Improvement of Microphysical PaRameterization through Observational Verfication Experiment (IMPROVE): Data Analysis and Modeling

University Of Washington, Seattle WA

Investigators

Abstract

Regional mesoscale models are becoming the central tools for the operational forecasting of local weather systems and quantitative precipitation forecasting (QPF) for periods of 0-48 h. Over time model resolution has continuously increased and model parameterizations of physical processes have become more sophisticated. Even so, improvements in QPF have been comparatively slow. Apart from resolution and initial conditions, the key model component that affects QPF is the bulk microphysical parameterization (BMP) of cloud and precipitation processes. There are considerable uncertainties in many of the assumptions on which BMPs are based. The only way to clearly evaluate the performance of a BMP (and to improve it) is to compare microphysical processes and predicted hydrometeor distributions in model simulations with in situ (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 these other predicted fields. To this end, the Principal Investigator initiated a study entitled "Improvement of Microphysical PaRameterization Through Observational Verification Experiment "(IMPROVE) to compare representations of cloud and precipitation processes in current mesoscale models with detailed measurements and observations. Two field studies were conducted during the past two winters: one examined precipitation produced by frontal systems as they approached the coast of Washington; and the second examined the orographic modulation of precipitation in situations of strong, moist airflow across the Oregon Cascades barrier. A rich data set was gathered during a total of 26 Intensive Observing Periods (IOPs) that covered a wide variety of frontal and orographic precipitation systems. Under this award the Principal Investigator will continue with analysis of the IMPROVE data. First, observational data will be reduced and analyzed to ascertain the physical processes leading to the development of precipitation and to produce temporal and spatial distributions of concentrations, size distributions, reflectivity factor, etc., for the various hydrometeor species. Second (and parallel to the observational analysis), mesoscale model simulations of the observed cases will be performed with resolution of ~1 km, making use of four dimensional data assimilation to produce the best possible simulations in terms of kinematic, thermal, and moisture distributions. As with the observations, the model simulations will be analyzed to ascertain temporal and spatial distributions of the hydrometeor species and model sensitivity tests will be conducted to ascertain the key physical processes that led to those distributions. Third, the microphysical processes and hydrometeor distributions determined from observations will be compared to those that occurred in the model simulations. This will provide the basis for modifications to the BMP. Finally, these modifications will be evaluated in model simulations of the wide variety of storm systems studied during IMPROVE, as well as in daily operational forecast runs of the University of Washington's real-time regional forecast system. The nature of the research proposed here has direct potential benefits to society through improved QPF. To further broaden the benefits of the proposed research, and to increase general awareness of mesoscale numerical weather prediction (NWP) models, a lay-accessible web site on IMPROVE and NWP will be maintained. The use of NWP web sites in local K-12 science classrooms will be encouraged through interactions with teachers, and the PI and staff will interact with the operational forecasting community to help the transfer of research results to applications.