Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 11
Articles | Volume 16, issue 11
https://doi.org/10.5194/amt-16-2733-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-2733-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 11
Research article
 | Highlight paper
 | 
02 Jun 2023
Research article | Highlight paper |  | 02 Jun 2023

Applying machine learning to improve the near-real-time products of the Aura Microwave Limb Sounder

Frank Werner, Nathaniel J. Livesey, Luis F. Millán, William G. Read, Michael J. Schwartz, Paul A. Wagner, William H. Daffer, Alyn Lambert, Sasha N. Tolstoff, and Michelle L. Santee

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The paper introduces a machine learning based retrieval algorithm for Aura/MLS, which could lead to a major update of the Aura/MLS NRT L2 products.
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Short summary
The algorithm that produces the near-real-time data products of the Aura Microwave Limb Sounder has been updated. The new algorithm is based on machine learning techniques and yields data products with much improved accuracy. It is shown that the new algorithm outperforms the previous versions, even when it is trained on only a few years of satellite observations. This confirms the potential of applying machine learning to the near-real-time efforts of other current and future mission concepts.
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