Assessing Practical Predictability of Winter Storms using NWP and Ensemble Data Assimilation

Winter storms remain a prediction challenge, from the synoptic-scale evolution of the associated mid-latitude cyclone to the formation, location, and intensity of mesoscale snowbands, to the transitions in precipitation type, to terrain-enhanced and lake-effect processes.  Analyzing the predictability of NWP model simulations gives insights to the growth of forecast errors and the underlying dynamical mechanisms for these extreme events.  The recent NASA IMPACTS field campaign, with two coordinated aircraft as well as ground observations across field operations in 2020, 2022, and 2023, provides an unprecedented dataset with which to evaluate winter storm simulations.   In order to analyze the practical predictability of east coast winter storms, operational models as well as convection-allowing ensemble WRF simulations that assimilate conventional and field campaign observations are assessed for several cases during the IMPACTS period.  These simulations are evaluated against conventional surface and radar observations as well as field campaign in-situ thermodynamic conditions and EXRAD radar onboard the ER2. A discussion of optimizing the performance of the data assimilation techniques, as well as evaluating the benefit of assimilating various types of observations is planned. Techniques such as ensemble neighborhood probability can illuminate areas that favor heavy precipitation such as mesoscale snowbands, and predictability horizon diagrams illustrate the timescales over which modeling systems converge on a solution. Finally, errors in precipitation amounts, locations, and types can be related to the representation of dynamical and physical processes in the models, including the thermodynamic environment and hydrometeor evolution in the microphysics scheme.  While the study focuses on examples from the eastern United States, techniques and insights are expected to have broader applicability to winter storms in other regions.