(Special Bonus) HAPpy Hour Seminar- Evaluating model bias in predicting freezing drizzle: Insights from the WINTRE-MIX field campaign

Join us for a special bonus HAPpy Hour seminar! 

The Speaker will join virtually.

Abstract: Aviation activities can be threatened by icing caused by the accretion of supercooled large droplets (SLD) on the aircraft. Forecasting SLD is often a challenge due to limitations of numerical weather prediction models. Observations collected by the National Research Council of Canada Convair-580 aircraft as part of the Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX) provide a detailed dataset for the evaluation of numerical forecasts of SLD aloft. Data from in-situ sensors and profiling cloud radar are used to examine a WINTRE-MIX intensive observing period on 07–08 March 2022 that coincided with a warm frontal passage through northern New York and southern Quebec. Widespread freezing drizzle (FZDZ) associated with SLD icing was observed periodically throughout the flight, with cloud-top temperatures reaching as low as -15°C.

This study evaluates Weather Research and Forecast (WRF) model’s performance in predicting FZDZ against Convair-580 observations. Sensitivity simulations with different environmental and microphysical setups were performed to isolate and diagnose biases in cloud and precipitation properties. The control (CTRL) simulation disagrees with observations both in its environmental setup and microphysical characteristics. Seeding from aloft and high cloud number concentrations are hypothesized to suppress the collision–coalescence process. Sensitivity experiments to test these hypotheses saw improved SLD production when compared to the CTRL case, however, magnitudes remain too low when compared to observations.

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