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A new methodology for rain identification from passive microwave data in the Tropics using neural networks
Author(s) -
Kacimi Sahra,
Viltard Nicolas,
Kirstetter PierreEmmanuel
Publication year - 2013
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.2114
Subject(s) - brightness temperature , remote sensing , environmental science , precipitation , satellite , meteorology , perceptron , radar , global precipitation measurement , artificial neural network , scale (ratio) , radiometer , microwave , computer science , geology , machine learning , geography , cartography , telecommunications , aerospace engineering , engineering
The detection of rainfall remains a challenge for the monitoring of precipitation from space. A methodology is presented to identify rain events from spaceborne passive microwave data using neural networks. We focus on BRAIN, the algorithm that provides instantaneous quantitative precipitation estimates at the surface, based on the MADRAS radiometer onboard the Megha‐Tropiques satellite. A version of BRAIN using data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has been used to compare several multilayer perceptrons (MLP) trained on different combinations of TMI brightness temperatures with the conventional GSCAT‐2 algorithm approach used for rainfall detection. These classiers were compared at a global scale to reference values from the TRMM Precipitation Radar (PR). They were also compared to ground measurements using two 1° × 1° dense rain‐gauge networks from different climatic zones in West Africa to assess the inuence of rainfall types. At the global scale the MLPs provide better Probability of Detection than the GSCAT‐2 decision tree but tend to have a higher False Alarm Rate. While no unique solution exists given the strong regional dependence of the classiers' performances, the screen based on the 19, 21 and 85 GHz channels provides the best detection results at the instantaneous scales. As to accumulated rainfall, the screen that exhibits the lower bias relative to the PR makes use of the 37 and 85 GHz channels. The evaluation over West Africa using 10 years of TRMM overpasses shows that MLPs are in better agreement with both the PR and the gauges than GSCAT‐2. The MLP trained on the 37 and 85 GHz channels increases the Probability of Detection by nearly 35% compared to the former screening over the two studied regions. Better results are obtained in the case of organized systems. Copyright © 2013 Royal Meteorological Society