Effects of High-Resolution Sea Surface Temperature and Data Assimilation using the WRF-3DVAR for Fog Prediction in the West Coast of South Korea

Sang-Keun Song, Seung-Beom Han, Hyeong-Sik Park and Jae-Hong Moon, Jeju National University, Jeju, South Korea
This study investigated the effects of high-resolution sea surface temperature (SST) and data assimilation (DA) on the coastal atmosphere during a sea fog event (23 June through 1 July 2018) along the west coast of South Korea. We carried out several numerical experiments to examine the effects of SST nudging: a base WRF run (BASE) and WRF runs with the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) (SST-O), the US Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC) (SST-F), and the gridded SST simulated by the Regional Ocean Modeling System (ROMS) model (SST-R). To examine the effect of DA during the fog event, we also compared the results between WRF runs with DA by 3DVAR (WRF-3DVAR) and without DA (WRF-NODA), under the same SST condition (ROMS SST). The OSTIA SST data was produced on a daily basis at a spatial resolution of 1/20° (~6 km) and the FNMOC SST was six-hourly data at a 10-km spatial resolution. In addition, the ROMS SST data has a spatial resolution of approximately 10 km at every 1 hour. Overall, the modeling results (e.g., air temperature, wind speed, and visibility) considering SST nudging (especially, the SST-R run) during the fog event showed a good agreement with those observed, compared to the BASE run. The large improvement in the SST-R run might be due to the combined effects of tidal mixing process and water temperature fluctuations in coastal waters in the ROMS simulation. In addition, the results simulated by the WRF-3DVAR during this event provided better agreement with those observed, compared to the WRF-NODA. From the statistical analysis (e.g., Index Of Agreement (IOA) and Root Mean Square Error (RMSE)), the simulation case with both DA and ROMS SST somewhat improved IOAs and RMSEs for several meteorological variables (e.g., air temperature, wind speed, and visibility) in the study area, compared to the other simulation cases. If our improved estimation (using both high-resolution SST and DA technique) is applied to atmospheric modeling in further studies, it can significantly contribute to helping us better understand coastal meteorology (e.g., the accurate prediction of sea and land surface winds) and further coastal air quality, which is greatly affected by wind variations in coastal areas.