Exploring the Dynamics of Sea Breeze Fronts in the Southwestern Caspian Sea: Analysis Using Observational Data, WRF Simulations, and Machine Learning Approaches

This research investigates the dynamics of the Sea Breeze Front (SBF) in the southwestern Caspian Sea, specifically focusing on Bandar Anzali, Iran. Utilizing two years of observational data alongside Weather Research and Forecasting (WRF) model simulations, the study examines the meteorological characteristics and behaviors associated with SBF events. SBF days were identified by analyzing land-sea temperature contrasts, supported by wind shifts, temperature decreases, increases in humidity, and cloud formation.

In-depth analysis reveals consistent atmospheric patterns during SBF events, such as temperature variations and notable wind shifts. The intensity of the land-sea thermal contrast is influenced by both local topography and atmospheric stability. A detailed case study of March 4, 2022, highlighted key meteorological changes, including temperature drops and wind direction shifts. While the WRF model accurately captured temperature and pressure variations, it slightly underestimated humidity and dew point.

Machine learning techniques, particularly K-means clustering, were employed to classify distinct atmospheric regimes linked to SBF occurrences. The clustering analysis identified two primary atmospheric patterns: cold, humid air masses favorable to SBF development, emphasizing the significant role of land-sea temperature gradients and local wind dynamics.

This study highlights the value of combining observational data, numerical simulations, and machine learning techniques to better understand coastal mesoscale processes. The findings provide fresh insights into SBF behavior in the Caspian region, with implications for enhancing coastal weather forecasting and management. Future work should focus on improving the accuracy of WRF model simulations and further examining the impact of regional topography on SBF dynamics.

 

 

Keywords: Sea Breeze Front, Machine Learning, WRF, K-means Clustering, Temperature Gradient, Caspian Sea,.