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A Next Generation (NextGen) Approach to Improve the Seasonal Prediction System in East Africa
  • Nachiketa Acharya,
  • Tufa Dinku,
  • Kyle Joseph Chen Hall
Nachiketa Acharya
International Research Institute for Climate and Society (IRI), The Earth Institute at Columbia University, Palisades NY, U.S.A.

Corresponding Author:nachiketa@iri.columbia.edu

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Tufa Dinku
International Research Institute for Climate and Society (IRI), The Earth Institute at Columbia University, Palisades NY, U.S.A.
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Kyle Joseph Chen Hall
International Research Institute for Climate and Society, Earth Institute at Columbia University, Palisades, NY, USA
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The use of an objective seasonal forecast procedure, defined as a traceable, reproducible, and well-documented set of steps that allows the quantification of forecast quality, are preferred and recommended by the World Meteorological Organization in their recent seasonal forecast guidance. However, National Meteorological Services (NMS) in African countries have been issuing seasonal rainfall forecasts using a subjective consensus process, which is based on meteorologists’ experiences using Global Producing Center’s (GPCs) outputs and other available information. A systematic general objective approach named as NextGen (Next Generation) forecasting system is being developed for some East African countries as part of implementing or strengthening ENACTS (Enhancing National Climate Services; https://iri.columbia.edu/resources/enacts/) initiative as well implementing Columbia University’s World Project “Adapting Agriculture to Climate Today, for Tomorrow” (ACToday; https://iri.columbia.edu/actoday/) project. This new forecast system is based on a calibrated multi-model ensemble (CMME) process using state-of-the-art general circulation models (GCM) from the North American Multi-Model Ensemble project. A canonical-correlation-analysis-based regression is used to calibrate the raw outputs from the GCMs; then the individually-calibrated GCMs are combined with equal weight to make a final CMME prediction. In addition to traditional tercile probability forecasts, NextGen also provides a more flexible format that enables users to extract information for those parts of the forecast distribution of the greatest interest to them in the decision-making process. Therefore, NextGen enables NMS to generate and deliver targeted climate information products relevant to the needs of decision-makers at multiple levels. The NextGen forecast system has so far been implemented in Ethiopia, Rwanda, Zambia, Malawi and Tanzania, and planned to be implemented in more countries in the near future. In this study, we describe the co-design, co-development, and skill assessment of this NextGen system.