Improvements in Freezing Rain Prediction by
Incorporating Droplet Temperature Characteristics (202)
Brian Barjenbruch, National
Weather Service Valley, NE, Elkhorn,
NE
Kristopher Sanders, National
Weather Service Topeka, KS
Abstract:
The Freezing Rain
Accumulation Model (FRAM) is a successful statistical method for predicting
ice-to-liquid ratios (ILR) and resultant ice accumulation due to freezing rain.
FRAM utilizes predictions of precipitation rate, surface wind speed, and
surface wet-bulb temperature as predictors of ILR. Ice accretion efficiency is
also dictated by the droplet temperature, but unfortunately these values are
not well-sampled in time and space by current observation platforms. Previous
studies have documented that the elevated melting layer can be very deep and
very warm (as warm as +10°C). In this warm layer aloft the raindrops
acquire a substantial amount of heat, requiring more heat to be removed from
the drop in order to freeze upon contact with the sub-freezing surface. If this
heat is not removed efficiently, it could lead to a disproportionately great
amount of runoff and relatively low ILR. In an effort to enhance the FRAM
predictive ability, RAP proximity-sounding characteristics of both the warm
layer and near-surface cold layer were evaluated during hundreds of freezing
rain events, including maximum and minimum temperature and wet-bulb
temperature, warm and cold layer depth, and warm and cold layer heat energy.
These upper air thermal characteristics were directly related to ILR, leading
to improved predictability and understanding of icing efficiency.