Avaliação do Modelo WRF para Aplicação de um índice de Previsão de Geada na Região Sul do Brasil
Understanding the formation processes, as well as the predictability of frosts, is of paramount importance to avoid socioeconomic damage, especially in regions that are prone to the occurrence of the phenomenon, such as in the South and Southeast of Brazil. Among the various methods of forecasting f...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.unesp.br:11449/236951 |
| Acceso en línea: | http://dx.doi.org/10.1590/0102-77863730084 http://hdl.handle.net/11449/236951 |
| Access Level: | acceso abierto |
| Palabra clave: | regional model frost physical parameterizations modelo regional geada parametrizações físicas |
| Sumario: | Understanding the formation processes, as well as the predictability of frosts, is of paramount importance to avoid socioeconomic damage, especially in regions that are prone to the occurrence of the phenomenon, such as in the South and Southeast of Brazil. Among the various methods of forecasting frosts, numerical modeling stands out, which, for this study, used the regional model WRF. This model uses physical parameterizations to represent the sub-grid processes. The WRF allows to choose the parameterization set to be used, in this way, it is possible to evaluate the ones that best reproduce the atmospheric conditions. In this work, the evaluation of the parameterizations was carried out for a set of five cases, covering seven municipalities in the Southern Region of Brazil. The model was executed with grid nesting for three domains: 30, 10 and 3 km of resolution. and for three sets of parameterizations. The result showed that the smallest error was obtained when using the following set of parameterizations: Dudhia for short wave, RRTM for long wave, WDM6 for cloud microphysics and YSU for the planetary boundary layer. Subsequently, a frost index (GI) was applied to the data in order to determine which would better predict the conditions for the occurrence of frost. Again, the same set of parameterizations was better compared to the other sets, showing that the cloud and radiation microphysics have more significant weights in the forecast of the frost phenomenon. |
|---|