Development of a single

Krasnopolʹsky, V. M.; Breaker, L. C.; Gemmill, William H.

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

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Development of a single "all-weather" neural network algorithm for estimating ocean surface winds from the special sensor microwave imager

1994
By Krasnopolʹsky, V. M. ; Breaker, L. C. ; Gemmill, William H.
Series: OPC contribution ; no. 94

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Personal Author:

Krasnopolʹsky, V. M. ; Breaker, L. C. ; Gemmill, William H.

Krasnopolʹsky, V. M. ; Breaker, L. C. ; Gemmill, William H. Less -
Corporate Authors:

National Meteorological Center (U.S.) ; Ocean Products Center

National Meteorological Center (U.S.) ; Ocean Products Center Less -
NOAA Program & Office:

NWS (National Weather Service)
Description:

Brightness temperatures from the Special Sensor Microwave lmager(SSM/I) are being used routinely to estimate surface wind speeds over the open ocean. Recently, Stogryn, Butler and Bartolac(SBB) developed an SSM/I wind speed retrieval algorithm using neural networks (NNs) as a basis. As a starting point for the present work, we first reproduce the results of SBB for "clear" and "cloudy" conditions using the same data sets and identically-constructed NNs. We then proceed to extend the results of SBB by constructing and training a single NN which can be applied to both clear and cloudy conditions. This single network not only eliminates the problem of bridging the gap between the clear and cloudy regions, but it also provided the basis for developing a network which could be extended to atmospheric conditions where higher levels of moisture exist. As a result, NNs were trained to cover adverse atmospheric conditions considered by SBB to be beyond the region where useful retrievals could be obtained. Finally, an "all-weather" network was constructed and trained which yields a bias of -0.05 m/sec and an rms error of 1.65 m/sec over the entire range of conditions encountered in the data.
Keywords:

[+]

Computer Programs Marine Meteorology Measurement Neural Networks (Computer Science) Ocean-atmosphere Interaction Remote Sensing Research Speed Winds
Series:

OPC contribution ; no. 94 ; Technical note (National Meteorological Center (U.S.)).

OPC contribution ; no. 94 ; Technical note (National Meteorological Center (U.S.)). Less -
Format:

PDF
Document Type:

Technical Note
Place as Subject:

United States
License:

https://creativecommons.org/publicdomain/zero/1.0/
Rights Information:

CC0 Public Domain
Compliance:

Library
Main Document Checksum:

[+]

urn:sha256:ddf522cb6600afe7749347583090518cf5cd906f7e8f8f98f8f843541645cb02
Download URL:

https://repository.library.noaa.gov/view/noaa/49374/noaa_49374_DS1.pdf
File Type:

[PDF-24.22 MB]