Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.668 IF 3.668
  • IF 5-year value: 3.707 IF 5-year
    3.707
  • CiteScore value: 6.3 CiteScore
    6.3
  • SNIP value: 1.383 SNIP 1.383
  • IPP value: 3.75 IPP 3.75
  • SJR value: 1.525 SJR 1.525
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 77 Scimago H
    index 77
  • h5-index value: 49 h5-index 49
Preprints
https://doi.org/10.5194/amt-2020-190
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-190
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Jul 2020

27 Jul 2020

Review status
This preprint is currently under review for the journal AMT.

Smartphone Pressure Data: Quality Control and Impact on Atmospheric Analysis

Rumeng Li1, Qinghong Zhang1, Juanzhen Sun2, Yun Chen3, Lili Ding4,5, and Tian Wang4 Rumeng Li et al.
  • 1Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
  • 2National Center for Atmospheric Science, Boulder, Colorado, USA
  • 3National Meteorological Center, Chinese Meteorological Administration, Beijing 100080, China
  • 4Moji Co., Ltd, Beijing, 100015, China
  • 5Theme Tech Inc, Beijing, 100020, China

Abstract. Smartphones are increasingly being equipped with atmospheric measurement sensors, providing huge auxiliary resources for global observations. Although China has the highest number of cellphone users, there is little research on whether these measurements provide useful information for atmospheric research. Here, for the first time, we present the global spatial and temporal variation of smartphone pressure measurements collected in 2016 from the Moji Weather app. The data have an irregular spatiotemporal distribution, with a high density in urban areas, a maximum in summer and two daily peaks corresponding to rush hours. With the dense dataset, we have developed a new bias correction method based on a machine learning approach without requiring users' personal information, which is shown to reduce the bias of pressure observation substantially. The potential application of the high-density smartphone data in cities is illustrated by a case study of a hailstorm occurred in Beijing in which high-resolution gridded pressure analysis is produced. It is shown that the dense smartphone pressure analysis during the storm can provide detailed information about fine-scale convective structure and decrease errors from an analysis based on surface meteorological-station measurements. This study demonstrates the potential value of smartphone data and suggests some future research need for its use in atmospheric science.

Rumeng Li et al.

Interactive discussion

Status: open (until 21 Sep 2020)
Status: open (until 21 Sep 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Rumeng Li et al.

Rumeng Li et al.

Viewed

Total article views: 116 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
91 21 4 116 2 2
  • HTML: 91
  • PDF: 21
  • XML: 4
  • Total: 116
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 27 Jul 2020)
Cumulative views and downloads (calculated since 27 Jul 2020)

Viewed (geographical distribution)

Total article views: 110 (including HTML, PDF, and XML) Thereof 110 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 08 Aug 2020
Publications Copernicus
Download
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 occurred in June, 10, 2016, Beijing, China.
In this paper, we describe a bias correction method based on machine learning without the need...
Citation