Improvement of Quantitative Precipitation Forecasts Using a New Microphysical Parameterization

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Accurate forecasts of precipitation associated with disruptive, high-impact weather events can save lives and money. Current forecast accuracy suffers from poor representation of microphysical processes in numerical weather prediction (NWP) models. The goal of this research is to improve quantitative precipitation forecasts (QPF) in regional scale models through better formulation of microphysical processes. The central hypothesis of this project is that significant improvement in QPF in complex terrain can be reached through a consistent representation of warm, cold, and mixed-phase clouds. Explicit treatment of all growth processes will generate more accurate mass distributions and fall trajectories, leading to more accurate spatial distribution of rain and snowfall. To test the central hypothesis, the investigators will implement an improved microphysical scheme in two advanced, research numerical models, perform rigorous sensitivity studies in idealized simulations, validate forecasts of heavy rain and snowfall events using analyzed data sets, evaluate improvement in QPF and rain-snow line forecasts using statistically meaningful methods, test model performance and investigate the interaction of the dynamics and microphysics of cloud systems in real-time winter storm simulations, and determine the ratio of increase in computational time to forecast improvement.Rain and snowfall totals, Doppler and profiler radar data, as well as in situ dynamic and microphysical aircraft measurements from two already completed field projects will be used in model validation. The new version of the microphysics parameterization will be made available to the scientific community and the local NWS office in Reno, where its performance will be tested in an operational environment.