A Method to Improve Land Use Representation for Weather Simulations Based on High‐Resolution Data Sets—Application to Corine Land Cover Data in the WRF Model

Abstract

AbstractLand cover (LC) data incorporation, for weather modeling purposes, highlights many problems. The straightforward Single Level Mode (SLM) aggregation is not adapted for high‐resolution LC maps, with a high number of classes, because it could generate false classifications. We propose a Multi‐Level Mode (MLM) aggregation method that includes a hierarchical structure. This study focuses on the Corine Land Cover (CLC) data set. Differences between MLM and SLM methods are small at the finest horizontal resolution and increase to a value of around 16% at 9‐km horizontal resolution. To further integrate CLC data into WRF (Weather Research Forecasting model), we included a dedicated table of physical parameters next to using the classical conversion toward the USGS one. To evaluate the LC impact on the modeled boundary layer, we used WRF at 1 km to simulate a 4‐day period with diurnal cycles of valley winds in the heterogeneous western pre‐Alps area. We tested three LCs (USGS, MODIS, and CLC), where CLC has two physical parameter tables and two aggregation methods. CLC performs better than other tested LCs. The CLC aggregation methods revealed limited differences between the simulated variables, although the MLM method gives better results. Since these comparisons are restricted to a single location with the same LC type, the differences between various simulations regarding atmospheric parameters probably result from horizontal advection from upwind areas where surface conditions differ. Extended time series with multiple locations and diverse meteorological conditions need to be considered to reinforce the results presented here.