Forecasting Advective Sea Fog with the Use of Classification and Regression Tree Analyses for Kunsan Air Base

Advective sea fog frequently plagues Kunsan Air Base (AB), Republic of Korea, in the spring and summer seasons. It is responsible for a variety of impacts on military operations, the greatest being to aviation. To date, there are no suitable methods developed for forecasting advective sea fog at Kunsan, primarily due to a lack of understanding of sea fog formation under various synoptic situations over the Yellow Sea. This work explored the feasibility of predicting sea fog development with a 24-hour forecast lead time. Before exploratory data analysis was performed, a geographical introduction to the region was provided along with a discussion of basic elements of fog formation, the physical properties of fog droplets, and its dissipation. Examined in this work were data sets of Kunsan surface observations, upstream upper air data, sea surface temperatures over the Yellow Sea, and modeled analyses of gridded data over the Yellow Sea. A complete ten year period of record was examined for inclusion into data mining models to find predictive patterns.