Statistical downscaling of precipitation in seasonal forecasting: advantages and limitations of different approaches
ABSTRACT: Seasonal climate predictions have a great number of applications and can help decision-making in many important socioeconomic sectors. However, the low spatial resolution (around hundreds of km) of the numerical models which are currently used for seasonal forecasting is insufficient for m...
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| Formato: | tesis doctoral |
| Fecha de publicación: | 2016 |
| País: | España |
| Recursos: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/9718 |
| Acesso em linha: | http://hdl.handle.net/10902/9718 |
| Access Level: | acceso abierto |
| Palavra-chave: | Seasonal forecasting Statistical downscaling Precipitation Philippines Predicción estacional Regionalización estadística Precipitación Filipinas |
| Resumo: | ABSTRACT: Seasonal climate predictions have a great number of applications and can help decision-making in many important socioeconomic sectors. However, the low spatial resolution (around hundreds of km) of the numerical models which are currently used for seasonal forecasting is insufficient for most of impact studies. Therefore, some kind of post-process is required in order to translate their coarse predictions to the useful, local-scale. To this aim, statistical downscaling (SD) techniques can be used. Nonetheless, whereas these techniques have been extensively applied for climate change modeling, there is only limited experience regarding their application for seasonal forecasting. Therefore, this Thesis focuses on adapting the different approaches and techniques available for SD for their correct application in the context of seasonal forecasting (for being the most challenging, precipitation is the only target variable considered). Likewise, their advantages and limitations are analyzed for a especially interesting region of study: the Philippines. |
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