An Artificial-Neural-Network-Based Integrated Regional Model for Rain Retrieval Over Land and Ocean

An integrated regional model is proposed for rain-rate retrievals over land/ocean from the brightness temperature (Tb) values of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The polarization-corrected temperature calculated from the 85.5-GHz channels is also considered as one of the inputs along with the nine channel Tb values. This model is applicable over the region between and . For this purpose, an artificial neural network is utilized. The collocated precipitation radar (PR) near-surface rain rates as given by a 2A25 data product is considered as a target value. The methodology consists of the separation of land and ocean pixels, the separation of stratiform and convective pixels over land/ocean, and the selection of important features (inputs) for the multilayer perceptron network by the feature selection technique for each group. For the separation of land/ocean pixels, the Tb values of the 10.65-GHz vertical channel are utilized. The values are utilized to separate the stratiform and convective pixels both over land and ocean. The rain retrieval from the developed model is validated with TRMM PR. Overall result shows the better agreement of the model-retrieved rain rate with the PR observation compared to the TMI (2A12) rain rate particularly over land. The rain retrieved from the developed model is further validated with Doppler weather radar. A reasonably good agreement is observed between these two estimations.

[1]  C. Prabhakara,et al.  A TRMM Microwave Radiometer Rain Rate Estimation Method with Convective and Stratiform Discrimination , 2000 .

[2]  Sanjay Sharma,et al.  A soft computing approach for rainfall retrieval from the TRMM microwave imager , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[3]  E. Anagnostou,et al.  Overland Precipitation Estimation from TRMM Passive Microwave Observations , 2001 .

[4]  Christian D. Kummerow,et al.  Rain Retrieval from TMI Brightness Temperature Measurements Using a TRMM PR-Based Database , 2006 .

[5]  Christian Kummerow,et al.  A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors , 1996, IEEE Trans. Geosci. Remote. Sens..

[6]  H. Michael Goodman,et al.  Precipitation retrieval over land and ocean with the SSM/I - Identification and characteristics of the scattering signal , 1989 .

[7]  Nikhil R. Pal,et al.  SOFM-MLP: a hybrid neural network for atmospheric temperature prediction , 2003, IEEE Trans. Geosci. Remote. Sens..

[8]  M. Todd,et al.  Estimates of Rainfall over the United Kingdom and Surrounding Seas from the SSM/I Using the Polarization Corrected Temperature Algorithm , 1995 .

[9]  Dong-Bin Shin,et al.  The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors , 2001 .

[10]  C. Prabhakara,et al.  A Model for Estimation of Rain Rate on Tropical Land from TRMM Microwave Imager Radiometer Observations , 2005 .

[11]  Emmanouil N. Anagnostou,et al.  Regional Differences in Overland Rainfall Estimation from PR-Calibrated TMI Algorithm , 2005 .

[12]  Ralph Ferraro,et al.  Next generation of NOAA/NESDIS TMI, SSM/I, and AMSR‐E microwave land rainfall algorithms , 2003 .

[13]  Chris Kidd,et al.  On rainfall retrieval using polarization-corrected temperatures , 1998 .

[14]  C. Kummerow,et al.  The Tropical Rainfall Measuring Mission (TRMM) Sensor Package , 1998 .

[15]  Chris Kidd,et al.  Rainfall Estimation from a Combination of TRMM Precipitation Radar and GOES Multispectral Satellite Imagery through the Use of an Artificial Neural Network , 2000 .

[16]  E. A. Smith,et al.  Design of an inversion-based precipitation proflie retrieval algorithm using an explicit cloud model for initial guess microphysics , 1994 .

[17]  G. Huffman,et al.  Global tropical rain estimates from microwave‐adjusted geosynchronous IR data , 1994 .

[18]  Nazzareno Pierdicca,et al.  Precipitation retrieval from spaceborne microwave radiometers based on maximum a a posteriori probability estimation , 1996, IEEE Trans. Geosci. Remote. Sens..

[19]  Ralph Ferraro,et al.  The Development of SSM/I Rain-Rate Retrieval Algorithms Using Ground-Based Radar Measurements , 1995 .

[20]  Grant W. Petty,et al.  Validation and Intercomparison of SSM/I Rain-Rate Retrieval Methods over the Continental United States , 1998 .

[21]  Ye Hong,et al.  Separation of Convective and Stratiform Precipitation Using Microwave Brightness Temperature , 1999 .