Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)

Abstract

Abstract. Snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas LSMs evaluate the snow cover, snow albedo, and snow depth through interaction with atmospheric conditions. Most LSMs include parameters based on empirical relations, resulting in uncertainties in model solutions. When the initially developed empirical parameters are local or inadequate, we need to optimize the parameter sets for a certain region. In this study, we seek the optimal parameter values in the snow-related processes – snow cover, snow albedo, and snow depth – of the Noah LSM, for South Korea, using the micro-genetic algorithm and the in situ surface observations and remotely sensed satellite data. Snow data from observation stations representing five land cover types – deciduous broadleaf forest, mixed forest, woody savanna, cropland, and urban and built-up lands – are used to optimize five snow-related parameters that calculate the fractional snow cover, maximum snow albedo of fresh snow, and fresh snow density associated with the snow depth. Another parameter, reflecting the dependence of fractional snow cover on the land cover types, is also optimized. Optimization of these six snow-related parameters has led to improvement in the root mean squared errors by 17.0 %, 6.2 %, and 3.3 % in snow depth, snow albedo, and fractional snow cover, respectively. In terms of the mean bias, the underestimation problems of snow depth and overestimation problems of snow albedo have been alleviated through optimization of parameters calculating the fresh snow by about 44.2 % and 31.0 %, respectively.