A bulk parametrization of melting snowflakes with explicit liquid water fraction for the COSMO model
- 1Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
- 2Deutscher Wetterdienst, Offenbach, Germany
- 3Hans Ertel Centre for Weather Research, Hamburg, Germany
Abstract. A new snow melting parametrization is presented for the non-hydrostatic limited-area COSMO ("consortium for small-scale modelling") model. In contrast to the standard cloud microphysics of the COSMO model, which instantaneously transfers the meltwater from the snow to the rain category, the new scheme explicitly considers the liquid water fraction of the melting snowflakes. These semi-melted hydrometeors have characteristics (e.g., shape and fall speed) that differ from those of dry snow and rain droplets. Where possible, theoretical considerations and results from vertical wind tunnel laboratory experiments of melting snowflakes are used as the basis for characterising the melting snow as a function of its liquid water fraction. These characteristics include the capacitance, the ventilation coefficient, and the terminal fall speed. For the bulk parametrization, a critical diameter is introduced. It is assumed that particles smaller than this diameter, which increases during the melting process, have completely melted, i.e., they are converted to the rain category. The values of the bulk integrals are calculated with a finite difference method and approximately represented by polynomial functions, which allows an efficient implementation of the parametrization. Two case studies of (wet) snowfall in Germany are presented to illustrate the potential of the new snow melting parametrization. It is shown that the new scheme (i) produces wet snow instead of rain in some regions with surface temperatures slightly above the freezing point, (ii) simulates realistic atmospheric melting layers with a gradual transition from dry snow to melting snow to rain, and (iii) leads to a slower snow melting process. In the future, it will be important to thoroughly validate the scheme, also with radar data and to further explore its potential for improved surface precipitation forecasts for various meteorological conditions.