Evaluation of AIRS Retrieval Approaches in the Planetary Boundary Layer
Abstract
Recent investigations suggest that microwave and hyperspectral infrared retrievals are challenged in representing the height and other features of the planetary boundary layer (PBL). PBL features, in general, are difficult to observe with IR sounding instruments due to instrument vertical resolution. Due to the limited direct observability of boundary layer features, machine learning approaches such as neural networks can play a unique role in discovering and reproducing complex or indirect empirical relationships between the PBL features and the observations. In current AIRS/AMSU Level 2 retrievals of temperature and water vapor, boundary layer phenomenology is introduced, to a significant extent, via the neural network first guess. In this work, we review the current state of hyperspectral IR sounding of the planetary boundary layer (PBL), with the goal of evaluating best available current temperature and water vapor sounding capability from neural network and physical retrievals. Our study uses sonde data from the Southern Great Plains site in Oklahoma operated by the Atmospheric Radiation Measurement program as ground truth. The retrievals evaluated include the current AIRS version 6 physical retrieval product, the version 6 neural network first guess, the version 7 candidate neural network first guess, and our proposed new PBL-focused neural network and physical retrieval concepts.
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. 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 .- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A11T2824B
- Keywords:
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- 3307 Boundary layer processes;
- ATMOSPHERIC PROCESSES;
- 3360 Remote sensing;
- ATMOSPHERIC PROCESSES