Exploring Ensemble Forecast Sensitivity to Observations for a Convective-Scale Data Assimilation System over the Dallas-Fort Worth Testbed

Forecast sensitivity to observation (FSO) methods have become increasingly popular over the past two decades, providing the ability to quantify the impacts of various observing systems on forecasts without having to conduct costly data denial experiments. While adjoint- and ensemble-based FSO are employed in many global operational systems, their use for regional convection-allowing data assimilation (DA) and forecast systems have not been fully examined. In this study, ensemble FSO (EFSO) is explored for high-frequency convective-scale DA for a severe weather case study over the Dallas-Fort Worth testbed. This testbed, originally established by the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, aims to improve high-resolution DA systems by assimilating a variety of existing state and regional mesoscale observing systems to fill gaps of conventional observing networks. This study utilizes EFSO to estimate relative impacts of nonconventional surface observations against conventional observations, and further incorporates assimilated radar observations into EFSO. Results show that, when applying advected localization and a neighborhood upscale averaging technique, EFSO estimates remain correlated and skillful with the actual error reduction of all assimilated observations for the duration of 2-h forecasts. The ability for EFSO to verify against other metrics (surface T, u, y, q) beside energy norms is also demonstrated, emphasizing that EFSO can be used to evaluate impacts of specific parts of the forecast system rather than integrated quantities. Partitioned EFSO revealed that while conventional and radar observations contributed to most of the total energy, nonconventional observations contributed a significant percentage (up to 25%) of the total impact to surface thermodynamic fields.