Land Data Assimilation Systems have been developed to generate the surface initial conditions such as soil moisture and temperature for better prediction of weather and climate. We have constructed Korea Land Data Assimilation System (KLDAS) based on an uncoupled land surface modeling framework that integrates high-resolution in-situ observation, satellite data, land surface information from the WRF Preprocessing System (WPS) and the MODIS land products over the East Asia. To present better surface conditions, the KLDAS is driven by atmospheric forcing data from the in-situ rainfall gauges and satellite. In this study, we 1) briefly introduce the KLDAS, 2) evaluate the meteorological states near the surface and the surface fluxes reproduced by the KLDAS against the in-situ observation, and then 3) examine the performance of the mesoscale model initialized by the KLDAS. We have generated a 5-year, 10 km, hourly atmospheric forcing dataset for use in KLDAS operating across East Asia. The KLDAS has effectively reproduced the observed patterns of soil moisture, soil temperature, and surface fluxes. Further scrutiny reveals that the numerical simulations incorporating the KLDAS outputs show better agreement in both the simulated near-surface conditions and rainfall distribution over the Korean Peninsula, compared to those without the KLDAS.
All Science Journal Classification (ASJC) codes
- Atmospheric Science