Rainfall climatology and forecasts for predicting crop yields in ethiopia quality climate and environmental data, combined with other kinds of data tailored to meet the needs of decision‐makers, are…

This is a $232,859.73 grant awarded by the Shared Services Center, a civilian agency, to the University of California, Santa Barbara (UCSB) to conduct research on rainfall climatology and forecasts for predicting crop yields in Ethiopia. The grant will enable UCSB to leverage global meteorological datasets and forecasting models to strengthen climate data collected locally by Ethiopia's National Meteorological Agency (NMA). This will help the NMA provide short-term forecasts for planting, fertilization and harvesting decisions, as well as long-term forecasts to support seed selection and disaster preparedness. The grant does not have a set-aside designation, indicating UCSB competed for this opportunity in full and open competition. UCSB has extensive expertise in climate research and frequently collaborates with other leading research institutions on federally-funded projects.

Rainfall climatology and forecasts for predicting crop yields in ethiopia quality climate and environmental data, combined with other kinds of data tailored to meet the needs of decision‐makers, are critical to generate actionable climate services. Several global meteorological datasets are available that can be used to strengthen key climate variables that are collected locally. Forecasts and climate projections are also available at multiple time scales that can help anticipate upcoming weekly and seasonal climate variations, and their impact on agricultural and pastoral productivity. There is an opportunity to improve cohesion across these available datasets and match them more precisely with the needs of the ethiopia national meteorological agency (NMA) and the agencies and ministries that depend on nmas analysis. By providing forecast data in formats that are compatible with observations, the NMA will be able to provide short term forecasts suitable for informing planting, fertilization and harvesting, long term forecasts supporting seed selection and disaster preparedness, and mid‐season monitoring/forecast combinations suitable for yield prediction.