02 Jul 2024
 | 02 Jul 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Bias Correction and Application of Labeled Smartphone Pressure Data for Evaluating the Best Track of Landfalling Tropical Cyclones

Ge Qiao, Yuyao Cao, Qinghong Zhang, and Juanzhen Sun

Abstract. Smartphone pressure observations have been demonstrated significant potential as a complement to traditional pressure monitoring. However, challenges remain in correcting biases and further leveraging these observations for practical applications. In this study, we used tropical cyclone (TC) Lekima in 2019, Hagupit in 2020 and IN-FA in 2021 as examples to conduct bias correction on labeled smartphone pressure data from Moji Weather app. We proposed a quality control procedure utilizing random forest machine learning models. By applying this quality control approach to the selected TCs, we discovered that the performance of the method for labeled data significantly surpassed that for unlabeled data developed in a previous study, reducing the mean absolute error from 3.105 hPa to 0.904 hPa. The bias-corrected smartphone data was then supplemented with weather station data for sea-level pressure analyses and compared with the analyses that used only weather station data. The significantly higher spatial resolution and broader coverage of the smartphone data led to notable differences between the two analysis fields. Additionally, we compared the MSLP of TCs derived from smartphone data, weather station obseravtions, and the best track dataset from the Shanghai Typhoon Institute of China Meteorological Administration. We found that the best track published by STI consistently underestimated the minimum sea level pressure, with a median difference of 0.51 hPa in the three TC cases.

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Ge Qiao, Yuyao Cao, Qinghong Zhang, and Juanzhen Sun

Status: open (until 24 Aug 2024)

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Ge Qiao, Yuyao Cao, Qinghong Zhang, and Juanzhen Sun
Ge Qiao, Yuyao Cao, Qinghong Zhang, and Juanzhen Sun


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Short summary
Smartphones equipped with multiple sensors have great potential to form high-resolution meteorological observation fields. In this study, we focused on smartphone pressure observation in tropical cyclone environment. We developed a machine learning-based quality control program that greatly reduced errors and found that smartphone data led to significant improvements in analysis fields. Some traditional best tracks were found to consistently underestimate the minimum sea level pressure.