| Título: | Retrieving Atmospheric Precipitable Water Vapor Using Artificial Neural Network Approach |
| Autores: |
Xin, Wang; Chengdu University of Information Technology Xiaobo, Deng; Chengdu University of Information Technology Shenglan, Zhang; Chengdu University of Information Technology |
| Fecha: | 2013-07-03 |
| Publicador: | TELKOMNIKA: Indonesian journal of electrical engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: | Neural network; AIRS; Precipitable water vapor; Retrieval |
| Descripción: | Discussing of water vapor and its variation is the important issue for synoptic meteorology and meteorology. In physical Atmospheric, the moisture content of the earth atmosphere is one of the most important parameters, it is hard to represent water vapor because of its space-time variation. High-spectral resolution Atmospheric Infrared Sounder (AIRS) data can be used to retrieve the small scale vertical structure of air temperature, which provided a more accurate and good initial field for the numerical forecasting and the large-scale weather analysis. This paper proposes an artificial neural network to retrieve the clear sky atmospheric radiation data from AIRS and comparing with the AIRS Level-2 standard product, and gain a good inversion results. |
| Idioma: | Inglés |