Simulation and Prediction of Ocean Data
This chapter presents a novel combined digital framework that is dedicated to the analysis and prediction of complex microwave remotely sensed data. The framework operates with multifactor microwave stochastic models and includes elements of digital signal/image processing and computer vision. The framework provides digital modeling and simulations of a variety of ocean microwave remote sensing data, scenes, and scenarios. A framework implementation provides a spatial averaging and spatial intermittent connectivity of the contributions at variable observation conditions. The microwave emission contributions related to individual environmental factors are defined using the corresponding electromagnetic models. Roughness–salinity–temperature anomalies (RSTA) represent complex thermohydrodynamic features associated with simultaneous variations of sea surface roughness, surface salinity, and temperature. Experimental oceanographic data show that the near-surface layer of the upper ocean, including the air–water interface and electromagnetic skin layer, is usually unstable and nonuniform.