Prediction of In-Cloud Icing Conditions at Ground Level Using the WRF Model

Abstract

n-cloud icing on aircraft and ground structures can be observed every winter in many countries. In extreme cases ice can cause accidents and damage to infrastructure such as power transmission lines, telecommunication towers, wind turbines, ski lifts, and so on. This study investigates the potential for predicting episodes of in-cloud icing at ground level using a state-of-the-art numerical weather prediction model. The Weather Research and Forecasting (WRF) model is applied, with attention paid to the model?s skill to explicitly predict the amount of supercooled cloud liquid water content (SLWC) at the ground level at different horizontal resolutions and with different cloud microphysics schemes. The paper also discusses how well the median volume droplet diameter (MVD) can be diagnosed from the model output. A unique dataset of direct measurements of SLWC and MVD at ground level on a hilltop in northern Finland is used for validation. A mean absolute error of predicted SLWC as low as 0.08 g m?3 is obtained when the highest model resolution is applied (grid spacing equal to 0.333 km), together with the Thompson microphysics scheme. The quality of the SLWC predictions decreases dramatically with decreasing model resolution, and a systematic difference in predictive skill is found between the cloud microphysics schemes applied. A comparison between measured and predicted MVD shows that when prescribing the droplet concentration equal to 250 cm?3 the model predicts MVDs ranging from 12 to 20 ?m, which corresponds well to the measured range. However, the variation from case to case is not captured by the current cloud microphysics schemes.