Fog simulations based on multi-model system: A feasibility study

摘要

Accurate forecasts of fog and visibility are very important to air and high way traffic, and are still a big challenge. A 1D fog model (PAFOG) is coupled to MM5 by obtaining the initial and boundary conditions (IC/BC) and some other necessary input parameters from MM5. Thus, PAFOG can be run for any area of interest. On the other hand, MM5 itself can be used to simulate fog events over a large domain. This paper presents evaluations of the fog predictability of these two systems for December of 2006 and December of 2007, with nine regional fog events observed in a field experiment, as well as over a large domain in eastern China. Among the simulations of the nine fog events by the two systems, two cases were investigated in detail. Daily results of ground level meteorology were validated against the routine observations at the CMA observational network. Daily fog occurrences for the two study periods was validated in Nanjing. General performance of the two models for the nine fog cases are presented by comparing with routine and field observational data. The results of MM5 and PAFOG for two typical fog cases are verified in detail against field observations. The verifications demonstrated that all methods tended to overestimate fog occurrence, especially for near-fog cases. In terms of TS/ETS, the LWC-only threshold with MM5 showed the best performance, while PAFOG showed the worst. MM5 performed better for advection-radiation fog than for radiation fog, and PAFOG could be an alternative tool for forecasting radiation fogs. PAFOG did show advantages over MM5 on the fog dissipation time. The performance of PAFOG highly depended on the quality of MM5 output. The sensitive runs of PAFOG with different IC/BC showed the capability of using MM5 output to run the 1D model and the high sensitivity of PAFOG on cloud cover. Future works should intensify the study of how to improve the quality of input data (e.g. cloud cover, advection, large scale subsidence) for the 1D model, particularly how to eliminate near-fog case in fog forecasting.