Effect of soil moisture uncertainty on the heavy rainfall prediction over the Korean Peninsula

Short-range (< 3 days) prediction of heavy rainfall events is one of the most challenging issues, partly due to the large uncertainty in the numerical weather prediction (NWP) model simulation near the surface. In the state-of-the-art mesoscale ensemble prediction system, surface and near-surface states are commonly under-dispersive due to the lack of surface perturbations, which often leads to poor weather forecasts, especially for surface-based convective cases.

The purpose of this presentation is to clarify the effects of accounting for the soil moisture uncertainty on the forecast of heavy precipitation over the Korean Peninsula during the summer of 2012. Our short-range ensemble prediction system is composed of a 24-member ensemble using the Weather and Research Forecast Model Advanced Research (WRF-ARW) model. Each member is configured with a triple nesting down to the finest grid resolution at 5 km that covers the Korean Peninsula. The initial ensemble perturbations for atmospheric states are generated by Ensemble Transform Kalman Filter (ETKF), and are centered at the National Center for Environmental Prediction (NCEP) final analysis. Soil moisture perturbations are produced by static recursive filtering methods in which the variance and the spatial correlation length of soil moisture states are calculated based on the soil moisture retrievals from Advanced Microwave Scanning Radiometer for EOS (AMSE-E) observation. The 24-member ensemble is run for 36 h for multiple heavy rainfall cases that are different in terms of the forcing mechanism and the thermo-dynamic structure. The effect of soil moisture perturbations on the precipitation forecast is discussed in terms of the reliability of the ensemble prediction system and the performance skill of the ensemble mean forecast.