Smartphone pressure data: quality control and impact on atmospheric analysis

Li, Rumeng; Zhang, Qinghong; Sun, Juanzhen; Chen, Yun; Ding, Lili; Wang, Tian

doi:https://doi.org/10.5194/amt-14-785-2021

Articles | Volume 14, issue 2

https://doi.org/10.5194/amt-14-785-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-14-785-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 2

Research article

|

02 Feb 2021

Research article |

| 02 Feb 2021

Smartphone pressure data: quality control and impact on atmospheric analysis

Rumeng Li, Qinghong Zhang, Juanzhen Sun, Yun Chen, Lili Ding, and Tian Wang

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Final revised paper (published on 02 Feb 2021)
Preprint (discussion started on 27 Jul 2020)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review if Li et al paper on Smartphone pressure sensors', Colin Price, 13 Aug 2020
- AC1: 'Response to reviewers', Rumeng Li, 23 Nov 2020
RC2: 'Review for AMT-2020-190', Anonymous Referee #3, 03 Nov 2020
- AC1: 'Response to reviewers', Rumeng Li, 23 Nov 2020

Peer-review completion

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

AR by Rumeng Li on behalf of the Authors (23 Nov 2020) Author's response Manuscript

ED: Publish as is (10 Dec 2020) by Laura Bianco

AR by Rumeng Li on behalf of the Authors (14 Dec 2020)

Short summary

In this paper, we describe a bias-correction method based on machine learning without the need to obtain users' personal information and demonstrate that the method can effectively reduce the bias in smartphone pressure observations. The characteristics of this dataset are discussed, and the potential application of the bias-corrected data is illustrated by the fine-scale analysis of a hailstorm that occurred on 10 June 2016 in Beijing, China.