Fog Simulations Based on Multi-Model System: A Feasibility Study

Abstract

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 PA-FOG 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.

A Numerical Study of Sea-Fog Formation over Cold Sea Surface Using a One-Dimensional Turbulence Model Coupled with the Weather Research and Forecasting Model

“…However, this may not apply to all cases of fog. For example, Shi et al (2011) coupled PAFOG with MM5 to simulate the radiation fog in China but found that moisture advection was not well represented in their simulation and PAFOG failed to generate the vertical growth of the fog layer. Figure 8a shows the time series of air temperature, dewpoint temperature, SST, and the integrated liquid water path at 5 m altitude calculated from the Eulerian approach.…”

“…However, a typical 1D turbulence model does not resolve the horizontal advection or pressure gradient force and therefore is limited to applications in radiation-fog studies. More recently, a 1D turbulence model was coupled with a 3D model to compensate for the intrinsic limitations of the 1D turbulence model (e.g., Holtslag et al 1990; Bergot and Guedalia 1994;Koracin et al 2001;Müller et al 2007;Shi et al 2011). Bergot and Guedalia (1994), Müller et al (2007), and Shi et al (2011) added the horizontal advection of heat and moisture simulated by a 3D regional model into the tendency equations of heat and moisture in a 1D turbulence model.…”

“…More recently, a 1D turbulence model was coupled with a 3D model to compensate for the intrinsic limitations of the 1D turbulence model (e.g., Holtslag et al 1990; Bergot and Guedalia 1994;Koracin et al 2001;Müller et al 2007;Shi et al 2011). Bergot and Guedalia (1994), Müller et al (2007), and Shi et al (2011) added the horizontal advection of heat and moisture simulated by a 3D regional model into the tendency equations of heat and moisture in a 1D turbulence model. Meanwhile, Holtslag et al (1990) and Koracin et al (2001) considered an air column moving along a trajectory simulated by a 3D regional model and studied atmospheric boundary-layer modification caused by the change in surface condition.…”

Improvement of Fog Simulation by the Nudging of Meteorological Tower Data in the WRF and PAFOG Coupled Model

“…Indeed, restricted visibility due to fog can hamper safe navigation of ships, onshore marine traffic systems in coastal regions and can result in marine traffic accidents (e.g., Fu et al, 2010 [3]). Despite many previous studies to better understand fog formation mechanism better (e.g., Duynkerke 1991;Fuzzi et al, 1992;Wobrock et al, 1992;Nakanishi 2000;Gultepe et al, 2007;Fu et al, 2006;Shi et al, 2011;Pu et al, 2016;Lin et al, 2017 [4-12]), fog forecasting is challenging because the formation of fog is a consequence of complex and nonlinear interactions at various scales from the synoptic conditions to the micro scale and topographical conditions (Steeneveld and de Bode 2018 [13]).…”

Ion Composition of Fog Water and Its Relation to Air Pollutants during Winter Fog Events in Nanjing, China

“…In addition, high levels of anthropogenic aerosols, especially a hygroscopic aerosol like sulfate, can change the light scattering in the air, especially under the condition of high relative humidity (CASS, 1979); thus, high levels of aerosols can decrease the visibility to below 1 km even without measurable liquid water content (LWC) in Nanjing, China (YANG et al, 2010). Accordingly, the one-dimensional fog model (PAFOG), which includes the impacts of aerosols, outperformed MM5 in forecasting the fog dissipation time (SHI et al, 2011). Therefore, from the view of improving fog forecasting by numerical model, it is imperative to study the chemical constitution of fog water and its relation with air pollutants.…”