Articles | Volume 9, issue 11
https://doi.org/10.5194/amt-9-5499-2016
https://doi.org/10.5194/amt-9-5499-2016
Research article
 | 
18 Nov 2016
Research article |  | 18 Nov 2016

Errors induced by different approximations in handling horizontal atmospheric inhomogeneities in MIPAS/ENVISAT retrievals

Elisa Castelli, Marco Ridolfi, Massimo Carlotti, Björn-Martin Sinnhuber, Oliver Kirner, Michael Kiefer, and Bianca Maria Dinelli

Related authors

Urban pollution monitoring with the AOTF-based NO2 camera: validation with other DOAS instruments
Pierre Gramme, Cedric Busschots, Emmanuel Dekemper, Didier Pieroux, Noel C. Baker, Stefano Casadio, Anna Maria lannarelli, Nicola Ferrante, Annalisa Di Bernardino, Paolo Pettinari, Elisa Castelli, Luca di Liberto, and Francesco Cairo
EGUsphere, https://doi.org/10.5194/egusphere-2025-2255,https://doi.org/10.5194/egusphere-2025-2255, 2025
Short summary
Evaluation of total column water vapour products from satellite observations and reanalyses within the GEWEX Water Vapor Assessment
Tim Trent, Marc Schröder, Shu-Peng Ho, Steffen Beirle, Ralf Bennartz, Eva Borbas, Christian Borger, Helene Brogniez, Xavier Calbet, Elisa Castelli, Gilbert P. Compo, Wesley Ebisuzaki, Ulrike Falk, Frank Fell, John Forsythe, Hans Hersbach, Misako Kachi, Shinya Kobayashi, Robert E. Kursinski, Diego Loyola, Zhengzao Luo, Johannes K. Nielsen, Enzo Papandrea, Laurence Picon, Rene Preusker, Anthony Reale, Lei Shi, Laura Slivinski, Joao Teixeira, Tom Vonder Haar, and Thomas Wagner
Atmos. Chem. Phys., 24, 9667–9695, https://doi.org/10.5194/acp-24-9667-2024,https://doi.org/10.5194/acp-24-9667-2024, 2024
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
Lee wave detection over the Mediterranean Sea using the Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) total column water vapour (TCWV) dataset
Enzo Papandrea, Stefano Casadio, Elisa Castelli, Bianca Maria Dinelli, and Mario Marcello Miglietta
Atmos. Meas. Tech., 12, 6683–6693, https://doi.org/10.5194/amt-12-6683-2019,https://doi.org/10.5194/amt-12-6683-2019, 2019
Short summary
The Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) version 2: algorithm evolution, dataset description and performance improvements
Elisa Castelli, Enzo Papandrea, Alessio Di Roma, Bianca Maria Dinelli, Stefano Casadio, and Bojan Bojkov
Atmos. Meas. Tech., 12, 371–388, https://doi.org/10.5194/amt-12-371-2019,https://doi.org/10.5194/amt-12-371-2019, 2019
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer
Luis Guanter, Jack Warren, Mark Omara, Apisada Chulakadabba, Javier Roger, Maryann Sargent, Jonathan E. Franklin, Steven C. Wofsy, and Ritesh Gautam
Atmos. Meas. Tech., 18, 3857–3872, https://doi.org/10.5194/amt-18-3857-2025,https://doi.org/10.5194/amt-18-3857-2025, 2025
Short summary
Hourly surface nitrogen dioxide retrieval from GEMS tropospheric vertical column densities: benefit of using time-contiguous input features for machine learning models
Janek Gödeke, Andreas Richter, Kezia Lange, Peter Maaß, Hyunkee Hong, Hanlim Lee, and Junsung Park
Atmos. Meas. Tech., 18, 3747–3779, https://doi.org/10.5194/amt-18-3747-2025,https://doi.org/10.5194/amt-18-3747-2025, 2025
Short summary
Remote sensing estimates of time-resolved HONO and NO2 emission rates and lifetimes in wildfires
Carley D. Fredrickson, Scott J. Janz, Lok N. Lamsal, Ursula A. Jongebloed, Joshua L. Laughner, and Joel A. Thornton
Atmos. Meas. Tech., 18, 3669–3689, https://doi.org/10.5194/amt-18-3669-2025,https://doi.org/10.5194/amt-18-3669-2025, 2025
Short summary
A study of measurement scenarios for the future CO2M mission: avoidance of detector saturation and the impact on XCO2 retrievals
Michael Weimer, Michael Hilker, Stefan Noël, Max Reuter, Michael Buchwitz, Blanca Fuentes Andrade, Rüdiger Lang, Bernd Sierk, Yasjka Meijer, Heinrich Bovensmann, John P. Burrows, and Hartmut Bösch
Atmos. Meas. Tech., 18, 3321–3340, https://doi.org/10.5194/amt-18-3321-2025,https://doi.org/10.5194/amt-18-3321-2025, 2025
Short summary
Assimilation of volcanic sulfur dioxide products from IASI and TROPOMI into the chemical transport model MOCAGE: case study of the 2021 La Soufrière Saint Vincent eruption with the March 2022 version of MOCAGE
Mickaël Bacles, Jonathan Améric, and Vincent Guidard
Atmos. Meas. Tech., 18, 2659–2680, https://doi.org/10.5194/amt-18-2659-2025,https://doi.org/10.5194/amt-18-2659-2025, 2025
Short summary

Cited articles

Carlotti, M.: Global-fit approach to the analysis of limb-scanning atmospheric measurements, Appl. Opt., 27, 3250–3254, https://doi.org/10.1364/AO.27.003250, 1988.
Carlotti, M., Brizzi, G., Papandrea, E., Prevedelli, M., Ridolfi, M., Dinelli, B. M., and Magnani, L.: GMTR: Two-dimensional geofit multitarget retrieval model for Michelson Interferometer for Passive Atmospheric Sounding/Environmental Satellite observations, Appl. Opt., 45, 716–727, 2006.
Carlotti, M., Arnone, E., Castelli, E., Dinelli, B. M., and Papandrea, E.: Position error in profiles retrieved from MIPAS observations with a 1-D algorithm, Atmos. Meas. Tech., 6, 419–429, https://doi.org/10.5194/amt-6-419-2013, 2013.
Dudhia, A., Jay, V. L., and Rodgers, C. D.: MIPAS Orbital Retrieval using Sequential Estimation, Earth Observation Data Group, Department of Physics, University of Oxford, available at: http://www.atm.ox.ac.uk/MORSE/ (last access: 14 November 2016), 2005.
Download
Short summary
MIPAS is a satellite-borne limb emission sounder. The algorithm used to infer atmospheric composition from its measurements exploits the assumption that the atmosphere is horizontally homogeneous. This assumption can cause significant errors. We use synthetic observations to quantify these errors. Furthermore we show that the inclusion of any kind of horizontal variability model improves all the retrieval targets and that the two-dimensional approach implies the smallest errors.
Share