Preprints
https://doi.org/10.5194/amt-2023-238
https://doi.org/10.5194/amt-2023-238
23 Nov 2023
 | 23 Nov 2023
Status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Advantages of assimilating multi-spectral satellite retrievals of atmospheric composition: A demonstration using MOPITT CO products

Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Daniel Ziskin, Debbie Mao, David Edwards, Avelino Arellano, Kevin Raeder, Jeffrey Anderson, and Helen Worden

Abstract. The Measurements Of Pollution In The Troposphere (MOPITT) is an ideal instrument to understand the impact of (1) assimilating multispectral/joint retrievals versus single-spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral/joint retrievals versus assimilating individual products separately. We use the Community Atmosphere Model with chemistry with the Data Assimilation Research Testbed (CAM-chem+DART) to assimilate different MOPITT CO products to address these three questions. Both anthropogenic and fire CO emissions are optimized in the data assimilation experiments. The results are compared with independent CO observations from TROPOspheric Monitoring Instrument (TROPOMI), the Total Carbon Column Observing Network (TCCON), NOAA Carbon Cycle Greenhouse Gases (CCGG) sites, In-service Aircraft for a Global Observing System (IAGOS), and Western wildfire Experiment for Cloud chemistry, Aerosol absorption and Nitrogen (WE-CAN). We find that (1) assimilating the MOPITT joint (multispectral Near-IR and Thermal-IR) column product leads to better model-observation agreement at and near the surface than assimilating the MOPITT Thermal-IR-only column retrieval. (2) Assimilating column products has a larger impact and improvement for background and large-scale CO compared to assimilating profile products due to vertical localization in profile assimilation. However, profile assimilation can out-perform column assimilations in fire-impacted regions and near the surface. (3) Assimilating multispectral/joint products results in similar or slightly better agreement with observations compared to assimilating the single-spectral products separately.

Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Daniel Ziskin, Debbie Mao, David Edwards, Avelino Arellano, Kevin Raeder, Jeffrey Anderson, and Helen Worden

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-238', Anonymous Referee #1, 24 Nov 2023
  • RC2: 'Comment on amt-2023-238', Anonymous Referee #2, 15 Dec 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-238', Anonymous Referee #1, 24 Nov 2023
  • RC2: 'Comment on amt-2023-238', Anonymous Referee #2, 15 Dec 2023
Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Daniel Ziskin, Debbie Mao, David Edwards, Avelino Arellano, Kevin Raeder, Jeffrey Anderson, and Helen Worden
Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Daniel Ziskin, Debbie Mao, David Edwards, Avelino Arellano, Kevin Raeder, Jeffrey Anderson, and Helen Worden

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Short summary
We assimilate different MOPITT CO products to understand the impact of (1) assimilating multispectral/joint retrievals versus single-spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral/joint retrievals versus assimilating individual products separately.