Title :
Radar rainfall estimation from vertical reflectivity profile using neural network
Author :
Xu, Gang ; Chandrasekar, V.
Author_Institution :
Colorado State Univ., Fort Collins, CO, USA
Abstract :
An adaptive radial basis function (RBF) neural network to estimate the ground rainfall from a vertical profile of reflectivity factor (Z) is presented in this paper. This RBF network was applied to two months of WSR-88D radar data to estimate rainfall. Results show that the adaptive RBF developed here can estimate rainfall fairly well. Results were also compared with the WSR-88D, Z-R relationship and the Z-R algorithm derived from previous day observations
Keywords :
atmospheric techniques; geophysical signal processing; meteorological radar; radar signal processing; radial basis function networks; rain; remote sensing by radar; RBF neural network; WSR-88D radar data; adaptive radial basis function neural network; ground rainfall; radar rainfall estimation; vertical reflectivity profile; Neural networks; Radar;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-7031-7
DOI :
10.1109/IGARSS.2001.978328
