Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones

Smartphone pressure observations have demonstrated significant potential to complement traditional pressure monitoring. However, challenges remain in correcting biases and further leveraging these observations for practical applications. In this study, we used tropical cyclones (TCs) Lekima in 2019, Hagupit in 2020 and In-fa in 2021 as examples to conduct bias correction on labeled smartphone pressure data from the Moji Weather app. We propose 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 to 0.904 hPa. The bias-corrected smartphone data were 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 minimum sea-level pressure of TCs derived from smartphone data, weather station observations and the best-track dataset from the Shanghai Typhoon Institute (STI) of the 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.

To Access Resource:

Questions? Email Resource Support Contact:

  • opensky@ucar.edu
    UCAR/NCAR - Library

Resource Type publication
Temporal Range Begin N/A
Temporal Range End N/A
Temporal Resolution N/A
Bounding Box North Lat N/A
Bounding Box South Lat N/A
Bounding Box West Long N/A
Bounding Box East Long N/A
Spatial Representation N/A
Spatial Resolution N/A
Related Links

Related Software #1 : geq-pku/smartphone-pressure: Smartphone pressure reasearch final version

Additional Information N/A
Resource Format PDF
Standardized Resource Format PDF
Asset Size N/A
Legal Constraints

Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Access Constraints None
Software Implementation Language N/A

Resource Support Name N/A
Resource Support Email opensky@ucar.edu
Resource Support Organization UCAR/NCAR - Library
Distributor N/A
Metadata Contact Name N/A
Metadata Contact Email opensky@ucar.edu
Metadata Contact Organization UCAR/NCAR - Library

Author Qiao, G.
Cao, Y.
Zhang, Q.
Sun, Juanzhen
Yu, H.
Bai, L.
Publisher UCAR/NCAR - Library
Publication Date 2025-02-14T00:00:00
Digital Object Identifier (DOI) Not Assigned
Alternate Identifier N/A
Resource Version N/A
Topic Category geoscientificInformation
Progress N/A
Metadata Date 2025-07-10T19:54:25.027300
Metadata Record Identifier edu.ucar.opensky::articles:42863
Metadata Language eng; USA
Suggested Citation Qiao, G., Cao, Y., Zhang, Q., Sun, Juanzhen, Yu, H., Bai, L.. (2025). Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones. UCAR/NCAR - Library. https://n2t.net/ark:/85065/d7891b7c. Accessed 01 August 2025.

Harvest Source