Impact of soil moisture data assimilation on analysis and medium-range forecasts in an operational global data assimilation and prediction system

Abstract

Accurate initial soil moisture conditions are essential for numerical weather prediction models, because they play a major role in land–atmosphere interactions. This study constructed a soil moisture data assimilation system and evaluated its impacts on the Global Data Assimilation and Prediction System based on the Korea Integrated Model (GDAPS-KIM) to improve its weather forecast skill. Soil moisture data retrieved from the Advanced Scatterometer (ASCAT) onboard the Meteorological Operational Satellite was assimilated into GDAPS-KIM using the ensemble Kalman filter method, and its impacts were evaluated for the 2019 boreal summer period. Our results indi-cated that the soil moisture data assimilation improved the agreement of the observations with the initial conditions of GDAPS-KIM. This led to a statistically significant improvement in the accuracy of the initial fields. A comparison of a five-day forecast against an ERA5 reanalysis and in situ observations revealed a reduction in the dry and warm biases of GDAPS-KIM over the surface and in the lower-and mid-level atmospheres. The temperature bias correction through the initialization of the soil moisture estimates from the data assimilation system was shown in the five-day weather forecast (root mean squared errors reduction of the temperature at 850 hPa by approximately 5% in East Asia).