Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors
<p>Mesoscale pressure waves, including atmospheric gravity waves, outflow and frontal passages, and wake lows, are outputs of and can potentially modify clouds and precipitation. The vertical motions associated with these waves can modify the temperature and relative humidity of air parcels an...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-01-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/17/113/2024/amt-17-113-2024.pdf |
| _version_ | 1797359582785306624 |
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| author | L. R. Allen S. E. Yuter S. E. Yuter M. A. Miller L. M. Tomkins |
| author_facet | L. R. Allen S. E. Yuter S. E. Yuter M. A. Miller L. M. Tomkins |
| author_sort | L. R. Allen |
| collection | DOAJ |
| description | <p>Mesoscale pressure waves, including atmospheric gravity waves, outflow and frontal passages, and wake lows, are outputs of and can potentially modify clouds and precipitation. The vertical motions associated with these waves can modify the temperature and relative humidity of air parcels and thus yield potentially irreversible changes to the cloud and precipitation content of those parcels. A wavelet-based method for identifying and tracking these types of wave signals in time series data from networks of low-cost, high-precision (0.8 Pa noise floor, 1 Hz recording frequency) pressure sensors is demonstrated. Strong wavelet signals are identified using a wave-period-dependent (i.e., frequency-dependent) threshold, and then those signals are extracted by inverting the wavelet transform. Wave periods between 1 and 120 min were analyzed – a range which could capture acoustic, acoustic-gravity, and gravity wave modes. After extracting the signals from a network of pressure sensors, the cross-correlation function is used to estimate the time difference between the wave passage at each pressure sensor. From those time differences, the wave phase velocity vector is calculated using a least-squares fit. If the fitting error is sufficiently small (thresholds of RMSE <span class="inline-formula"><</span> 90 s and NRMSE <span class="inline-formula"><</span> 0.1 were used), then a wave event is considered robust and trackable. We present examples of tracked wave events, including a Lamb wave caused by the Hunga Tonga volcanic eruption in January 2020, a gravity wave train, an outflow boundary passage, a frontal passage, and a cold front passage. The data and processing techniques presented here can have research applications in wave climatology and testing associations between waves and atmospheric phenomena.</p> |
| first_indexed | 2024-03-08T15:25:50Z |
| format | Article |
| id | doaj.art-be4a742d1ef8432d84b67ab79938769c |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-03-08T15:25:50Z |
| publishDate | 2024-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-be4a742d1ef8432d84b67ab79938769c2024-01-10T09:56:08ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482024-01-011711313410.5194/amt-17-113-2024Objective identification of pressure wave events from networks of 1 Hz, high-precision sensorsL. R. Allen0S. E. Yuter1S. E. Yuter2M. A. Miller3L. M. Tomkins4Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, 27695, USACenter for Geospatial Analytics, North Carolina State University, Raleigh, NC, 27695, USADepartment of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC, 27695, USADepartment of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC, 27695, USACenter for Geospatial Analytics, North Carolina State University, Raleigh, NC, 27695, USA<p>Mesoscale pressure waves, including atmospheric gravity waves, outflow and frontal passages, and wake lows, are outputs of and can potentially modify clouds and precipitation. The vertical motions associated with these waves can modify the temperature and relative humidity of air parcels and thus yield potentially irreversible changes to the cloud and precipitation content of those parcels. A wavelet-based method for identifying and tracking these types of wave signals in time series data from networks of low-cost, high-precision (0.8 Pa noise floor, 1 Hz recording frequency) pressure sensors is demonstrated. Strong wavelet signals are identified using a wave-period-dependent (i.e., frequency-dependent) threshold, and then those signals are extracted by inverting the wavelet transform. Wave periods between 1 and 120 min were analyzed – a range which could capture acoustic, acoustic-gravity, and gravity wave modes. After extracting the signals from a network of pressure sensors, the cross-correlation function is used to estimate the time difference between the wave passage at each pressure sensor. From those time differences, the wave phase velocity vector is calculated using a least-squares fit. If the fitting error is sufficiently small (thresholds of RMSE <span class="inline-formula"><</span> 90 s and NRMSE <span class="inline-formula"><</span> 0.1 were used), then a wave event is considered robust and trackable. We present examples of tracked wave events, including a Lamb wave caused by the Hunga Tonga volcanic eruption in January 2020, a gravity wave train, an outflow boundary passage, a frontal passage, and a cold front passage. The data and processing techniques presented here can have research applications in wave climatology and testing associations between waves and atmospheric phenomena.</p>https://amt.copernicus.org/articles/17/113/2024/amt-17-113-2024.pdf |
| spellingShingle | L. R. Allen S. E. Yuter S. E. Yuter M. A. Miller L. M. Tomkins Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors Atmospheric Measurement Techniques |
| title | Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors |
| title_full | Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors |
| title_fullStr | Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors |
| title_full_unstemmed | Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors |
| title_short | Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors |
| title_sort | objective identification of pressure wave events from networks of 1 thinsp hz high precision sensors |
| url | https://amt.copernicus.org/articles/17/113/2024/amt-17-113-2024.pdf |
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