Neural Network Techniques for Hyperspectral IR Profiling of Cloudy Atmospheres

In recent decades, spaceborne microwave and hyperspectral infrared (IR) sounding instruments have led to significant enhancements in the accuracy of numerical weather prediction (NWP) including short-term NWP forecast of severe weather such as tornado outbreaks. MIT Lincoln Laboratory has pioneered the use of neural networks in retrieving the three-dimensional distribution of atmospheric temperature and water vapor from these observations, and, in recent operational use by NASA in the Level 2 retrieval algorithm suite for the Atmospheric Infrared Sounder, this work has significantly improved accuracy and yield in cloud-covered scenes versus previous efforts. More recent research efforts are focused on machine learning retrieval uncertainty quantification and application of deep learning and sensor fusion techniques to improve sounding performance in the planetary boundary layer, the region closest to the Earth’s surface. Here, we describe all of these neural network algorithm efforts, and present recent validation results.

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This material is based upon work supported by the National Aeronautics and Space Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Aeronautics and Space Administration .