2025/08/06 by Jeremy A. Gibbs, Joshua G. Gebauer, Louis J. Wicker +2 · 1 voice
Decision Sciences · Earth and Planetary Sciences · Environmental Science · #Meteorological Phenomena and Simulations #Simulation Techniques and Applications #Wind and Air Flow Studies
paper · doi:10.1175/waf-d-24-0153.1
openalex publication_date 2025/08/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/11
Abstract Convection-allowing models are used to predict the evolution of severe weather phenomena in the atmosphere. These models are sensitive to errors in the numerical environment used to initialize their forecast integration. Data assimilation methods can help overcome these errors but are often limited by sparse observational coverage. Numerous novel observation platforms are currently available that promise to close these coverage gaps, but they have not yet been widely assimilated into weather models. Observing system simulation experiments are often used to determine how best to assimilate these novel observations in space and time by using synthetic observations from a high-resolution “nature run.” However, a single deterministic nature run does not provide a measure of the flow’s intrinsic predictability within the chosen modeling system. We present a framework to provide insight into this predictability by using an ensemble of nature runs. The ensemble provides a range of likely outcomes for storm evolution and an upper limit against which forecasts are verified. We applied this framework to two events from Oklahoma in 2023: a quasi-linear convective system in February and a supercell case in April. The intrinsic predictability of the nature-run ensemble was used to calibrate each case and to verify the forecast ensembles. Results showed that the intrinsic predictability suggested by the nature-run ensemble was both field and case dependent. This framework may help guide future studies by giving researchers a better understanding of what is possible for a chosen flow problem and modeling system and how best to include and arrange novel observations. Significance Statement We wanted to place the relative gains in severe weather forecast performance that arise from including observational data in context to what may be reasonably expected from our chosen modeling system. In other words, if our forecast moves an inch toward the “truth,” it matters whether that truth is a foot or a mile away from our forecast estimate—and in turn, whether the effort to move that inch is worth the cost. Results demonstrate a proof-of-concept framework to achieve this goal and show that our ability to understand what is possible is both field and case dependent. Our framework may help improve efficiency by providing researchers with a better understanding of the trade-offs involved with their observational experiment design.