Planning for a Future-Proofed Observing System: Observing System Simulation Experiments For the Maryland Mesonet

The Maryland Mesonet Project (MMP) aims to construct a network of about 75 observing stations to improve regional forecasts and nowcasts in order to mitigate the impact of severe weather events across the state. Mesonets are distinguished from conventional sources of surface observations by the generation of observational data for surface wind velocity, temperature, humidity, etc. with a spatial and temporal frequency sufficient to capture mesoscale processes and the development of severe convective storms. The spatial configuration of stations within a mesonet is an important factor in the utility newly provided observations will have via data assimilation in terms of impact on forecast accuracy, making it desirable to optimize station placement in an objective manner. This optimization must consider that the utility associated with any particular observing system configuration is constrained by errors inherent to the numerical weather prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and computational resources available for large ensembles. For this reason, we perform observing system simulation experiments (OSSEs) using a regional WRF modeling system based on the NSSL Warn on Forecast System (WoFS) to determine an optimal configuration for stations placed by the MMP. These OSSEs consider both current generation weather prediction systems and anticipated improvements that are expected to come with smaller model errors and better-constrained large-scale environments. We evaluate multiple candidate network configurations, which include: a network placed to minimize the variance of distance between MMP and pre-existing stations, a network placed to minimize only the variance of distance between MMP stations, a network placed according to population density, and a network with greater density in the Appalachian plateau region of western Maryland, upstream of the prevailing motion of storm activity. We also perform experiments using only the pre-existing operationally-used observation network without new construction, as a baseline for forecast improvements attributable to MMP configurations. Each of these configurations is tested for seven 18-hour case-study events featuring severe weather. We choose these events based on severe weather reports indicating significant community impact in the time period between 2020 and 2022. For each case-study, we generate a set of 3-hour forecasts from which we produce aggregated verification statistics using the fractions skill score (FSS) neighborhood-verification method. To simulate the effect of future weather prediction systems on network utility, we perform experiments for several configurations of our experimental modeling system including: a configuration tuned to be representative of existing convective-permitting, limited area modeling systems, a configuration featuring reduced model error, a configuration using a large ensemble, and an ideal, ‘futuristic’ configuration combining large ensemble size, reduced model error, and non-parametric data assimilation methodology.