Application of a cloud-texture analysis scheme to the cloud cluster structure recognition and rainfall estimation in a mesoscale rainstorm process
| Autor: | LI Shen-shen, Zhao Zhong-ming, Shou Shao-wen, Shou Yi-xuan |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Atmospheric Science
Meteorology business.industry Mesoscale meteorology Cloud computing Structural basin Rainfall estimation Structure recognition Yangtze river Classification methods Entropy (energy dispersal) business Astrophysics::Galaxy Astrophysics Physics::Atmospheric and Oceanic Physics Geology Remote sensing |
| Zdroj: | Advances in Atmospheric Sciences. 23:767-774 |
| ISSN: | 1861-9533 0256-1530 |
| DOI: | 10.1007/s00376-006-0767-x |
| Popis: | It is thought that satellite infrared (IR) images can aid the recognition of the structure of the cloud and aid the rainfall estimation. In this article, the authors explore the application of a classification method relevant to four texture features, viz. energy, entropy, inertial-quadrature and local calm, to the study of the structure of a cloud cluster displaying a typical meso-scale structure on infrared satellite images. The classification using the IR satellite images taken during 4–5 July 2003, a time when a meso-scale torrential rainstorm was occurring over the Yangtze River basin, illustrates that the detailed structure of the cloud cluster can be obviously seen by means of the neural network classification method relevant to textural features, and the relationship between the textural energy and rainfall indicates that the structural variation of a cloud cluster can be viewed as an exhibition of the convection intensity evolvement. These facts suggest that the scheme of following a classification method relevant to textural features applied to cloud structure studies is helpful for weather analysis and forecasting. |
| Databáze: | OpenAIRE |
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