The IMPROVE field study along the Washington coast and the central Oregon Cascades gathered a unique observational dataset to verify and improve the bulk microphysical parameterizations (BMPs) in numerical models. In order to fully utilize this dataset, more understanding of the sensitivities within BMPs are needed in order to quantify their importance on the microphysical pathways associated with heavy precipitation.

The first goal of this paper is to illustrate the BMP sensitivities using the Penn State/NCAR Mesoscale Model (MM5) down to 1.33 km grid spacing for a heavy precipitation event over the Cascades on 13-14 December 2001. Using a sophisticated BMP (Reisner2), the spatial distribution of important microphysical processes are shown over the Cascades as well as the microphysical pathways that led to the model overprediction of precipitation. It will be shown that the more recent schemes in the MM5 (ie., Reisner) have microphysical pathways which favor more cloud water and rimed snow and graupel than earlier BMP schemes. A large portion of the precipitation differences between BMPs in the MM5 can be explained by the slope intercept for snow number concentration. Other sensitivities that will be tested include the cloud water autoconversion, CCN concentrations, and fall speeds.

The second goal of this paper is to verify the spatial distribution of the microphysical processes and hydrometeor types using radar and aircraft observations. For example, the spatial distribution of hydrometeor types for each BMP run is compared with the SPOL radar, and the simulated cloud water is verified using aircraft and radiometer measurements.