Stations Matter: How Gauge-Enhanced Gridded Precipitation Estimates Can Inform Humanitarian Decision Making and Help Vulnerable Countries Manage Extreme Events

In the early 2020s, extreme weather events punished many vulnerable food-insecure countries. In Ethiopia, Kenya and Somalia, a climate change-enhanced multi-year La Nina produced sequential droughts that pushed more than 18 million people to the edge of famine. More than nine million livestock perished. In March-April-May of 2022 the region faced the worst drought in 70+ years. In March of 2023 Cyclone Freddy, the longest-lasting and highest-Accumulated Cyclone Energy (ACE)-producing tropical cyclone ever recorded worldwide struck southeastern East Africa, leading to more than 1,434 fatalities, flooding, and wide-spread crop losses. Integrated measurement systems that combine satellite observations, high resolution climatologies and in situ rain gauge observations provide a vital line of defense against such hazards. For example, the University of California at Santa Barbara’s Climate Hazards InfraRed Precipitation with Stations (CHIRPS) product is used by the Famine Early Warning Systems Network (FEWS NET) to identify droughts, and help guide billions of dollars of humanitarian relief that help protect the lives of millions of people. Index insurance providers, like Pula, are using CHIRPS to insure close to three million households across Ethiopia, Zambia, Kenya, Malawi, Nigeria, Ghana, Uganda, and Mozambique.

While such applications are promising, the decline in the number of available in situ observations is declining in many countries, such as Kenya (Figure 1). Precipitation observations are a key component in integrated gridded rainfall estimates, such as CHIRPS. Unfortunately, the gauge data support is decreasing. Globally, and in almost all countries, the number of available gauge data across the world has been decreasing steadily for the past 4 decades.

To help address these declines, we have initiated the Enhancing Meteorological Networks Partnership (EMNP), funded by the Famine Early Warning System Network (FEWS NET) and USAID. The EMNP leverages the 3D-Printed Automatic Weather Station (3D-PAWS) as a cost effective and sustainable means for national meteorological and hydrological services (NMHSs) to install, maintain, and expand their weather station observation networks. Here, we describe pilot EMNP activities in Kenya, the CHIRPS data set, and how more timely and accurate station data can be used to better inform risk management and agricultural decision making in Kenya, at both county and national levels.