Articles | Volume 15, issue 22
https://doi.org/10.5194/amt-15-6723-2022
https://doi.org/10.5194/amt-15-6723-2022
Research article
 | 
22 Nov 2022
Research article |  | 22 Nov 2022

Synergistic retrieval and complete data fusion methods applied to simulated FORUM and IASI-NG measurements

Marco Ridolfi, Cecilia Tirelli, Simone Ceccherini, Claudio Belotti, Ugo Cortesi, and Luca Palchetti

Related authors

On the use of Infrared Atmospheric Sounding Interferometer (IASI) spectrally resolved radiances to test the EC-Earth climate model (v3.3.3) in clear-sky conditions
Stefano Della Fera, Federico Fabiano, Piera Raspollini, Marco Ridolfi, Ugo Cortesi, Flavio Barbara, and Jost von Hardenberg
Geosci. Model Dev., 16, 1379–1394, https://doi.org/10.5194/gmd-16-1379-2023,https://doi.org/10.5194/gmd-16-1379-2023, 2023
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
CCl4 distribution derived from MIPAS ESA v7 data: intercomparisons, trend, and lifetime estimation
Massimo Valeri, Flavio Barbara, Chris Boone, Simone Ceccherini, Marco Gai, Guido Maucher, Piera Raspollini, Marco Ridolfi, Luca Sgheri, Gerald Wetzel, and Nicola Zoppetti
Atmos. Chem. Phys., 17, 10143–10162, https://doi.org/10.5194/acp-17-10143-2017,https://doi.org/10.5194/acp-17-10143-2017, 2017
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

Agócs, T., Vanin, F., Laberinti, P., Oetjen, H., Serlenga, D., Sole, M. P., Salenc, C., Lamarre, D., Kaspers, M., Rodrigues, G., Carou, A. M., Strotmann, S., Copano, M., Lippa, M., and Morini, C.: Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) – System Overview and Key Technology Developments of ESA's 9th Earth Explorer, in: IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 7186–7189, https://doi.org/10.1109/IGARSS46834.2022.9883892, 2022. a
Aires, F., Aznay, O., Prigent, C., Paul, M., and Bernardo, F.: Synergistic multi-wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp-A, J. Geophys. Res.-Atmos., 117, D18, https://doi.org/10.1029/2011JD017188, 2012. a, b
Amato, U., Masiello, G., Serio, C., and Viggiano, M.: The σ-IASI code for the calculation of infrared atmospheric radiance and its derivatives, Environ. Model. Softw., 17, 651–667, https://doi.org/10.1016/S1364-8152(02)00027-0, 2002. a
Andrey-Andrés, J., Fourrié, N., Guidard, V., Armante, R., Brunel, P., Crevoisier, C., and Tournier, B.: A simulated observation database to assess the impact of the IASI-NG hyperspectral infrared sounder, Atmos. Meas. Tech., 11, 803–818, https://doi.org/10.5194/amt-11-803-2018, 2018. a
Ben-Yami, M., Oetjen, H., Brindley, H., Cossich, W., Lajas, D., Maestri, T., Magurno, D., Raspollini, P., Sgheri, L., and Warwick, L.: Emissivity retrievals with FORUM's end-to-end simulator: challenges and recommendations, Atmos. Meas. Tech., 15, 1755–1777, https://doi.org/10.5194/amt-15-1755-2022, 2022. a
Download
Short summary
Synergistic retrieval (SR) and complete data fusion (CDF) methods exploit the complementarity of coinciding remote-sensing measurements. We assess the performance of the SR and CDF methods on the basis of synthetic measurements of the FORUM and IASI-NG missions. In the case of perfectly matching measurements, SR and CDF results differ by less than 1 / 10 of the error due to measurement noise. In the case of a realistic mismatch, the two methods show differences in the order of their error bars.