Sensitivity studies on difficult cloud and fog situations for weather model

Abstract: Fog and clouds are difficult to predict for meteorologist today. Models have a tendency to show either too much or too little clouds or fog, which can be a problem for people and industries that depend on the weather conditions. Improvements are being done, but errors still occur. One of the problems is that when an improvement is attained, it might only work for one specific season. This happens, for instance, due to winter causing colder air temperature, while water, which is a poor heat conductor, remains fairly warm relative to the air. Consequently, warmer air near the surface of water rises, and thereby creates clouds when the air has cooled down to the point of saturation. Using MUSC (Modèle Unifié Simple Colonne), which is a 1D-model of the weather model HARMONIE-AROME (Hirlam Aladin Research for Mesoscale Operational Numerical Weather Prediction in Euromed)-(Application of Research to Operations at Mesoscale), different cases are evaluated for specific parameters to see improvements can be made. To check the consistency of the improvement, the cases are not bound to one single season.