Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa

Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa AbstractAccumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, the performance of nine operational global ensemble prediction systems (EPSs) is analyzed relative to climatology-based forecasts for 1–5-day accumulated precipitation based on the monsoon seasons during 2007–14 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, state-of-the-art statistical postprocessing methods were applied in the form of Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), and results were verified against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated and unreliable, and often underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. The differences between raw ensemble and climatological forecasts are large and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles but, somewhat disappointingly, typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007–14, but overall they have little added value compared to climatology. The suspicion is that parameterization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa

Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa

VOLUME 33 WEATHER A ND F O RECASTING APRIL 2018 Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa PETER VOGEL Institute of Meteorology and Climate Research, and Institute for Stochastics, Karlsruhe Institute of Technology, Karlsruhe, Germany PETER KNIPPERTZ,ANDREAS H. FINK, AND ANDREAS SCHLUETER Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany TILMANN GNEITING Heidelberg Institute for Theoretical Studies, Heidelberg, and Institute for Stochastics, Karlsruhe Institute of Technology, Karlsruhe, Germany (Manuscript received 25 August 2017, in final form 18 December 2017) ABSTRACT Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, the performance of nine operational global ensemble prediction systems (EPSs) is analyzed relative to climatology-based forecasts for 1–5-day accumulated pre- cipitation based on the monsoon seasons during 2007–14 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, state-of-the-art statistical post- processing methods were applied in the form of Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), and results were verified against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated and unreliable, and often underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. The differences between raw ensemble and climatological forecasts are large and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles but, somewhat disappointingly, typically do not outperform climatology. Most EPSs exhibit slight...
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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0434
eISSN
1520-0434
D.O.I.
10.1175/WAF-D-17-0127.1
Publisher site
See Article on Publisher Site

Abstract

AbstractAccumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, the performance of nine operational global ensemble prediction systems (EPSs) is analyzed relative to climatology-based forecasts for 1–5-day accumulated precipitation based on the monsoon seasons during 2007–14 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, state-of-the-art statistical postprocessing methods were applied in the form of Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), and results were verified against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated and unreliable, and often underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. The differences between raw ensemble and climatological forecasts are large and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles but, somewhat disappointingly, typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007–14, but overall they have little added value compared to climatology. The suspicion is that parameterization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems.

Journal

Weather and ForecastingAmerican Meteorological Society

Published: Apr 25, 2018

References

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