Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

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

doi:https://doi.org/10.5194/amt-11-1529-2018

Articles | Volume 11, issue 3

https://doi.org/10.5194/amt-11-1529-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-11-1529-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 3

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

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Final revised paper (published on 19 Mar 2018)
Supplement to the final revised paper
Preprint (discussion started on 01 Nov 2017)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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SC1: 'Very interesting paper! Some questions/comments.', Andrew Sayer, 03 Nov 2017
- AC1: 'Reply to Dr Sayer', A. Lipponen, 23 Jan 2018
RC1: 'Review of `Bayesian Dark Target Algorithm for MODIS AOD retrieval over land'', Adam Povey, 15 Nov 2017
- AC2: 'Reply to Dr Povey', A. Lipponen, 23 Jan 2018
RC2: 'Review of "Bayesian Dark Target Algorithm for MODIS AOD retrieval over land", Lipponen et al.', Anonymous Referee #3, 22 Nov 2017
- AC3: 'Reply to Anonymous Referee #3', A. Lipponen, 23 Jan 2018
RC3: 'Review', Anonymous Referee #2, 29 Nov 2017
- AC4: 'Reply to Anonymous Referee #2', A. Lipponen, 23 Jan 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Antti Lipponen on behalf of the Authors (13 Feb 2018) Author's response Manuscript

ED: Publish as is (13 Feb 2018) by Alexander Kokhanovsky

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.