Research results obtained from the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE) field project (carried out in the Pacific Northwest in 2001) have demonstrated the importance of the details of snow particles and associated microphysical processes in the development of precipitation associated with winter-time cyclonic storms and orographic environments. However, snow particles are generally represented in simplistic ways in the bulk microphysical schemes that mesoscale models use to develop cloud and precipitation hydrometeors and produce quantitative precipitation forecasts at the ground.
To address this deficiency, the Principal Investigators (PIs) are developing a bulk scheme that has a new set of prognostic equations that predict the habit composition of snow and use more realistic habit-dependent parameters that strongly influence the growth of snow and its interaction with other simulated hydrometeor species. It is anticipated that this more realistic treatment of snow will lead to better quantitative precipitation forecasts, particularly over mountainous regions where the amount and distribution of precipitation are highly sensitive to changes in the behavior of snow.
An added benefit of the habit prediction model is the output of the habit composition of snowfall at the ground. This information is of potential benefit for two important forecasting parameters. One is the "snow ratio", or ratio of snow depth to liquid equivalent precipitation depth, which is dependent on particle habit and is necessary for translating model quantitative precipitation forecasts into a snow depth forecast. The other is the vertical profile of particle habit in accumulating snow layers, which could provide useful additional information to avalanche forecasting models.
In order to help guide the development of the snow habit prediction model, the PIs will make use of data from several IMPROVE cases. In addition, the PIs will gather a longer-term observational data set of the properties of falling and accumulating snow, over the course of two winter seasons in the Cascade Mountains. These observations will be gathered with a snow lab that will be capable of measuring the habits, size distributions, and fall speeds of falling snow particles near the ground; and the density, liquid equivalent precipitation rate, and shear strength of snow accumulation on the ground. These observations will extend for a long enough period to gather meaningful statistics on a wide variety of individual and mixed habit types of snowfall. This unique data set is essential to the development and long-term verification of the snow habit prediction model, and will also address a number of other important questions about properties of snow accumulation at the ground, and about methods and instruments for measuring snow fall speeds, sizes, liquid equivalent precipitation rate, and particle habit.
Intellectual Merit: Both the modeling and observational efforts will improve understanding of how snow particles of different habits develop in different meteorological conditions; how the habit characteristics influence the interaction of snow with other hydrometeor classes; and how the habits of snow particles ultimately affect the distribution and properties of precipitation at the ground. The observational data set will also provide valuable information on measurement techniques for the properties of falling and accumulating snow.
Broader Impacts: The enhancement of the representation of snow microphysical processes in a forecast model will ultimately lead to improved forecasts of precipitation. Working toward better understanding and prediction of the quantity and distribution of precipitation has direct potential benefits to society. Additionally, the PIs will broadly disseminate results of the research to the forecasting community and the public via web pages, attendance at appropriate conferences and workshops, and direct interaction with operational modeling groups and forecasters.
