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Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall in the Tropics

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  • 1 Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • | 2 Institute for Stochastics, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • | 3 Department of Computer Science, Stanford University, Stanford, California
  • | 4 Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
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Abstract

Precipitation forecasts are of large societal value in the tropics. Here, we compare 1–5-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF, 2009–17) and the Meteorological Service of Canada (MSC, 2009–16) over 30°S–30°N with an extended probabilistic climatology based on the Tropical Rainfall Measuring Mission 3 B42 gridded dataset. Both models predict rainfall occurrence better than the reference only over about half of all land points, with a better performance by MSC. After applying the postprocessing technique ensemble model output statistics, this fraction increases to 87% (ECMWF) and 82% (MSC). For rainfall amount there is skill in many tropical areas (about 60% of land points), which can be increased by postprocessing to 97% (ECMWF) and 88% (MSC). Forecasts for extremes (>20 mm) are only marginally worse than those of occurrence but do not improve as much through postprocessing, particularly over dry areas. Forecast performance is generally best over arid Australia and worst over oceanic deserts, the Andes and Himalayas, as well as over tropical Africa, where models misrepresent the high degree of convective organization, such that even postprocessed forecasts are hardly better than climatology. Skill of 5-day accumulated forecasts often exceeds that of shorter ranges, as timing errors matter less. An increase in resolution and major model update in 2010 has significantly improved ECMWF predictions. Especially over tropical Africa new techniques such as convection-permitting models or combined statistical-dynamical forecasts may be needed to generate skill beyond the climatological reference.

Significance Statement

Accurate forecasts of rainfall could support tropical countries to more effectively manage key resources such as water, food, health, and energy. Here we assessed the usefulness of 1–5-day predictions from two leading weather centers against satellite-based rainfall estimates. The forecast models failed to predict the probability of rainfall occurrence better than a climatological reference in many parts of the tropics but showed some value in predicting rainfall amounts and even extremes. Statistical correction methods can significantly improve the raw model output except for high mountain ranges, some coastal areas, and most of tropical Africa. Future studies should refine statistical correction methods, run forecast models at higher spatial resolution, improve model physics, and experiment with statistical forecast techniques.

Denotes content that is immediately available upon publication as open access.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Waves to Weather (W2W) Special Collection.

Corresponding author: Peter Knippertz, peter.knippertz@kit.edu

Abstract

Precipitation forecasts are of large societal value in the tropics. Here, we compare 1–5-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF, 2009–17) and the Meteorological Service of Canada (MSC, 2009–16) over 30°S–30°N with an extended probabilistic climatology based on the Tropical Rainfall Measuring Mission 3 B42 gridded dataset. Both models predict rainfall occurrence better than the reference only over about half of all land points, with a better performance by MSC. After applying the postprocessing technique ensemble model output statistics, this fraction increases to 87% (ECMWF) and 82% (MSC). For rainfall amount there is skill in many tropical areas (about 60% of land points), which can be increased by postprocessing to 97% (ECMWF) and 88% (MSC). Forecasts for extremes (>20 mm) are only marginally worse than those of occurrence but do not improve as much through postprocessing, particularly over dry areas. Forecast performance is generally best over arid Australia and worst over oceanic deserts, the Andes and Himalayas, as well as over tropical Africa, where models misrepresent the high degree of convective organization, such that even postprocessed forecasts are hardly better than climatology. Skill of 5-day accumulated forecasts often exceeds that of shorter ranges, as timing errors matter less. An increase in resolution and major model update in 2010 has significantly improved ECMWF predictions. Especially over tropical Africa new techniques such as convection-permitting models or combined statistical-dynamical forecasts may be needed to generate skill beyond the climatological reference.

Significance Statement

Accurate forecasts of rainfall could support tropical countries to more effectively manage key resources such as water, food, health, and energy. Here we assessed the usefulness of 1–5-day predictions from two leading weather centers against satellite-based rainfall estimates. The forecast models failed to predict the probability of rainfall occurrence better than a climatological reference in many parts of the tropics but showed some value in predicting rainfall amounts and even extremes. Statistical correction methods can significantly improve the raw model output except for high mountain ranges, some coastal areas, and most of tropical Africa. Future studies should refine statistical correction methods, run forecast models at higher spatial resolution, improve model physics, and experiment with statistical forecast techniques.

Denotes content that is immediately available upon publication as open access.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Waves to Weather (W2W) Special Collection.

Corresponding author: Peter Knippertz, peter.knippertz@kit.edu
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