Articles | Volume 11, issue 3
https://doi.org/10.5194/amt-11-1529-2018
https://doi.org/10.5194/amt-11-1529-2018
Research article
 | 
19 Mar 2018
Research article |  | 19 Mar 2018

Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

Antti Lipponen, Tero Mielonen, Mikko R. A. Pitkänen, Robert C. Levy, Virginia R. Sawyer, Sami Romakkaniemi, Ville Kolehmainen, and Antti Arola

Related authors

HAPI2LIBIS (v1.0): A new tool for flexible high resolution radiative transfer computations with libRadtran (version 2.0.5)
Antti Kukkurainen, Antti Mikkonen, Antti Arola, Antti Lipponen, Ville Kolehmainen, and Neus Sabater
EGUsphere, https://doi.org/10.5194/egusphere-2025-220,https://doi.org/10.5194/egusphere-2025-220, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
A global perspective on CO2 satellite observations in high AOD conditions
Timo H. Virtanen, Anu-Maija Sundström, Elli Suhonen, Antti Lipponen, Antti Arola, Christopher O'Dell, Robert R. Nelson, and Hannakaisa Lindqvist
Atmos. Meas. Tech., 18, 929–952, https://doi.org/10.5194/amt-18-929-2025,https://doi.org/10.5194/amt-18-929-2025, 2025
Short summary
Machine learning data fusion for high spatio-temporal resolution PM2.5
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
EGUsphere, https://doi.org/10.5194/egusphere-2024-4056,https://doi.org/10.5194/egusphere-2024-4056, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Model analysis of biases in the satellite-diagnosed aerosol effect on the cloud liquid water path
Harri Kokkola, Juha Tonttila, Silvia M. Calderón, Sami Romakkaniemi, Antti Lipponen, Aapo Peräkorpi, Tero Mielonen, Edward Gryspeerdt, Timo Henrik Virtanen, Pekka Kolmonen, and Antti Arola
Atmos. Chem. Phys., 25, 1533–1543, https://doi.org/10.5194/acp-25-1533-2025,https://doi.org/10.5194/acp-25-1533-2025, 2025
Short summary
Post-process correction improves the accuracy of satellite PM2.5 retrievals
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
Atmos. Meas. Tech., 17, 5747–5764, https://doi.org/10.5194/amt-17-5747-2024,https://doi.org/10.5194/amt-17-5747-2024, 2024
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Multi-layer retrieval of aerosol optical depth in the troposphere using SEVIRI data: a case study of the European continent
Maryam Pashayi, Mehran Satari, and Mehdi Momeni Shahraki
Atmos. Meas. Tech., 18, 1415–1439, https://doi.org/10.5194/amt-18-1415-2025,https://doi.org/10.5194/amt-18-1415-2025, 2025
Short summary
Star photometry with all-sky cameras to retrieve aerosol optical depth at night-time
Roberto Román, Daniel González-Fernández, Juan Carlos Antuña-Sánchez, Celia Herrero del Barrio, Sara Herrero-Anta, África Barreto, Victoria E. Cachorro, Lionel Doppler, Ramiro González, Christoph Ritter, David Mateos, Natalia Kouremeti, Gustavo Copes, Abel Calle, María José Granados-Muñoz, Carlos Toledano, and Ángel M. de Frutos
EGUsphere, https://doi.org/10.5194/egusphere-2025-667,https://doi.org/10.5194/egusphere-2025-667, 2025
Short summary
Ground-based contrail observations: comparisons with reanalysis weather data and contrail model simulations
Jade Low, Roger Teoh, Joel Ponsonby, Edward Gryspeerdt, Marc Shapiro, and Marc E. J. Stettler
Atmos. Meas. Tech., 18, 37–56, https://doi.org/10.5194/amt-18-37-2025,https://doi.org/10.5194/amt-18-37-2025, 2025
Short summary
Improvements in aerosol layer height retrievals from TROPOMI oxygen A-band measurements by surface albedo fitting in optimal estimation
Martin de Graaf, Maarten Sneep, Mark ter Linden, L. Gijsbert Tilstra, and J. Pepijn Veefkind
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-198,https://doi.org/10.5194/amt-2024-198, 2025
Revised manuscript accepted for AMT
Short summary
Satellite Aerosol Composition Retrieval from a combination of three different Instruments: Information content analysis
Ulrike Stöffelmair, Thomas Popp, Marco Vountas, and Hartmut Bösch
EGUsphere, https://doi.org/10.5194/egusphere-2024-2800,https://doi.org/10.5194/egusphere-2024-2800, 2024
Short summary

Cited articles

Anderson, T. L., Charlson, R. J., Winker, D. M., Ogren, J. A., and Holmén, K.: Mesoscale variations of tropospheric aerosols, J. Atmos. Sci., 60, 119–136, 2003. a, b
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A limited memory algorithm for bound constrained optimization, SIAM J. Sci. Comput., 16, 1190–1208, 1995. a
Calvetti, D. and Somersalo, E.: An Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing, vol. 2, Springer Science & Business Media, 2007. a
Chandrasekhar, S.: Radiative transfer, Dover, New York, 1960. a
Chatterjee, A., Michalak, A., Kahn, R., Paradise, S., Braverman, A., and Miller, C.: A geostatistical data fusion technique for merging remote sensing and ground-based observations of aerosol optical thickness, J. Geophys. Res.-Atmos., 115, D20207, https://doi.org/10.1029/2009JD013765, 2010. a
Download
Short summary
Atmospheric aerosols are small solid or liquid particles suspended in the atmosphere and they have a significant effect on the climate. Satellite data are used to get global estimates of atmospheric aerosols. In this work, a statistics-based Bayesian aerosol retrieval algorithm was developed to improve the accuracy and quantify the uncertainties related to the aerosol estimates. The algorithm is tested with NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data.
Share