorithm For Microwave-Based Precipitation Retrieval: Description And Application To Tmi Measurements Over Ocean
Abstract
A physically oriented inversion algorithm to retrieve precipitation from satellite-based passive microwave measurements named the Bayesian algorithm for microwave-based precipitation retrieval (BAMPR) is proposed. First, we illustrate the procedure that BAMPR follows to produce precipitation estimates from observed multichannel brightness temperatures. Retrieval products are the surface rain rates, columnar equivalent water contents, and hydrometeor content profiles, together with the associated estimation uncertainties. Numerical tests performed on simulated measurements show that retrieval errors are reduced when a rain type and pattern classification procedure is employed, and that estimates are quite sensitive to the adopted error model. Finally, for different tropical storms that were observed by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), we compare the rain retrieved from BAMPR relative to those retrieved from the Goddard Profiling (Gprof) algorithm and the Precipitation Radar-adjusted TMI estimation of rainfall (PATER) algorithm. Despite a similar inversion approach, the algorithms exhibit different performances that can be mainly related to different training databases and retrieval constraints such as cloud classification.
Year
DOI
Venue
2005
10.1109/TGRS.2005.844726
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
atmospheric remote sensing, spaceborne microwave radiometry, precipitation retrieval
Journal
43
Issue
ISSN
Citations 
4
0196-2892
8
PageRank 
References 
Authors
2.06
3
6
Name
Order
Citations
PageRank
Alessandra Tassa1113.24
Alberto Mugnai25617.07
Frank Silvio Marzano312928.79
Peter Bauer44816.74
Peter Bauer54816.74
J. P. V. P. Baptista68420.66