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 indicated 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).
Highlights
As the lower boundary condition of numerical weather prediction (NWP) models, soil moisture regulates the exchange process of water and energy between land and air on the local, regional, and global scales
We attempt to make a decision on the Atmosphere 2021, 12, 1089 application of the soil moisture data assimilation in GDAPS_KIM and, figure out how the corrected initial soil moisture conditions improve the skills of the forecasts in the NWP
At National Oceanic and Atmospheric Administration (NOAA), which is produced by using blended multi-satellite soil moisture data
Summary
As the lower boundary condition of numerical weather prediction (NWP) models, soil moisture regulates the exchange process of water and energy between land and air on the local, regional, and global scales. Of the Met office in the United Kingdom [14] and the Integrated Forecast System (IFS) of the European Center for Medium-Range Weather Forecasts (ECMWF) [16,17] This demonstrates that ASCAT retrieval data were stably provided in near-real time and represented proper soil moisture quality data with a horizontal resolution appropriate for global NWP models [14]. In this study, we constructed a soil moisture data assimilation system to improve the weather forecast skill of GDAPS-KIM by correcting the initial surface conditions using satellite observations. We attempt to make a decision on the Atmosphere 2021, 12, 1089 application of the soil moisture data assimilation in GDAPS_KIM and, figure out how the corrected initial soil moisture conditions improve the skills of the forecasts in the NWP through these results.
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