The Planetary Boundary Layer (PBL) is the layer of atmosphere bordering the surface of the earth and represents the greatest importance to human activities. It is also the most difficult layer of the atmosphere to measure directly.
The Marine Atmospheric Boundary Layer (MABL) event has greater complications with high variability over a wide range of temporal and spatial scales due to high horizontal heterogeneity introduced by mesoscale ocean phenomena. Additionally, in situ observations of the MABL are sparse when compared to land based in situ observations. A complete observational approach that accounts for the ocean, the MABL thermodynamic structure, and clouds is required to properly profile the MABL.
We are collaborating with 4S-Silversword Software and Services, LLC and the Joint Center for Earth Systems Technology (JCET), University of Maryland Baltimore County under the Department of Defense’s Office of Naval Research on Small Business Technology Transfer (STTR) to utilize innovative AI solutions to enhance Marine Atmospheric Boundary Layer profiling via Satellite Based Remote Sensing Data Fusion. We are investigating the space-based, remote-sensing technologies best suited to observe conditions characteristic of the MABL and develop AI-based retrieval algorithms including Deep Edge Detection and Deep Learning Data fusion to derive MABL characteristics from chosen remote sensing technologies.