Title :
Multiparameter radar snowfall estimation using neural network techniques
Author :
Rongrui Xiao ; Chandrasekar, V.
Author_Institution :
Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA
Abstract :
WISP94 CSU-CHILL radar data and ground snowgage measurements at Stapleton International Airport (SIA) and Denver International Airport (DIA), Denver, Colorado, are analyzed in this paper. Traditionally, the radar estimation of ground snowfall is estimated by Z-S relations. The performance of such parametric relations are not satisfactory due to the complexities of the snow process. In this paper a radial-basis function neural network based algorithm is applied to map the relationship between the radar observations and ground snowfall measurements. The development of the neural network based technique, and the snowfall estimation results are presented
Keywords :
atmospheric techniques; geophysical signal processing; geophysics computing; meteorological radar; neural nets; radar signal processing; remote sensing by radar; snow; Denver; Denver International Airport; Stapleton International Airport; USA; United States; WISP94 CSU-CHILL radar; algorithm; atmosphere; ground snowfall; measurement technique; meteorological radar; multiparameter radar; neural net; neural network; parametric relations; radar remote sensing; radial-basis function; snow; snowfall; Airports; Function approximation; Ice; Meteorological radar; Neural networks; Radar measurements; Radial basis function networks; Reflectivity; Snow; Storms;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1996. IGARSS '96. 'Remote Sensing for a Sustainable Future.', International
Conference_Location :
Lincoln, NE
Print_ISBN :
0-7803-3068-4
DOI :
10.1109/IGARSS.1996.516405
