Optimizing Numerical Weather Prediction Utility of the Maryland Mesonet with Observing System Simulation Experiments

The Maryland Mesonet project will construct a network of 75 surface observing stations with aims that include mitigating the statewide impact of severe convective storms and improving analyses of records. The spatial configura- tion of mesonet stations is expected to affect the utility newly provided observations will have via data assimilation, making it desirable to study the effects of mesonet configuration. Furthermore, the impact associated with any observing system configuration is constrained by errors inherent to the prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and resource availability. To address such possibilities, we perform sets of observing system simulation experiments using a high-resolution regional modeling system to assess the expected impact of four candidate mesonet configurations. Experiments cover seven 18-h case study events featuring moist convective regimes associated with severe weather over the state of Maryland and are performed using two versions of our experimental modeling system: a "standard-uncertainty" configuration tuned to be representative of existing convective-allowing prediction systems and a "constrained-uncertainty" configuration with reduced boundary condition and model error that reflects a possible trajectory for future prediction systems. We fi nd that the assimilation of mesonet data produces definitive improvements to analysis fi elds below 1000 m that are mediated by modeling system uncertainty. Conversely, mesonet impact on forecast verification is inconclusive and strongly variable across verification metrics. The impact of mesonet configuration appears limited by a saturation effect that caps local analysis improvements past a minimal density of observing stations.