Alternative Spatio-temporal models for daily rainfall prediction in Navarre

To predict accumulated daily rainfall in a particular location where no rain gauges are available is important for agriculture, meteorology, traffic networks, environment and many other areas. However, statistical modelling of rain is not trivial because a high variability is presented within and between days. In this work we analyze the performance of alternative spatio-temporal models. The sampled data consist of daily observations taken in 87 manual rainfall gauges during the 1990-2010 period in Navarre, Spain. The accuracy and precision of the interpolated data is checked with data of 28 automated rainfall non-sampled gauges of the same region but placed in different locations than the manual rainfall gauges. Interpolations will be mapped on a squared grid of 1km2 grid over the whole study region and to assess the prediction performance of the models a continuous ranked probability score (CRPS) has also been calculated.