Assessing the suitability of statistical downscaling approaches for seasonal forecasting in Senegal

Abstract

This work tests the suitability of statistical downscaling (SD) approaches to generate local seasonal forecasts of daily maximum temperature and precipitation for a set of selected stations in Senegal for the July-August-September season during the period 1979-2000. Two-month lead raw daily maximum temperature and precipitation from the five models included in the ENSEMBLES seasonal hindcast are compared against the corresponding downscaled predictions, which are obtained by applying the analog technique based on two different types of predictors: the direct surface variables and a combination of appropriate upper-air variables. Beyond correcting the large biases of the low-resolution raw model outputs, SD is found to add noteworthy value in terms of forecast association (as measured by interannual correlation), providing thus suitable (i.e. calibrated) predictions at the local-scale needed for practical applications, which means a clear advantage for the end-users of seasonal forecasts over the area of study. Moreover, a recommendation on the adequacy of surface (large-scale) predictors for SD of maximum temperature (precipitation) is also given.