Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones
<p>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...
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Copernicus Publications
2025-02-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/18/829/2025/amt-18-829-2025.pdf |
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| author | G. Qiao Y. Cao Q. Zhang J. Sun H. Yu L. Bai |
| author_facet | G. Qiao Y. Cao Q. Zhang J. Sun H. Yu L. Bai |
| author_sort | G. Qiao |
| collection | DOAJ |
| description | <p>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 <span class="inline-formula">hPa</span>. 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 <span class="inline-formula">hPa</span> in the three TC cases.</p> |
| format | Article |
| id | doaj-art-1ff4e893a275433ab2477445c87bb52b |
| institution | OA Journals |
| issn | 1867-1381 1867-8548 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj-art-1ff4e893a275433ab2477445c87bb52b2025-08-20T02:13:51ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482025-02-011882984110.5194/amt-18-829-2025Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclonesG. Qiao0Y. Cao1Q. Zhang2J. Sun3H. Yu4L. Bai5Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, ChinaDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, ChinaDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, ChinaNational Center for Atmospheric Science, Boulder, CO 80307, USAShanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, ChinaShanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China<p>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 <span class="inline-formula">hPa</span>. 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 <span class="inline-formula">hPa</span> in the three TC cases.</p>https://amt.copernicus.org/articles/18/829/2025/amt-18-829-2025.pdf |
| spellingShingle | G. Qiao Y. Cao Q. Zhang J. Sun H. Yu L. Bai Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones Atmospheric Measurement Techniques |
| title | Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones |
| title_full | Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones |
| title_fullStr | Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones |
| title_full_unstemmed | Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones |
| title_short | Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones |
| title_sort | bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones |
| url | https://amt.copernicus.org/articles/18/829/2025/amt-18-829-2025.pdf |
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