@phdthesis{10902/9718,
year = {2016},
month = {9},
url = {http://hdl.handle.net/10902/9718},
abstract = {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.},
abstract = {RESUMEN: Las predicciones estacionales climáticas tienen un gran número de aplicaciones y pueden ayudar a la toma de decisiones en diversos sectores socioeconómicos. Sin embargo, la baja resolución espacial (del orden de los cientos de km) de los modelos numéricos utilizados en la actualidad para la predicción estacional resulta insuficiente para la mayoría de estudios de impacto, por lo que se requiere algún tipo de postproceso que permita llevar sus predicciones a una escala local útil. Para ello se pueden utilizar técnicas de downscaling estadístico (SD, por sus siglas en inglés). No obstante, mientras que estas técnicas han sido ampliamente utilizadas para la modelización del cambio climático, la experiencia hasta la fecha en la predicción estacional es muy limitada. Por tanto, esta Tesis se centra en adaptar las distintas metodologías y técnicas de SD para su correcta aplicación en el contexto de la predicción estacional (por ser la más problemática, la precipitación es la única variable que se considera). Asimismo, sus ventajas y limitaciones se analizan para una región de estudio particularmente interesante: Filipinas.},
organization = {This Thesis has been developed at the Santander Meteorology Group (http://www.
meteo.unican.es), formed by researchers and professors from the Department of Applied Mathematics and Computer Science of the University of Cantabria (UC) and the Institute of Physics of Cantabria (UC and Spanish National Research Council), in the context of the international research projects EUPORIAS (European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales) and SPECS (Seasonal-to-Decadal Climate Prediction for the Improvement of European Climate Services), funded by the EU Commission through grant agreements 308291 and 308378, respectively.},
title = {Statistical downscaling of precipitation in seasonal forecasting: advantages and limitations of different approaches},
author = {García Manzanas, Rodrigo},
}