3. Atmospheric and Oceanic Remote Sensing Applications

This chapter is devoted to atmospheric and oceanic satellite remote sensing (RS) NN applications. Two major RS problems, forward problem and inverse problem (or satellite retrievals), are introduced and discussed. Applications of forward models (FM) for solving the forward problem in the process of direct assimilation of satellite measurements and for variational retrievals, as well as applications of retrieval algorithms, solutions of the inverse problem, for assimilation of geophysical parameters in data assimilation systems, are discussed. Correspondingly, two neural network (NN) applications, NN FMs and NN retrieval algorithms, are introduced. An intelligent NN retrieval system, which incorporates an automatic quality control of satellite retrievals, is introduced. Previously developed RS NN applications are reviewed. Theoretical considerations are illustrated with real-life applications of the NN approach to the Special Sensor Microwave Imager (SSM/I). SSM/I NN FM and SSM/I NN retrieval algorithms are introduced, discussed, and compared with FMs and retrieval algorithms developed using other techniques elsewhere. Advantages and limitations of NN FMs and retrieval algorithms are discussed. An example of QuikSCAT wind vector retrievals is used to demonstrate great potential of using the NN technique to go beyond the standard point-wise retrieval paradigm. This chapter contains an extensive list of references giving extended background and further detail to the interested reader on each examined topic. It can serve as a textbook and an introductory reading for students and beginning and advanced investigators interested in learning how to apply the NN emulation technique in different RS applications.