Use of High Resolution Field Data to Improve Model Microphysics and Investigate Orographic Precipitation Processes

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Quantitative precipitation forecasts from short-term numerical weather prediction (NWP) models have improved only gradually during the last few decades. Recent model verification using field data has revealed some potentially large deficiencies in bulk microphysical parameterizations (BMPs). These problems often lead to little increase in precipitation accuracy when model grid spacing is decreased to less than a few kilometers, even for stratiform orographic precipitation. The primary objectives of this project are to use high-resolution observations to verify and improve the microphysics in mesoscale models, and to document the three-dimensional structures and physical mechanisms of orographic precipitation. This project builds on previous results obtained during first phase of the research, during which the model microphysical predictions were compared against remotely sensed (radar) observations, in situ aircraft data, and ground measurements for an IPEX (Intermountain Precipitation EXperiment) case over northeast Utah and an IMPROVE (Improvement of Microphysical PaRameterization through Observational Verification Experiment) event over the Oregon Cascades. In order to make improvements to the BMPs in the new Weather and Research Forecasting (WRF) model, additional cases need to be investigated, including 1-2 events from the PACific JETs (PACJET) experiment along the California coast in February 2001. In addition, this project will complete 3-4 months of high-resolution simulations over the northern Oregon Cascades in order to compare the simulated cloud structure with WSR-88D radar data. Model microphysical budgets aloft will be calculated along hydrometeor trajectories to determine the dominant microphysical processes that result in surface precipitation errors. These additional field studies and long-term model comparisons with WSR-88D data will also provide an opportunity to study some of the detailed three-dimensional structures and physical mechanisms of orographic precipitation. These datasets will help verify the project's idealized modeling results, which suggest that an upstream-tilting gravity wave induced by relatively wide barriers, such as the Cascades or Sierras, can enhance the ice generation aloft and help seed the lower-level feeder cloud with ice and snow. Radar and in situ aircraft data will help illustrate how the windward orographic cloud and microphysics are modified by the mountain circulation aloft. These observational results will also be combined with high-resolution case study simulations. Overall, by increasing the understanding of orographic precipitation processes and improving BMPs, this project will improve quantitative precipitation forecasting, which is a major initiative of the U.S. Weather Research Program. Deficiencies in BMPs also exist in global climate models (GCMs), since similar assumptions are made for snow and cloud water distributions. Given the importance of clouds in determining global climate change, this research also benefits the climate community. This project will involve the education and training of two graduate students and the mentoring of undergraduate research projects in real-time mesoscale modeling at Stony Brook. Project results will also be shared within the mesoscale meteorology and numerical weather prediction classes at Stony Brook. Collaboration with Eastern Regional Headquarters of the National Weather Service in the area of forecaster training on mesoscale models will be continued.