Smartphone pressure data: quality control and impact on atmospheric analysis

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

Atmospheric measurement techniques · 02 February 2021 · China Meteorological Administration (CMA), NSF NCAR / UCAR

In one sentence. Machine-learning bias correction of 2016 smartphone pressure data reduces bias, and dense analysis resolves convective structure in a Beijing hailstorm better than station data.

Abstract

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.

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