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: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ae696f049b6b4997a9058f4e32cb7fab |
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| Sumario: | <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> |
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