Cloud coverage estimation for improved atmospheric acoustic predictions

Weather is an important factor in atmospheric acoustic transmission loss over moderate distances, 1–10 km. One critical issue is cloud cover. This work focuses on measuring the cloud coverage at a costal location that also has permanent temperature logging masts and a wind speed profiling LIDAR which characterizes the coastal meteorological conditions. Improving cloud coverage estimates improves surface heat flux estimates, which in turn supports acoustic propagation modeling. The cloud cover is determined from photos which are analyzed in MATLAB using RGB filtering. Photographic data collected on July 6, 15, and 25 of 2023 will be presented. These photos were taken at intervals of 2 min, over the course of a day, under changing weather conditions. The simple RGB filter was successful for cases without rain throughout most of the day with the exceptions primarily occurring near sunrise and sunset. [Work supported by the Robert W. Young Award for Undergraduate Student Research in Acoustics.]