NMME Experimental Subseasonal Precipitation Forecasts (SubX) provide enhanced predictions of end-of-season water balance over Sub-Saharan Africa

Abstract

The Famine Early Warning Systems Network (FEWS NET) and its partners use timely and spatially focused monitoring products to guide humanitarian assistance and secure livelihoods in some of the world’s most food-insecure populations. Subseasonal predictions bridge the gap between coarse, seasonal climate predictions and finer-scale, medium-range weather forecasts, providing insight for in-season agricultural decision making, including planting dates, labor allocation, and loan and fertilizer investments. In adverse conditions, they can increase the lead-time for necessary intervention (e.g., food or monetary aid); and in favorable conditions can provide opportunities for increased investment and subsequent improved agricultural productivity. Accuracy in forecasting both each scenario can enhance loss mitigation and increase capital, providing opportunities for long-term resilience building and poverty reduction. Collectively, this work aims to improve upon and contribute to early warning systems in semiarid African rainfed agricultural zones for the purpose of improving food security and livelihoods. Here we report on an ongoing project to assess the efficacy of the NMME Experimental Subseasonal Precipitation Forecasts (SubX) for use in a regional water balance model—the Water Requirement Satisfaction Index (WRSI)—in Sub-Saharan Africa. While SubX has been shown to be effective for hydrologic monitoring in India and eastern South America, and for predicting extreme events, including droughts and floods in the US and South Korea, assessments of the accuracy and certainty of SubX is quite limited over Africa. We begin to fill this gap by assessing the viability of SubX for use in WRSI deterministic and probabilistic forecasts in rainfed agricultural areas of east, southern, and west Africa, from 1999-2016. We briefly explore the regional characteristics and inter-model variability in forecast skill.