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Neural network retrieval of wind velocity profiles from satellite data

Published online by Cambridge University Press:  01 March 2000

J Cogan ,
W Gutman ,
E Measure ,
D Bustamante  and
G Vaucher
J Cogan
Affiliation:
Army Research Laboratory, Information Science and Technology Directorate, White Sands Missile Range, NM 88002, USA
W Gutman
Affiliation:
New Mexico State University, Physical Sciences Laboratory, Las Cruces, NM 88003, USA
E Measure
Affiliation:
Army Research Laboratory, Information Science and Technology Directorate, White Sands Missile Range, NM 88002, USA
D Bustamante
Affiliation:
New Mexico State University, Physical Sciences Laboratory, Las Cruces, NM 88003, USA
G Vaucher
Affiliation:
Army Research Laboratory, Information Science and Technology Directorate, White Sands Missile Range, NM 88002, USA
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Abstract

A method has been developed for retrieval of wind velocity profiles from satellite sounder radiances using a neural network technique. Each wind velocity represents a mean value for a layer approximately centred at that standard height or pressure level. The neural network input vector includes sounder radiances from the Geostationary Operational Environmental Satellite (GOES), plus ancillary information such as latitude and longitude. A set of ‘co-incident’ rawinsonde soundings provide the ‘truth’ data set. In the initial results root mean square errors for the standard levels from 850 to 300 hPa have about the same magnitude as those for current methods such as tracking of moisture features in GOES imagery. As with more traditional methods, the largest root mean square error occurs near the tropopause. Although the results to date appear highly promising, considerable further work is needed before we have an operationally capable technique.

Type
Research Article
Information
Meteorological Applications , Volume 7 , Issue 1 , March 2000 , pp. 69 - 74
Copyright
© 2000 Meteorological Society

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