5:15 PM - 6:45 PM
[MTT38-P03] Characteristics of Surface Boundary Layer Disturbances observed by ultra-multipoint narrow network of micro-barometers
Keywords:micro-pressure fluctuation, surface boundary layer, turbulence, wind measurement
1. Background
Imada and Nakajima (2022) developed an inexpensive micro-pressure observation system that can be installed in large numbers with tsunami early warning in mind.[1] The system measures atmospheric pressure every 35 ms with an accuracy of about 0.5 Pa, and may be able to detect fluctuations caused by thunders, turbulence, etc. Hiramine et al. (2023) made 100 of such micro-barometers and conducted an observation in a small area in the field expecting to capture fluctuations associated with surface boundary layer disturbances.[2] Here we will report the results of the analysis of the observation data.
2. Micro-pressure multi-point observation system
Each micro-pressure gauge consists of a capacitive MEMS pressure sensor, DPS310, a microcomputer, M5Stack ATOM Lite, and dry-cell batteries. Data was collected on a small Debian GNU/Linux PC via a Wi-Fi-connected UDP.
3. Field observation
The observation was performed at a flat square in Kyushu University from 15:12 to 17:20 on April 8, 2023. Micro-barometers were placed at 10-times-10 grid points with about 0.3 m intervals. The observation area is square, but one diagonal is in the north-south direction. An ultrasonic anemometer, ULSA BASIC, was placed at about 2.8 m from the observation area to measure the wind at about 1.5 m above the ground. The data obtained continuously at 35 ms intervals from 16:10 to 16:44 are analyzed after linear interpolation to 5 ms intervals and bias-correction assuming that the 100-second moving averages were aligned.
4. Results
4.1 Structure of Disturbances
First, to capture the qualitative characteristics of the atmospheric pressure fluctuations, we made a movie of the time variation of the observed horizontal distribution of atmospheric pressure. We can recognize both isolated vortex-like and wave-like disturbances. The movement of the disturbances generally corresponded to the wind direction, but the speed was about half of the wind speed at the corresponding time.
4.2 Tracking of isolated vortex-like disturbances
For clearly identifiable isolated vortex-like disturbances, the positions of the center were estimated by approximating the pressure distribution near the center with an elliptic paraboloid, [3][4] and the velocity of movement was calculated. When the disturbance was well inside the observation area, the calculated velocity of movement was consistent with that seen in the movie. We plan to further investigate more of traceable disturbances in the future.
4.3 Spatiotemporal Spectrum
The figure shows the results of performing a spatiotemporal Fourier transform, rotating the horizontal wavenumber axis to east-west and north-south, and integrating the results for the east-west wavenumber. The power is larger at lower frequencies, and the larger portion of the power is biased toward the northward motion. Compared with the straight line corresponding to the period mean wind speed (2.6 m/s) in the figure, the power is enhanced along the line. This means that the movement of the disturbance corresponds to the wind speed, which contradicts the results of 4.1 and 4.2. The reasons will be examined in the future.
Reference
[1] E. Imada and K. Nakajima, "Development of a micro-pressure observation system for the observation of atmospheric Lamb waves excited with the motion of the ground," MTT45-P04, Japan Geoscience Union Annual Meeting 2022.
[2] T. Hiramine, E. Imada and K. Nakajima, "An attempt of super multi-point intensive observation of micro-pressure fluctuation," MTT37-P03, Japan Geoscience Union Annual Meeting 2023.
[3] M. Hayashi and K. Shimoji, "Atmospheric Tracking Wind Calculation Algorithm," Weather Satellite Center Technical Report, No. 58, February 2013.
[4] T. Hamada, "3. Wind Calculation," Weather Satellite Center Technical Report (Special Issue II-2), GMS System General Report, p. 15-42.
Acknowledgements
This research was supported by a grant from JSPS Scientific Research Grants 22K18872.
Imada and Nakajima (2022) developed an inexpensive micro-pressure observation system that can be installed in large numbers with tsunami early warning in mind.[1] The system measures atmospheric pressure every 35 ms with an accuracy of about 0.5 Pa, and may be able to detect fluctuations caused by thunders, turbulence, etc. Hiramine et al. (2023) made 100 of such micro-barometers and conducted an observation in a small area in the field expecting to capture fluctuations associated with surface boundary layer disturbances.[2] Here we will report the results of the analysis of the observation data.
2. Micro-pressure multi-point observation system
Each micro-pressure gauge consists of a capacitive MEMS pressure sensor, DPS310, a microcomputer, M5Stack ATOM Lite, and dry-cell batteries. Data was collected on a small Debian GNU/Linux PC via a Wi-Fi-connected UDP.
3. Field observation
The observation was performed at a flat square in Kyushu University from 15:12 to 17:20 on April 8, 2023. Micro-barometers were placed at 10-times-10 grid points with about 0.3 m intervals. The observation area is square, but one diagonal is in the north-south direction. An ultrasonic anemometer, ULSA BASIC, was placed at about 2.8 m from the observation area to measure the wind at about 1.5 m above the ground. The data obtained continuously at 35 ms intervals from 16:10 to 16:44 are analyzed after linear interpolation to 5 ms intervals and bias-correction assuming that the 100-second moving averages were aligned.
4. Results
4.1 Structure of Disturbances
First, to capture the qualitative characteristics of the atmospheric pressure fluctuations, we made a movie of the time variation of the observed horizontal distribution of atmospheric pressure. We can recognize both isolated vortex-like and wave-like disturbances. The movement of the disturbances generally corresponded to the wind direction, but the speed was about half of the wind speed at the corresponding time.
4.2 Tracking of isolated vortex-like disturbances
For clearly identifiable isolated vortex-like disturbances, the positions of the center were estimated by approximating the pressure distribution near the center with an elliptic paraboloid, [3][4] and the velocity of movement was calculated. When the disturbance was well inside the observation area, the calculated velocity of movement was consistent with that seen in the movie. We plan to further investigate more of traceable disturbances in the future.
4.3 Spatiotemporal Spectrum
The figure shows the results of performing a spatiotemporal Fourier transform, rotating the horizontal wavenumber axis to east-west and north-south, and integrating the results for the east-west wavenumber. The power is larger at lower frequencies, and the larger portion of the power is biased toward the northward motion. Compared with the straight line corresponding to the period mean wind speed (2.6 m/s) in the figure, the power is enhanced along the line. This means that the movement of the disturbance corresponds to the wind speed, which contradicts the results of 4.1 and 4.2. The reasons will be examined in the future.
Reference
[1] E. Imada and K. Nakajima, "Development of a micro-pressure observation system for the observation of atmospheric Lamb waves excited with the motion of the ground," MTT45-P04, Japan Geoscience Union Annual Meeting 2022.
[2] T. Hiramine, E. Imada and K. Nakajima, "An attempt of super multi-point intensive observation of micro-pressure fluctuation," MTT37-P03, Japan Geoscience Union Annual Meeting 2023.
[3] M. Hayashi and K. Shimoji, "Atmospheric Tracking Wind Calculation Algorithm," Weather Satellite Center Technical Report, No. 58, February 2013.
[4] T. Hamada, "3. Wind Calculation," Weather Satellite Center Technical Report (Special Issue II-2), GMS System General Report, p. 15-42.
Acknowledgements
This research was supported by a grant from JSPS Scientific Research Grants 22K18872.