Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry

<p>Wind retrieval parameters, i.e. quality indicators and the two-dimensional variational ambiguity removal (2DVAR) analysis speeds, are explored with the aim to improve wind speed retrieval during rain for tropical regions. We apply the well-researched support vector machine (SVM) method in m...

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Autores principales:	X. Xu, A. Stoffelen
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Environmental engineering TA170-171 Earthwork. Foundations TA715-787
Acceso en línea:	https://doaj.org/article/ae696f049b6b4997a9058f4e32cb7fab
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spelling	oai:doaj.org-article:ae696f049b6b4997a9058f4e32cb7fab2021-11-30T11:16:16ZSupport vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry10.5194/amt-14-7435-20211867-13811867-8548https://doaj.org/article/ae696f049b6b4997a9058f4e32cb7fab2021-11-01T00:00:00Zhttps://amt.copernicus.org/articles/14/7435/2021/amt-14-7435-2021.pdfhttps://doaj.org/toc/1867-1381https://doaj.org/toc/1867-8548<p>Wind retrieval parameters, i.e. quality indicators and the two-dimensional variational ambiguity removal (2DVAR) analysis speeds, are explored with the aim to improve wind speed retrieval during rain for tropical regions. We apply the well-researched support vector machine (SVM) method in machine learning (ML) to solve this complex problem in a data-oriented regression. To guarantee the effectiveness of SVM, the inputs are extensively analysed to evaluate their appropriateness for this problem, before the results are produced. The comparisons between distributions and differences between data of rain-contaminated winds, corrected winds and good quality C-band winds illustrate that the rain-distorted wind distributions become more nominal with SVM, hence much reducing the rain-induced biases and error variance. Further confirmation is obtained from a case with synchronous Himawari-8 observation indicating rain (clouds) in the scene. Furthermore, the estimation of simultaneous rain rate is attempted with some success to retrieve both wind and rain. Although additional observations or higher resolution may be required to better assess the accuracy of the wind and rain retrievals, the ML results demonstrate benefits of such methodology in geophysical retrieval and nowcasting applications.</p>X. XuA. StoffelenCopernicus PublicationsarticleEnvironmental engineeringTA170-171Earthwork. FoundationsTA715-787ENAtmospheric Measurement Techniques, Vol 14, Pp 7435-7451 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Environmental engineering TA170-171 Earthwork. Foundations TA715-787
spellingShingle	Environmental engineering TA170-171 Earthwork. Foundations TA715-787 X. Xu A. Stoffelen Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry
description	<p>Wind retrieval parameters, i.e. quality indicators and the two-dimensional variational ambiguity removal (2DVAR) analysis speeds, are explored with the aim to improve wind speed retrieval during rain for tropical regions. We apply the well-researched support vector machine (SVM) method in machine learning (ML) to solve this complex problem in a data-oriented regression. To guarantee the effectiveness of SVM, the inputs are extensively analysed to evaluate their appropriateness for this problem, before the results are produced. The comparisons between distributions and differences between data of rain-contaminated winds, corrected winds and good quality C-band winds illustrate that the rain-distorted wind distributions become more nominal with SVM, hence much reducing the rain-induced biases and error variance. Further confirmation is obtained from a case with synchronous Himawari-8 observation indicating rain (clouds) in the scene. Furthermore, the estimation of simultaneous rain rate is attempted with some success to retrieve both wind and rain. Although additional observations or higher resolution may be required to better assess the accuracy of the wind and rain retrievals, the ML results demonstrate benefits of such methodology in geophysical retrieval and nowcasting applications.</p>
format	article
author	X. Xu A. Stoffelen
author_facet	X. Xu A. Stoffelen
author_sort	X. Xu
title	Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry
title_short	Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry
title_full	Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry
title_fullStr	Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry
title_full_unstemmed	Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry
title_sort	support vector machine tropical wind speed retrieval in the presence of rain for ku-band wind scatterometry
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/ae696f049b6b4997a9058f4e32cb7fab
work_keys_str_mv	AT xxu supportvectormachinetropicalwindspeedretrievalinthepresenceofrainforkubandwindscatterometry AT astoffelen supportvectormachinetropicalwindspeedretrievalinthepresenceofrainforkubandwindscatterometry
_version_	1718406704094248960

Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry

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