Affiliation:
1. Institute of Meteorology and Climate Research Karlsruhe Institute of Technology (KIT) Karlsruhe Germany
2. Institute for Stochastics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany
3. Computational Statistics Group Heidelberg Institute for Theoretical Studies Heidelberg Germany
Abstract
AbstractNumerical‐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.
Funder
Deutsche Forschungsgemeinschaft
Cited by
2 articles.
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