A deep learning approach for identifying coastal sea breezes globally

Sea breezes can occur on any coast of the world affecting meteorological variables broadly used to detect sea breeze episodes. To date, there is no universal method to identify sea breezes that works all over the globe. Most existing methodologies develop their own selection methods based on thresholds related to the local characteristics of the study site: e.g. wind direction based on the coastline orientation, cloud cover, precipitation, humidity, land-sea air temperature difference, pressure amplitude, insolation, among others. However, detecting past episodes from well-defined criteria makes sea breeze identification dependent on these criteria. This makes most classified events on the same site differ from each other, as well as making extrapolation to other regions difficult. The scarcity of high-resolution observed historical data over land and sea surfaces has limited the sea breeze understanding in many coastal regions across the world. To address the need of developing a universal method applicable to any coastal region of the globe, here we explore deep learning techniques (e.g. deep convolutional neural networks). We train these models using a time-series of high-resolution physical reanalysis (e.g. ERA-5 Land) gridded and observed data, after identifying 5 years of sea breezes manually for random stations around the globe. Results from this study constitute the first historical sea breeze global database spanning approximately the last 40 years. The ability of machine learning models to detect sea breezes allows the development of a simple and universal approach, which will improve the understanding of sea breeze at spatial scales which has not been addressed before, as far as we know.