Evaluation and Improvement of Microphysical Parameterizations in Mesoscale Models

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The use of high-resolution weather prediction models has become the cornerstone of operational forecasting of regional weather and precipitation. But even with steady advances in model resolution and physics, precipitation forecasts have been slow to improve. It has become evident that there are substantial deficiencies in model bulk microphysical parameterizations (BMP) of cloud and precipitation processes, that is, the model descriptions of moist processes. Underlying these deficiencies are considerable uncertainties in many of the assumptions on which BMPs are based. One avenue to improve the performance of a BMP is to compare microphysical processes and predicted cloud/precipitation distributions from model simulations with in situ (mainly airborne) and remotely sensed (e.g., radar) observations. In addition, it is critically important that the microphysical measurements be obtained concurrently with observations of wind, temperature and humidity, so that errors in the simulated microphysics can be isolated from errors in other predicted fields. In response to these problems, UW researchers initiated a study entitled Improvement of Microphysical PaRameterization Through Observational Verification Experiment (IMPROVE) to acquire the observations required to compare cloud and precipitation processes in current forecast and research models with detailed measurements and observations from a variety of weather systems. Two field studies were conducted: the Washington Offshore Frontal Study (IMPROVE-1), which examined frontal systems as they approached the Washington coast from 4 January to 14 February 2001; and, the Oregon Cascades Orographic Study (IMPROVE-2), which examined the orographic modulation of clouds and precipitation across the Oregon Cascades between 26 November and 22 December 2001. Making use of a comprehensive array of observing platforms, both field studies were highly successful in obtaining data for evaluating the performance of BMPs in mesoscale models, with twenty-six Intensive Observing Periods (IOPs) encompassing a wide variety of frontal and orographic precipitation systems. Analysis of this data has documented significant problems with the most sophisticated microphysics scheme in the MM5 model, including excessive snow amounts over the windward slopes and mountain crest, excessive snow blow-over to the lee slopes, too much cloud liquid water over the lower windward slopes and too little over the crest, problematic snow size distributions, and unrealistic graupel amounts at mid-levels. This research will determine if these and other microphysical problems are revealed in other cases. Specifically, observational data from additional IMPROVE IOPs will be analyzed to ascertain the physical processes leading to the development of clouds/precipitation in the observed cases, followed by a comparison with mesoscale model simulations down to resolutions of approximately 1 km. These comparisons will provide the basis for modifications to BMPs in order to better represent the development of clouds and precipitation. In addition, the dual-Doppler radar data from NOAA P3 aircraft flights during IMPROVE will be used to evaluate the fidelity of mountain waves and other key mesoscale structures in the model. Finally, based on the above evaluations, modifications in the model moist physics will be evaluated for a wide variety of storm systems studied during IMPROVE, as well as in daily operational forecast runs of the University of Washington's real-time MM5/WRF regional forecast system. Regarding intellectual merit, the Principal Investigators will analyze probably the most comprehensive data set in existence dealing with the flow and moist physics over a topographic barrier. This study will provide the best evaluation to date of the fidelity of mesoscale model structures and moist physics over terrain and should lead to improved moist process parameterizations in weather prediction models. The research has the potential for broad benefits to society. The identification and correction of deficiencies in the moist physics of weather forecast models should result in improved understanding and prediction of clouds and precipitation, with the attendant societal and economic benefits. The physical understanding and model improvements resulting from this project will be widely disseminated for use by operational forecasting centers and other groups. This effort will also have substantial educational benefits, creating a group of graduate students knowledgeable in this critical area, and will expose a number of undergraduates to this important research topic.