18th Conference on Weather and Forecasting, 14th Conference on Numerical Weather Prediction, and Ninth Conference on Mesoscale Processes

Thursday, 2 August 2001
High resolution precipitation climatologies from radar data
Henry E. Fuelberg, Florida State University, Tallahassee, FL; and G. S. Quina, B. A. Mroczka, R. J. Lanier, J. Bradberry, and J. P. Breidenbach
Precipitation seldom occurs uniformly over an area. Some of this variability is due to mesoscale processes such as land/sea breezes, topographic circulations, shape of the coastline, or the presence of lakes or other variations in the surface. A knowledge of precipitation variability and its causes will lead to improved forecasts.

We have prepared radar-derived precipitation climatologies for 28 radar sites in the southeastern United States. Four years of hourly digital precipitation data (HDP, now called the Digital Precipitation Array or DPA) from WSR-88D sites were used to prepare the climatologies. Since the radar data are available on a horizontal grid of 4 km, the resolution of our climatologies is much greater than available from rain gauges. The radar-derived values have been summed over the warm and cool seasons, and frequencies of various hourly totals have been prepared. Examples of these products are on the Web at http://bertha.met.fsu.edu/~gquina/. The radar-derived precipitation climatologies contain various limitations due to biases, partial beam blockage, overshooting beams, etc. Nonetheless, they reveal a multitude of information about the mesoscale aspects of precipitation occurrence.

This proposed paper will describe our methodologies, present the results, and give examples of the types of mesoscale forcings that produce the precipitation variability. We believe that these high resolution precipitation climatologies are the first of their kind. The paper also will describe our plans to develop a high resolution historical database of precipitation that is based on a synthesis of radar and gauge data using software developed by the National Weather Service's Office of Hydrology. This synthesis will utilize the strengths of each data source, and diminish the effects of their weaknesses.

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