One possible alternative to dynamical model forecasts and post-processing are statistical forecasts based on spatio-temporal correlation patterns in past observations. Here, we investigate this approach exemplarily for the prediction of the probability of precipitation (PoP) in northern tropical Africa. Such an approach is deemed promising, as rainfall in this region is predominantly related to long-lived MCSs, which in turn are modulated by synoptic-scale African easterly waves (AEW). The results presented here are based on Tropical Rainfall Measuring Mission (TRMM) satellite-based rainfall estimates during the period 1998–2014. The statistical model is developed for every grid-point using a logistic regression approach applied to the rainfall of the previous two days.
Results do indeed show correlation patterns consistent with MCS and AEW propagation but also indications of latitudinal shifts in the monsoon system. The statistical PoP forecasts are reliable and have a higher resolution than climatology-based forecasts. Improvements in the predictive performance in terms of the Brier score reach up to 20%. In view of the promising initial results, we will expand the concept to precipitation amounts, which we expect to be modulated by preceding rainfall characteristics as well, and possibly to other regions. Furthermore, we will explore whether statistical forecasts benefit from the inclusion of NWP predictions of larger-scale features such as regional moisture patterns, the African easterly jet or tropical wave modes.