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Enhancing dynamical seasonal predictions through objective regionalization

Enhancing dynamical seasonal predictions through objective regionalization AbstractImproving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically-based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large scale atmospheric dynamics that drive inter-annual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically-based forecast system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability we show that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the JAS rain (July-August-September) months, and results show an improvement in the Max Plank Institute for Meteorology’s Atmosphere-ocean General Circulation Model (AGCM) version 4.5 (ECHAM4.5) predictions for applicable areas of East Africa for the two test cases presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Meteorology and Climatology American Meteorological Society

Enhancing dynamical seasonal predictions through objective regionalization

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References (39)

Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1558-8432
eISSN
1558-8432
DOI
10.1175/JAMC-D-16-0192.1
Publisher site
See Article on Publisher Site

Abstract

AbstractImproving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically-based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large scale atmospheric dynamics that drive inter-annual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically-based forecast system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability we show that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the JAS rain (July-August-September) months, and results show an improvement in the Max Plank Institute for Meteorology’s Atmosphere-ocean General Circulation Model (AGCM) version 4.5 (ECHAM4.5) predictions for applicable areas of East Africa for the two test cases presented.

Journal

Journal of Applied Meteorology and ClimatologyAmerican Meteorological Society

Published: Mar 2, 2017

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