Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

Nair, Arshad Arjunan; Yu, Fangqun

doi:https://doi.org/10.5194/acp-20-12853-2020

Articles | Volume 20, issue 21

https://doi.org/10.5194/acp-20-12853-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-20-12853-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 21

Research article

|

05 Nov 2020

Research article |

| 05 Nov 2020

Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

Arshad Arjunan Nair and Fangqun Yu

Download

Final revised paper (published on 05 Nov 2020)
Preprint (discussion started on 10 Jun 2020)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC1: 'Comments', Anonymous Referee #1, 28 Jul 2020
- AC1: 'ACP-2020-509: Authors' response to Anonymous Referee #1', Arshad Nair, 23 Aug 2020
RC2: 'Review for Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements', Anonymous Referee #2, 03 Aug 2020
- AC2: 'Authors' response to comments by Anonymous Referee #2', Arshad Nair, 23 Aug 2020

Peer-review completion

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

AR by Arshad Nair on behalf of the Authors (23 Aug 2020) Author's response Manuscript

ED: Publish as is (30 Sep 2020) by Gordon McFiggans

AR by Arshad Nair on behalf of the Authors (30 Sep 2020)

Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Article (7023 KB)
Full-text XML

Short summary

Small particles in the atmosphere can affect cloud formation and properties and thus Earth's energy budget. These cloud condensation nuclei (CCN) contribute the largest uncertainties in climate change modeling. To reduce these uncertainties, it is important to quantify CCN numbers accurately, measurements of which are sparse. We propose and evaluate a machine learning method to estimate CCN, in the absence of their direct measurements, using more common measurements of weather and air quality.