Weather forecasts play an essential role in how different sectors plan and make decisions. The energy community benefits from good wind forecasts. A worker saves the company time by planning their commute around weather that might make them late for work. Agriculture producers could protect their citrus crops from freezing with a good temperature forecast. A recent study by NOAA’s Global Systems Laboratory (GSL) and Colorado State University quantified the impact of improved weather forecasts from GSL’s High-Resolution Rapid Refresh (HRRR) for these three sectors.
In 2014 NOAA began using the High-Resolution Rapid Refresh (HRRR) numerical weather prediction system to provide operational weather forecasts for the NOAA National Weather Service. HRRR is a real-time model with a 3-km resolution, updated hourly, and is the premier model used for high-resolution severe weather forecasting by the NWS. Since its initial release in 2014, the HRRR model has been upgraded three times.
The HRRR is one of several weather forecasting models the energy community uses to predict the winds at the turbine hub height, both over terrain and off-shore. In a recent study, researchers found that better 12-hour wind forecasts from the HRRR model between version 2 and 3 would have resulted in a cost savings to the wind energy industry of over $200M per year. This result assumes that the wind industry used only the HRRR forecasts for their day-ahead decisions regarding the mix of wind-produced vs fossil fuel produced energy. The improvement is due to both reduced overprediction and underprediction errors by the improved version 3 of the HRRR, relative to version 2. These savings would have a direct impact on household incomes via the impact of the cost of energy.
Researchers also looked at economic losses when commuters encounter unexpected rain and thus arrive late for work. This study assumed that commuters used the 12-hour HRRR precipitation forecast the evening before work to plan their departure time the following morning. Researchers estimate under-forecasted precipitation results in economic losses of $117M.
The final area researchers evaluated was the economic savings from improved freeze forecasts for specialty crops such as tree nuts, fruits, and vegetables. Results showed agriculture producers making decisions based on improved temperature forecasts between HRRRv1 and HRRRv3 would have saved $12.3M.
To complete a study like this, researchers had to make a lot of assumptions. They assumed the HRRR was the only source of weather information for the decision-maker, and that the forecast was the only piece of information influencing their decision. Often, many other pieces of information are included in the decision-making process, from personal experience to internal company procedure; as this type of information is hard to find and quantify, these economic impact studies should be considered as a reasonable upper bound on the possible impact improved forecasts provide for each of the sectors.
There are many other examples where improvements to the HRRR model may have important economic benefits which will be explored in future work.