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

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

doi:https://doi.org/10.5194/amt-16-2733-2023

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

Data sets

MLS/Aura L1 Radiances from Digital Autocorrelators V005 R. Jarnot and V. Perun https://doi.org/10.5067/Aura/MLS/DATA1502

MLS/Aura Level 2 Water Vapor (H2O) Mixing Ratio V005 A. Lambert, W. Read, and N. Livesey https://doi.org/10.5067/Aura/MLS/DATA2508

MLS/Aura Level 2 Nitrous Oxide (N2O) Mixing Ratio V005 A. Lambert, N. Livesey, and W. Read https://doi.org/10.5067/Aura/MLS/DATA2515

MLS/Aura Level 2 Nitric Acid (HNO3) Mixing Ratio V005 G. Manney, M. Santee, L. Froidevaux, N. Livesey, and W. Read https://doi.org/10.5067/Aura/MLS/DATA2511

MLS/Aura Level 2 Sulfur Dioxide (SO2) Mixing Ratio V005 W. Read and N. Livesey https://doi.org/10.5067/Aura/MLS/DATA2519

MLS/Aura Level 2 Temperature V005 M. Schwartz, N. Livesey, and W. Read https://doi.org/10.5067/Aura/MLS/DATA2520

MLS/Aura Level 2 Ozone (O3) Mixing Ratio V005 M. Schwartz, L. Froidevaux, N. Livesey, and W. Read https://doi.org/10.5067/Aura/MLS/DATA2516

MLS/Aura Level 2 Carbon Monoxide (CO) Mixing Ratio V005 M. Schwartz, H. Pumphrey, N. Livesey, and W. Read https://doi.org/10.5067/Aura/MLS/DATA2506

MLS/Aura Near-Real-Time L2 Temperature V005 EOS MLS Science Team https://disc.gsfc.nasa.gov/datacollection/ML2T_NRT_005.html

MLS/Aura Near-Real-Time L2 Water Vapor (H2O) Mixing Ratio V005 EOS MLS Science Team https://disc.gsfc.nasa.gov/datacollection/ML2H2O_NRT_005.html

MLS/Aura Near-Real-Time L2 Ozone (O3) Mixing Ratio V005 EOS MLS Science Team https://disc.gsfc.nasa.gov/datacollection/ML2O3_NRT_005.html

MLS/Aura Near-Real-Time L2 Carbon Monoxide (CO) Mixing Ratio V005 EOS MLS Science Team https://disc.gsfc.nasa.gov/datacollection/ML2CO_NRT_005.html

MLS/Aura Near-Real-Time L2 Sulfur Dioxide (SO2) Mixing Ratio V005 EOS MLS Science Team https://disc.gsfc.nasa.gov/datacollection/ML2SO2_NRT_005.html

MLS/Aura Near-Real-Time L2 Nitric Acid (HNO3) Mixing Ratio V005 EOS MLS Science Team https://disc.gsfc.nasa.gov/datacollection/ML2HNO3_NRT_005.html

MLS/Aura Near-Real-Time L2 Nitrous Oxide (N2O) Mixing Ratio V005 EOS MLS Science Team https://disc.gsfc.nasa.gov/datacollection/ML2N2O_NRT_005.html

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.