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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

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
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.