Integration of Local Observations into the One Dimensional Fog Model PAFOG

Abstract

The numerical prediction of fog requires a very high vertical resolution of the atmosphere. Owing to a prohibitive computational effort of high resolution three dimensional models, operational fog forecast is usually done by means of one dimensional fog models. An important condition for a successful fog forecast with one dimensional models consists of the proper integration of observational data into the numerical simulations. The goal of the present study is to introduce new methods for the consideration of these data in the one dimensional radiation fog model PAFOG. First, it will be shown how PAFOG may be initialized with observed visibilities. Second, a nudging scheme will be presented for the inclusion of measured temperature and humidity profiles in the PAFOG simulations. The new features of PAFOG have been tested by comparing the model results with observations of the German Meteorological Service. A case study will be presented that reveals the importance of including local observations in the model calculations. Numerical results obtained with the modified PAFOG model show a distinct improvement of fog forecasts regarding the times of fog formation, dissipation as well as the vertical extent of the investigated fog events. However, model results also reveal that a further improvement of PAFOG might be possible if several empirical model parameters are optimized. This tuning can only be realized by comprehensive comparisons of model simulations with corresponding fog observations.

A Case Study: Evaluation of PAFOG One‐D Model With Advection in Simulations of Fog/Stratus From C‐FOG Experiment

“…Their results also showed that PAFOG had ability to simulate stratiform clouds, where large‐scale subsidence played a critical role in suppressing the vertical growth and density rates of cloud. PAFOG is also widely used around the world (i.e., Kim, Yum, Hong, & Song, 2020; Kim, Yum, & Kim, 2020; Shi et al., 2012; Thoma et al., 2012). Thoma et al.…”

“…Their results also showed that PAFOG had ability to simulate stratiform clouds, where large-scale subsidence played a critical role in suppressing the vertical growth and density rates of cloud. PAFOG is also widely used around the world (i.e., Kim, Yum, Hong, & Song, 2020;Shi et al, 2012;Thoma et al, 2012). Thoma et al (2012) suggested that PAFOG with a nudging scheme and application of observed visibility represented a distinct improvement over previous model versions, for forecasts of fog onset and dissipation times.…”

“…PAFOG is also widely used around the world (i.e., Kim, Yum, Hong, & Song, 2020;Shi et al, 2012;Thoma et al, 2012). Thoma et al (2012) suggested that PAFOG with a nudging scheme and application of observed visibility represented a distinct improvement over previous model versions, for forecasts of fog onset and dissipation times. Kim, Yum, Hong, and Song (2020) and applied an WRF-PAFOG coupled model system to investigate fog occurring along the south coast of the Korean Peninsula.…”