Numerical Analysis of Fog Predictability over National Center for Intensive Observation of Severe Weather (NCIO)

Fog forecasting has been known to be very difficult due to the multi-scale nature of its formation mechanism: not only the synoptic conditions but also the local meteorological conditions crucially influence fog formation. In this study, we use a coupled model system of a 3D mesoscale model (WRF) and a single column model with a fine vertical resolution (PAFOG, PArameterized FOG) to analyze the predictability of fog formed over the southern coastal region of the Korean Peninsula, where National Center for Intensive Observation of Severe Weather (NCIO) is located. The center is unique in that a 300 m meteorological tower is built at the location to measure basic meteorological variables (temperature, dew point temperature and winds) at eleven different altitudes, and comprehensive physics measurements are made with the various remote sensing instruments such as cloud radar, wind profiler, microwave radiometer, and ceilometer. The comprehensive set of measurement data from NCIO will be utilized as input data to the model system and for evaluating the results. Particularly the data for initial and external forcings, which are tightly connected to the coupled model predictability, are derived from the tower measurement. With sensitivity experiments, this study aims at finding out the most important factors that influence fog preditability at NCIO such as tower data nudging, coupling/non-coupling, consideration of local soil conditions. Detailed results will be discussed at the conference.