Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-2041-2021
https://doi.org/10.5194/amt-14-2041-2021
Research article
 | 
12 Mar 2021
Research article |  | 12 Mar 2021

Application of the Complete Data Fusion algorithm to the ozone profiles measured by geostationary and low-Earth-orbit satellites: a feasibility study

Nicola Zoppetti, Simone Ceccherini, Bruno Carli, Samuele Del Bianco, Marco Gai, Cecilia Tirelli, Flavio Barbara, Rossana Dragani, Antti Arola, Jukka Kujanpää, Jacob C. A. van Peet, Ronald van der A, and Ugo Cortesi

Related authors

An improved formula for the complete data fusion
Simone Ceccherini, Nicola Zoppetti, and Bruno Carli
Atmos. Meas. Tech., 15, 7039–7048, https://doi.org/10.5194/amt-15-7039-2022,https://doi.org/10.5194/amt-15-7039-2022, 2022
Short summary
Level 2 processor and auxiliary data for ESA Version 8 final full mission analysis of MIPAS measurements on ENVISAT
Piera Raspollini, Enrico Arnone, Flavio Barbara, Massimo Bianchini, Bruno Carli, Simone Ceccherini, Martyn P. Chipperfield, Angelika Dehn, Stefano Della Fera, Bianca Maria Dinelli, Anu Dudhia, Jean-Marie Flaud, Marco Gai, Michael Kiefer, Manuel López-Puertas, David P. Moore, Alessandro Piro, John J. Remedios, Marco Ridolfi, Harjinder Sembhi, Luca Sgheri, and Nicola Zoppetti
Atmos. Meas. Tech., 15, 1871–1901, https://doi.org/10.5194/amt-15-1871-2022,https://doi.org/10.5194/amt-15-1871-2022, 2022
Short summary
The ESA MIPAS/Envisat level2-v8 dataset: 10 years of measurements retrieved with ORM v8.22
Bianca Maria Dinelli, Piera Raspollini, Marco Gai, Luca Sgheri, Marco Ridolfi, Simone Ceccherini, Flavio Barbara, Nicola Zoppetti, Elisa Castelli, Enzo Papandrea, Paolo Pettinari, Angelika Dehn, Anu Dudhia, Michael Kiefer, Alessandro Piro, Jean-Marie Flaud, Manuel López-Puertas, David Moore, John Remedios, and Massimo Bianchini
Atmos. Meas. Tech., 14, 7975–7998, https://doi.org/10.5194/amt-14-7975-2021,https://doi.org/10.5194/amt-14-7975-2021, 2021
Short summary
Phosgene distribution derived from MIPAS ESA v8 data: intercomparisons and trends
Paolo Pettinari, Flavio Barbara, Simone Ceccherini, Bianca Maria Dinelli, Marco Gai, Piera Raspollini, Luca Sgheri, Massimo Valeri, Gerald Wetzel, Nicola Zoppetti, and Marco Ridolfi
Atmos. Meas. Tech., 14, 7959–7974, https://doi.org/10.5194/amt-14-7959-2021,https://doi.org/10.5194/amt-14-7959-2021, 2021
Short summary
The cost function of the data fusion process and its application
Simone Ceccherini, Nicola Zoppetti, Bruno Carli, Ugo Cortesi, Samuele Del Bianco, and Cecilia Tirelli
Atmos. Meas. Tech., 12, 2967–2977, https://doi.org/10.5194/amt-12-2967-2019,https://doi.org/10.5194/amt-12-2967-2019, 2019
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
The differences between remote sensing and in situ air pollutant measurements over the Canadian oil sands
Xiaoyi Zhao, Vitali Fioletov, Debora Griffin, Chris McLinden, Ralf Staebler, Cristian Mihele, Kevin Strawbridge, Jonathan Davies, Ihab Abboud, Sum Chi Lee, Alexander Cede, Martin Tiefengraber, and Robert Swap
Atmos. Meas. Tech., 17, 6889–6912, https://doi.org/10.5194/amt-17-6889-2024,https://doi.org/10.5194/amt-17-6889-2024, 2024
Short summary
NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations
Leon Kuhn, Steffen Beirle, Sergey Osipov, Andrea Pozzer, and Thomas Wagner
Atmos. Meas. Tech., 17, 6485–6516, https://doi.org/10.5194/amt-17-6485-2024,https://doi.org/10.5194/amt-17-6485-2024, 2024
Short summary
Improved convective cloud differential (CCD) tropospheric ozone from S5P-TROPOMI satellite data using local cloud fields
Swathi Maratt Satheesan, Kai-Uwe Eichmann, John P. Burrows, Mark Weber, Ryan Stauffer, Anne M. Thompson, and Debra Kollonige
Atmos. Meas. Tech., 17, 6459–6484, https://doi.org/10.5194/amt-17-6459-2024,https://doi.org/10.5194/amt-17-6459-2024, 2024
Short summary
Atmospheric propane (C3H8) column retrievals from ground-based FTIR observations in Xianghe, China
Minqiang Zhou, Pucai Wang, Bart Dils, Bavo Langerock, Geoff Toon, Christian Hermans, Weidong Nan, Qun Cheng, and Martine De Mazière
Atmos. Meas. Tech., 17, 6385–6396, https://doi.org/10.5194/amt-17-6385-2024,https://doi.org/10.5194/amt-17-6385-2024, 2024
Short summary
Can the remote sensing of combustion phase improve estimates of landscape fire smoke emission rate and composition?
Farrer Owsley-Brown, Martin J. Wooster, Mark J. Grosvenor, and Yanan Liu
Atmos. Meas. Tech., 17, 6247–6264, https://doi.org/10.5194/amt-17-6247-2024,https://doi.org/10.5194/amt-17-6247-2024, 2024
Short summary

Cited articles

Aires, F., Aznay, O., Prigent, C., Paul, M., and Bernardo, F.: Synergistic multi-wavelength remote sensing versus a posterior combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp-A, J. Geophys. Res., 117, D18304, https://doi.org/10.1029/2011JD017188, 2012. 
AURORA consortium (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications, grant no. 687428): Technical Note On L2 Data Simulations [D3.4], 35 pp., available at: https://cordis.europa.eu/project/id/687428/results (last access: 29 December 2020), 2017. 
Ceccherini, S., Carli, B., and Raspollini, P.: Equivalence of data fusion and simultaneous retrieval, Opt. Express, 23, 8476–8488, https://doi.org/10.1364/OE.23.008476, 2015. 
Ceccherini, S., Carli, B., Tirelli, C., Zoppetti, N., Del Bianco, S., Cortesi, U., Kujanpää, J., and Dragani, R.: Importance of interpolation and coincidence errors in data fusion, Atmos. Meas. Tech., 11, 1009–1017, https://doi.org/10.5194/amt-11-1009-2018, 2018. 
Ceccherini, S., Zoppetti, N., Carli, B., Cortesi, U., Del Bianco, S., and Tirelli, C.: The cost function of the data fusion process and its application, Atmos. Meas. Tech., 12, 2967–2977, https://doi.org/10.5194/amt-12-2967-2019, 2019. 
Download
Short summary
The new platforms for Earth observation from space will provide an enormous amount of data that can be hard to exploit as a whole. The Complete Data Fusion algorithm can reduce the data volume while retaining the information of the full dataset. In this work, we applied the Complete Data Fusion algorithm to simulated ozone profiles, and the results show that the fused products are characterized by higher information content compared to individual L2 products.