Using Novel Lake-based Snowfall Measurements in the Alps and Himalayas to optimise Cloud and Precipitation processes in a Regional Atmospheric Model (MetUM)

Complex mountain orography induces sharp gradients in precipitation accumulation locally. The associated complexity in understanding these events depends on local orographic, microphysical, and dynamical conditions, which makes simulating snowfall a major challenge for regional atmospheric models. This study addresses these deficiencies by using a unique repository of snowfall measurements at a range of ‘super sites’ in the European Alps and Himalayas, which are used to produce a precipitation-optimised version of the atmosphere-only UK Met Office Unified Model (MetUM) at a spatial resolution of 1.5 km. The snowfall measurements involve using the winter time-series of water pressure in frozen lakes to measure the mass of falling snow during extreme precipitation events directly over the lake area, which are comparable in size to the model’s grid cells. Development of the precipitation-optimised version of the MetUM involves undertaking a series of model sensitivity experiments focused on varying the physical representation of cloud and precipitation microphysics, with the aim of better capturing the onset and end periods, and amounts of received snowfall during these extreme events. The MetUM is configured to use a double moment cloud microphysical scheme (CASIM: Cloud AeroSol Interacting Microphysics) with prescribed hydrometeor spectral attributes necessary to quantify both the auto-conversion rates and thresholds for the cloud conversion to take place. Results from these experiments suggest that local microphysical processes, often subsumed within small spatial scales, can influence dynamics at larger scales, impacting gradients in precipitation. Cloud radiative properties, including the hydrometeor effective radii and optical depths are further validated against satellite-based observations.