Abstract
The frequency of “wintry mix” precipitation—freezing rain and ice pellets—is considerable in the mid-Atlantic region of the United States. Despite the fact that the general conditions necessary to support the various winter precipitation types have been known for years, in that region, the proper forecast of type and duration of winter precipitation is one of the most difficult challenges in operational meteorology, with extensive public safety and economic ramifications. The purpose of this project is to report on an improved methodology for winter precipitation forecasts.
This study analyzed precipitation type and surface temperature data from NOAA’s hourly surface airway observations and temperatures and heights for all mandatory and significant levels from NOAA’s Radiosonde Data of North America from Washington Dulles International Airport, Virginia (1962–95), and Greensboro, North Carolina (1948–95). Precipitation that occurred within 2 h of a sounding for the months November through March was used for analysis. The upper-air data were combined to create vertical temperature profiles for each observation of precipitation type—rain, freezing rain mix, ice pellets, and snow. Those profiles were then categorized by the number of freezing levels (i.e., the number of times the sounding crossed the 0°C isotherm) and examined to determine if they could be used to isolate specific precipitation types and as a result segregate winter precipitation scenarios by forecasting difficulty.
Four basic temperature profiles were found for both Greensboro and Washington Dulles Airport during winter—zero, one, two, and three or more freezing level(s). Each of these profiles produced characteristic types of precipitation (snow, freezing rain, freezing rain mix, and rain); for each profile, either climatology or discriminant analysis was used to statistically determine precipitation type.
The results of these analyses were used to develop the site-specific Discriminant Analysis Mixed Precipitation (DAMP) models for Greensboro and Washington Dulles Airport. The variables required to run the model are the height(s) of the freezing level(s) and critical temperature(s) from a modeled or observed sounding. The DAMP models are easy for the forecaster to use and understand and provide probability guidance in situations that are difficult to resolve.
The overall classification results showed that the models were very effective for predicting precipitation type. Probabilities of detection were 98.4%, 85.8%, and 92.6% for rain, freezing rain mix, and snow, respectively, for Washington Dulles Airport, and 98.7%, 87.1%, and 89.7% for Greensboro.
Corresponding author address: James D. Hux, 291 McCormick Road, P.O. Box 400123, Charlottesville, VA 22904-4123.
Email: jdh2r@virginia.edu
