In this study, deep neural network is utilized as another approach for improving accuracy of the precipitation based on microwave-sensor. And The ensemble Bayesian model averaging(EBMA), which employs a weighting scheme for each member using posterior probability, in order to produce a more improved blending of precipitation from multi-satellite and to evaluate the effect of accuracy improvement. Experiments to improve rain rate were carried out based on data obtained from Global Precipitation Measurement (GPM) Microwave Imager (GMI). Input data for the DNN model include 7 brightness temperatures (Tb), ice water path (IWP), convective rain rate, scattering index (SI) and land sea mask is used. The experiment for blending of precipitation product was performed using rain rate product of three satellites and sensors, namely GMI of GPM core observatory, special sensor microwave imager/sounder (SSMI/S) of the Defense Meteorological Satellite Program (DMSP) F16 and microwave humidity sounder (MHS) of NOAA-18. In both experiments, precipitation product of the Dual-frequency Precipitation Radar (DPR) of CO was used as reference data. The probability density function(PDF) of gamma distribution combined with logistic regression is used to estimate the probability and quantity of precipitation for considering the characteristics of precipitation. And then, the exponent for these two functions and the percentile threshold of the cumulative density function were set by optimizing simulations. After that, the validation statistics of the blending precipitation through comparison with precipitation obtained from DPR is carried out.

Date Published: 7 October 2019
PDF: 6 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115522 (7 October 2019); doi: 10.1117/12.2533138

Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)