Articles | Volume 14, issue 2
Atmos. Meas. Tech., 14, 785–801, 2021
https://doi.org/10.5194/amt-14-785-2021
Atmos. Meas. Tech., 14, 785–801, 2021
https://doi.org/10.5194/amt-14-785-2021

Research article 02 Feb 2021

Research article | 02 Feb 2021

Smartphone pressure data: quality control and impact on atmospheric analysis

Rumeng Li et al.

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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
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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.