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 cell phone 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 in 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 that 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 needs for their use in atmospheric science.
Highlights
A lack of high-resolution observational data is one of the obstacles that limits the advance of numerical weather prediction (Bauer et al, 2015)
The overall distribution pattern of pressure perturbation in SFC + smartphone experiment (SP) is consistent with the conceptual model of Markowski and Richardson (2010), but the current analysis reveals that the surface high-pressure region and low-level divergence center slightly lag behind the center of the intense reflectivity echoes rather than right beneath it, as in their conceptual model
This study focused on smartphone pressure data acquired from the Moji Weather app in 2016 and showed their characteristics for the first time
Summary
A lack of high-resolution observational data is one of the obstacles that limits the advance of numerical weather prediction (Bauer et al, 2015) This limitation can be extended to all areas in atmospheric research. We present, for the first time, a year-long dense and extensive smartphone dataset collected by the Moji Weather app, Atmos. We use the Moji smartphone pressure data for all of 2016 to show the spatial and temporal distribution of the dataset. With this highly dense network, we demonstrate the feasibility of a new machinelearning bias-correction method that does not require users’ private information, thereby ameliorating ethical issues.
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