Abstract
Abstract. A new approach to downscaling soil moisture forecasts from the seasonal ensemble prediction forecasting system of the ECMWF (European Centre for Medium-Range Weather Forecasts) is presented in this study. Soil moisture forecasts from this system are rarely used nowadays, although they could provide valuable information. Weaknesses of the model soil scheme in forecasting soil water content and the low spatial resolution of the seasonal forecasts are the main reason why soil water information has hardly been used so far. The basic idea to overcome some of these problems is the application of additional information provided by two satellite sensors (ASCAT and Envisat ASAR) to improve the forecast quality, mainly to reduce model bias and increase the spatial resolution. Seasonal forecasts from 2011 and 2012 have been compared to in situ measurement sites in Kenya to test this two-step approach. Results confirm that this downscaling is adding skill to the seasonal forecasts.
Highlights
Proper knowledge of soil water content and distribution is important for many applications in earth system sciences
Seasonal forecasts used for this comparison are produced by the seasonal ensemble prediction system (EPS) of ECMWF
The forecast climatology is calibrated, meaning that it has to be shifted to the ASCAT climatology
Summary
Proper knowledge of soil water content and distribution is important for many applications in earth system sciences. Simplifications in the representation of modelled land-surface processes in numerical models are unavoidable They lead to systematic errors in the soil moisture field, which is degrading forecast quality (Drusch and Viterbo, 2007). In hydrological applications, including flood forecasting and drought monitoring, one is interested in the root zone soil moisture at the catchment or finer scales, as its knowledge can improve estimates significantly (Wagner et al, 2007). This in turn is necessary for agricultural and food security issues as well as for disaster management.
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