Evaluating the Economic Impacts of Improvements to the High-Resolution Rapid Refresh (HRRR) Numerical Weather Prediction Model

Abstract

Forecasts from numerical weather prediction (NWP) models play a critical role in many sectors of the U.S. economy. Improvements to operational NWP model forecasts are generally assumed to provide significant economic savings through better decision-making. But is this true? Since 2014, several new versions of the High-Resolution Rapid Refresh (HRRR) model were released into operation within the National Weather Service. Practically, forecasts have an economic impact only if they lead to a different action than what would be taken under an alternative information set. And in many sectors, these decisions only need to be considered during certain weather conditions. We estimate the economic impacts of improvements made to the HRRR, using 12-h wind, precipitation, and temperature forecasts in several cases where they can have “economically meaningful” behavioral consequences. We examine three different components of the U.S. economy where such information matters: 1) better integration of wind energy resources into the electric grid, 2) increased worker output due to better precipitation forecasts that allow workers to arrive to their jobs on time, and 3) better decisions by agricultural producers in preparing for freezing conditions. These applications demonstrate some of the challenges in ascertaining the economic impacts of improved weather forecasts, including highlighting key assumptions that must be made to make the problem tractable. For these sectors, we demonstrate that there was a marked economic gain for the United States between HRRR versions 1 and 2 and a smaller, but still appreciable economic gain between versions 2 and 3.