Sensitivity of Microphysical Parameters in the Thompson Scheme Using Idealized WRF Simulations

In numerical weather prediction models, microphysics schemes represent water vapor, cloud, and precipitation processes. These schemes rely on fixed parameters that are inherently uncertain or known to vary in space and time, such as the densities of snow and graupel. Inaccurate specification of these parameters leads to errors in the partitioning of surface precipitation into liquid and ice phases. To assess the sensitivity of model results to these parameters, in this study the Weather Research and Forecast (WRF) model version 4.5 was used to perform a set of idealized two-dimensional simulations of wintertime stable orographic precipitation. The design of the experiment was inspired by observations made on 25 and 26 October 2024 on the southern slope of the Pyrenees. The model configuration includes a mountain centered in the domain with a height of 1500 m and a half-width of 10 km, a horizontal grid spacing of 1 km, and 200 vertical levels. Microphysical processes are parameterized with the Thompson scheme, which is characterized by a special snow treatment that includes snow-size distribution dependence on ice water content and temperature, and a nonspherical shape of snow particles.

Model sensitivity was assessed by running-ensemble simulations, which were created by varying 6 empirical parameters of the microphysical scheme: the exponent a in the snow mass–size relation (aₘₛ), graupel density (ρ_g), the shape parameter of the gamma particle size distribution for rain (μ_r), snow (μ_s), and graupel (μ_g), and the coefficient controlling the conversion of rimed snow to graupel (r_sg). Two sets of experiments were conducted. First, 6 single-parameter perturbation experiments were run, each one with 64 members. Second, a multi-parameter perturbation experiment with 1024 members in which all parameters were perturbed simultaneously. Preliminary results indicate that cloud and snow species exhibit the strongest response to single-parameter perturbations, with particularly high sensitivity to aₘₛ and μ_s. Specifically, increasing aₘₛ leads to snow at higher altitudes (5000–6000 m), while increasing μ_s lowers the melting layer to approximately 3000 m.

This research has been funded by projects ARTEMIS (PID2021-124253OB-I00), LIFE22-IPC-ES-LIFE PYRENEES4CLIMA and the Institute for Water Research (IdRA) of the University of Barcelona.