Sources of predictability of synoptic-scale rainfall during the West African summer monsoon

Numerical-model-based forecasts of precipitation exhibit poor skill over northern tropical Africa when compared with climatology-based forecasts and with other tropical regions. However, as recently demonstrated, purely data-driven forecasts based on spatio-temporal dependences inferred from gridded satellite rainfall estimates show promise for the prediction of the 24-hr precipitation occurrence rate in this region. The present work explores this potential further by advancing the statistical model and providing meteorological interpretations of the performance results. Advances include (a) the use of a recently developed correlation metric, the Coefficient of Predictive Ability (CPA), to identify predictors, (b) forecast evaluation with robust reliability diagrams and score decompositions, (c) a study domain over tropical Africa nested in a considerably enlarged spatio-temporal domain to identify coherent propagating features, and (d) the introduction of a novel coherent-linear-propagation factor to quantify the coherence of propagating signals. The statistical forecast is compared with a climatology-based benchmark, the European Centre for Medium-Range Weather Forecasts (ECMWF) operational ensemble forecast, and a statistically postprocessed ensemble forecast. All methods show poor skill within the main rainbelt over northern tropical Africa, where differences in Brier scores between the different approaches are hardly statistically significant. However, the data-driven forecast outperforms the other methods along the fringes of the rainbelt, where meridional rainfall gradients are large. The coherent-linear-propagation factor, in concert with metrics of convective available potential energy and convective instability, reveals that high stochasticity in the rainbelt limits predictability. At the fringes of the rainbelt, the data-driven approach leverages coherent precipitation features associated with propagating tropical weather systems such as African Easterly Waves. The figure shows the coefficient of predictive ability (CPA) of satellite-based rainfall estimates over temporal lags of 3 days to 1 day (bottom to top) with respect to the grid point near Niamey in the summer monsoon season 2001-2019. The westward propagation of the maximum CPA (white cross) over three days indicates the triggering and modulation of rainfall due to African Easterly Waves and mesoscale convective systems. The maximum CPA identified as shown is used to obtain predictors to train statistical models to issue forecasts of 24-hr rainfall occurrence over West Africa at a threshold of 0.2 mm.image

Automated summary

Numerical-model-based precipitation forecasts have low accuracy in northern tropical Africa compared to climatology-based forecasts and other tropical regions. However, purely data-driven forecasts based on satellite rainfall estimates show promise for predicting precipitation occurrence in this region.