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Statistical Forecasts for the Occurrence of Precipitation Outperform Global Models over Northern Tropical Africa
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  • Peter Vogel,
  • Peter Knippertz,
  • Tilmann Gneiting,
  • Andreas H. Fink,
  • Manuel Klar,
  • Andreas Schlueter
Peter Vogel
Karlsruhe Institute of Technology
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Peter Knippertz
Karlsruhe Institute of Technology

Corresponding Author:peter.knippertz@kit.edu

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Tilmann Gneiting
Heidelberg Institute for Theoretical Studies; Karlsruhe Institute of Technology
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Andreas H. Fink
Karlsruhe Institute of Technology
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Manuel Klar
Trier University
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Andreas Schlueter
Stanford University
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Short-term global ensemble predictions of rainfall currently have no skill over northern tropical Africa when compared to simple climatology-based forecasts, even after sophisticated statistical postprocessing. Here we demonstrate that statistical forecasts for the probability of precipitation based on a simple logistic regression model have considerable potential for improvement. The new approach we present here relies on gridded rainfall estimates from the Tropical Rainfall Measuring Mission for July--September 1998--2017 and uses rainfall amounts from the pixels that show highest positive and negative correlations on the previous two days as input. Forecasts using this model are reliable and have a higher resolution and better skill than climatology-based forecasts. The good performance is related to westward propagating African easterly waves and embedded mesoscale convective systems. The statistical model is outmatched by the postprocessed dynamical forecast in the dry outer tropics only, where extratropical influences are important.
16 Feb 2021Published in Geophysical Research Letters volume 48 issue 3. 10.1029/2020GL091022