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

Volume

1

fYear

1996

fDate

27-31 May 1996

Firstpage

566

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/IGARSS.1996.516405

Filename

516405