Using a reanalysis driven land surface model for initialization of a numerical weather prediction system

Realistic initialization of the land surface is important to produce accurate NWP forecasts. Therefore, making use of available observations is essential when estimating the surface state. In this work, sequential land surface data assimilation of soil variables is replaced with an offline cycling method. In order to obtain a best possible initial state for the lower boundary of the NWP system, the land surface model is re-run between forecasts with an analyzed atmospheric forcing. We found a relative reduction of 2 meter temperature root mean square errors and mean errors of 6% and 12% respectively, and 4.5% and 11% for 2 meter specific humidity. During a convective event, the system was able to produce useful (fractions skill score greater than the uniform forecast) forecasts (above 30 mm per 12 hour) down to a 100 km length scale where the reference failed to do so below 200 km. The different precipitation forcing caused differences in soil moisture fields that persisted for several weeks and consequently impacted the surface fluxes of heat and moisture and further the forecasts of screen level parameters. The experiments also indicate diurnal and weather dependent variation of the forecast errors that give valuable insight on the role of initial land surface conditions and the land-atmosphere interactions in southern Scandinavia.