Assimilating MTSAT-Derived Humidity in Nowcasting Sea Fog over the Yellow Sea

Abstract

An extended three-dimensional variational data assimilation (3DVAR) method based on the Weather Research and Forecasting Model (WRF) is developed to assimilate satellite-derived humidity from sea fog at its initial stage over the Yellow Sea. The sea fog properties, including its horizontal distribution and thickness, are retrieved empirically from the infrared and visible cloud imageries of the Multifunctional Transport Satellite (MTSAT). Assuming a relative humidity of 100% in fog, the MTSAT-derived humidity is assimilated by the extended 3DVAR assimilation method. Two sea fog cases, one spread widely over the Yellow Sea and the other spread narrowly along the coast, are first studied in detail with a suite of experiments. For the widespread-fog case, the assimilation of MTSAT-derived information significantly improves the forecast of the sea fog area, increasing the probability of detection and equitable threat scores by about 20% and 15%, respectively. The improvement is attributed to a more realistic representation of the marine boundary layer (MBL) and better descriptions of moisture and temperature profiles. For the narrowly spread coastal case, the model completely fails to reproduce the sea fog event without the assimilation of MTSAT-derived humidity. The extended 3DVAR assimilation method is then applied to 10 more sea fog cases to further evaluate its effect on the model simulations. The results reveal that the assimilation of MTSAT-derived humidity not only improves sea fog forecasts but also provides better moisture and temperature structure information in the MBL.

The Impact of Turbulent Diffusion Driven by Fog‐Top Cooling on Sea Fog Development

“…Only the mechanism of gravitational settling is considered for a smooth surface, for instance, bare soil and ocean when calculating deposition velocity (Katata et al, ). A threshold of 0.016 g kg −1 for LWC is generally adopted to diagnose simulated sea fog (e.g., Gao et al, ; Wang et al, ; Yang et al, ; Zhou & Du, ).…”

“…The statistical scores shown in Table quantitatively evaluate the last 24‐hr simulation of sea fog area. The values are obtained using the objective evaluation method widely used in previous sea fog numerical studies (e.g., Gao et al, ; Wang et al, ; Yang et al, ; Zhou & Du, ). The results show that Exp‐TOP has a smaller false alarm ratio (FAR; 0.423 versus 0.473) and a bias value (Bias) closer to 1.0 (0.968 versus 1.179) than Exp‐noTOP.…”

“…Previous studies have indicated that the horizontal distribution of advection fog shows a close relationship with the saturation condition near the sea surface (e.g., Cho et al, 2000;Gao et al, 2007;Wang et al, 2014;Yang et al, 2019). Measurements of buoys and ships shown in Figure 1 are selected to evaluate the simulated saturation condition.…”

“…For instance, the annual number of fog days observed at Chengshantou (CS in Figure ) reaches up to 83 days (e.g., Cho et al, ; Gao et al, ; Zhang et al, ). In order to mitigate socioeconomic impacts of sea fog over the Yellow Sea and meet increasing practical demands, it is urgent to improve the forecast skill of sea fog (Gao et al, ; Wang et al, ). For accurate sea fog prediction, one major prerequisite is to well understand the physical mechanism of its evolution.…”

Assessing the Impact of Surface and Wind Profiler Data on Fog Forecasting Using WRF 3DVAR: An OSSE Study on a Dense Fog Event over North China

“…In recent years, DA studies on fog prediction have examined the roles of both techniques and observations. With regard to DA techniques, the potential of 1D variational (1DVAR; Bergot et al 2005;Muller et al 2007) and 3D variational (3DVAR; Gao et al 2010;Wang et al 2014) data assimilation, as well as more-advanced techniques such as 4D variational data assimilation (4DVAR; Vandenberghe and Ware 2003) and the ensemble Kalman filter (EnKF; Remy and Bergot 2010), has been explored and promising results have been obtained. The results showed that fog forecasting was greatly improved as a result of improvement of model initial conditions after DA.…”