Using an OSSE to Estimate How Hundreds to Thousands of UAS Can Improve NWP Forecasts of Severe Storm Environments

Uncrewed aircraft systems (UAS) have been shown to be useful in severe storm research field campaigns. Given the success of UAS observations in these more targeted deployments, a possible next step is to explore the utility of routine UAS observations in severe storm forecasting. In an attempt to estimate the impact of routine UAS observations on severe storm NWP forecasts, we designed an observing system simulation experiment (OSSE). The benefit of the OSSE approach is that a model run is used as the truth rather than the real atmosphere, which allows us to test different configurations of routine UAS observations without having to deploy hundreds to thousands of UAS. Our OSSE uses a 1-km WRF run over CONUS that loosely follows a week in the spring of 2022 as the nature run, which is our surrogate for reality. Synthetic observations are then sampled from the nature run and assimilated into a variant of the prototype Rapid Refresh Forecast System (RRFS). Using this setup, a variety of UAS network configurations are tested. The primary experiments consist of UAS flying vertical profiles every hour up to 2 km AGL with average spacings between UAS sites of 150, 100, 75, and 35 km (which ranges from 347 to 6335 UAS). We find that for severe storm environments (simply defined here as nature run grid points with MUCAPE > 50 J kg-1), assimilating UAS observations improves the bulk verification statistics for various severe weather environmental parameters, such as MLCAPE, MLCIN, and 0–1 km SRH, with skill increasing as more UAS observations are assimilated. For the experiment with the most UAS observations, reductions in root-mean-squared errors (RMSE) for severe weather environmental parameters can exceed 25% for the analysis time for certain parameters. We also find that bulk verification statistics do not change linearly as more UAS are included. For example, the curve of the number of UAS versus RMSE tends to “flatten out” as more UAS are added, indicating that individual UAS have a smaller impact on the forecast as more UAS are assimilated. Altogether, these results suggest that a routine network of vertically profiling UAS can substantially improve forecasts of severe weather by improving the representation of severe storm environments.