54
A land data assimilation system using MODIS-derived land data and its application to WRF prediction

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Tuesday, 25 January 2011
A land data assimilation system using MODIS-derived land data and its application to WRF prediction
Washington State Convention Center
Yoon-Jin Lim, Korea Institute of Science and Technology Information, Daejeon, South Korea; and K. Y. Byun, T. Y. Lee, J. Kim, and M. S. Joh

Poster PDF (1.9 MB)

This paper introduces the Korea Land Data Assimilation System (KLDAS) developed to provide mesoscale weather prediction model with high-resolution land surface initial conditions over East Asia. The KLDAS is an uncoupled land surface modeling system based on the WRF Preprocessing System (WPS) and MODIS land products. It is driven by atmospheric forcings such as observed rainfall, satellite-derived downward solar radiation, and analysis-based near-surface meteorological conditions. In the East Asia region, KLDAS reaches a quasi-equilibrium state for soil and surface conditions after only 13 months integration. Atmospheric forcing conditions generated in KLDAS are evaluated against observation data on the Korean Peninsula. Soil temperature, moisture, and surface heat fluxes simulated by KLDAS are comparable to on-sites observations. Two case studies show that the simulation with high resolution land surface initial condition based on KLDAS is found to produce improved near-surface simulations compared to that with global model based land surface initialization.