Sub-seasonal precipitation prediction for Africa: Forecast evaluation and sources of predictability

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  • 1 National Centre for Atmospheric Science (NCAS), University of Reading, Whiteknights campus, Earley Gate PO Box 243, RG6 6BB, Reading, United Kingdom
  • 2 Atmospheric Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford OX1 3PU, United Kingdom
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Abstract

This paper evaluates sub-seasonal precipitation forecasts for Africa using hindcasts from three models (ECMWF, UKMO, and NCEP) participating in the Subseasonal to Seasonal (S2S) prediction project. A variety of verification metrics are employed to assess weekly precipitation forecast quality at lead times of one to four weeks ahead (Weeks 1-4) during different seasons. Overall, forecast evaluation indicates more skilful predictions for ECMWF over other models and for East Africa over other regions. Deterministic forecasts show substantial skill reduction in Weeks 3-4 linked to lower association and larger underestimation of predicted variance compared to Weeks 1-2. Tercile-based probabilistic forecasts reveal similar characteristics for extreme categories and low quality in the near-normal category. Although discrimination is low in Weeks 3-4, probabilistic forecasts still have reasonable skill, especially in wet regions during particular rainy seasons. Forecasts are found to be over-confident for all weeks, indicating the need to apply calibration for more reliable predictions. Forecast quality within the ECMWF model is also linked to the strength of climate drivers’ teleconnections, namely El Niño-Southern Oscillation, Indian Ocean Dipole, and the Madden-Julian Oscillation. The impact of removing all driver-related precipitation regression patterns from observations and hindcasts shows reduction of forecast quality compared to including all drivers’ signals, with more robust effects in regions where the driver strongly relates to precipitation variability. Calibrating forecasts by adding observed regression patterns to hindcasts provides improved forecast associations particularly linked to the Madden-Julian Oscillation. Results from this study can be used to guide decision-makers and forecasters in disseminating valuable forecasting information for different societal activities in Africa.

Corresponding author: Felipe M. de Andrade (f.marquesdeandrade@reading.ac.uk)

Abstract

This paper evaluates sub-seasonal precipitation forecasts for Africa using hindcasts from three models (ECMWF, UKMO, and NCEP) participating in the Subseasonal to Seasonal (S2S) prediction project. A variety of verification metrics are employed to assess weekly precipitation forecast quality at lead times of one to four weeks ahead (Weeks 1-4) during different seasons. Overall, forecast evaluation indicates more skilful predictions for ECMWF over other models and for East Africa over other regions. Deterministic forecasts show substantial skill reduction in Weeks 3-4 linked to lower association and larger underestimation of predicted variance compared to Weeks 1-2. Tercile-based probabilistic forecasts reveal similar characteristics for extreme categories and low quality in the near-normal category. Although discrimination is low in Weeks 3-4, probabilistic forecasts still have reasonable skill, especially in wet regions during particular rainy seasons. Forecasts are found to be over-confident for all weeks, indicating the need to apply calibration for more reliable predictions. Forecast quality within the ECMWF model is also linked to the strength of climate drivers’ teleconnections, namely El Niño-Southern Oscillation, Indian Ocean Dipole, and the Madden-Julian Oscillation. The impact of removing all driver-related precipitation regression patterns from observations and hindcasts shows reduction of forecast quality compared to including all drivers’ signals, with more robust effects in regions where the driver strongly relates to precipitation variability. Calibrating forecasts by adding observed regression patterns to hindcasts provides improved forecast associations particularly linked to the Madden-Julian Oscillation. Results from this study can be used to guide decision-makers and forecasters in disseminating valuable forecasting information for different societal activities in Africa.

Corresponding author: Felipe M. de Andrade (f.marquesdeandrade@reading.ac.uk)
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