The objective of this work is to improve precipitation estimation and numerical forecasts of quantitative precipitation with the optimal use of observations from advanced instruments. A two-dimensional video disdrometer will provide detailed information about the size, shape and density of precipitating particles at a fixed location on the ground. A vertically pointing wind profiler will measure precipitation (or clear air) characteristics vertically over a given point. Polarization radar measurements will allow classification of hydrometeor types and retrieval of hydrometeor drop size distributions over the depth of the troposphere out to a range of about 120 km. The combination of these measurements makes it possible map detailed precipitation characteristics through a volume of the atmosphere. These observations will be used to refine and verify microphysical parameterizations in numerical weather prediction models. They also will enable the project team to develop forward observation operators for polarization radar variables, improve quantitative precipitation estimation algorithms through optimal use of polarimetric radar data, and assimilate radar observations into numerical weather prediction models for optimal retrieval of microphysical parameters and for model initialization and forecasting. Error structures of radar measurements and retrievals will be quantified. Observation-based and model-based microphysical retrievals will be cross-validated and verified with in-situ measurements. The project will help satisfy increasing needs for improved microphysics parameterization in research and operational numerical weather prediction models as they start to resolve convection and precipitation explicitly. It also will position operational weather forecasters to make optimum use of new observational capabilities that will result from the planned upgrade of the US national network of operational weather radars to dual- polarization capability in the next five years.
The broader impacts of this research include more accurate quantitative precipitation estimation and better short-term quantitative precipitation forecasting. Better forecasting also will result from better initialization of numerical weather prediction models via advanced data assimilation. The improved precipitation estimates will greatly improve our ability to assess the risks of flooding and to improve the prediction capabilities of hydrological models. The improved microphysical parameterization schemes can be implemented and tested within community numerical weather prediction models. Research results will be disseminated through seminars, conference presentations and formal publications in the scientific literature to ensure a broad impact. The activity will also provide two graduate students with the opportunity to participate in a field program and gain research experiences with disdrometer and radar observations, numerical weather prediction, and advanced data assimilation.
