- Email: [email protected]
Ocean ambient noise off Chennai due to very severe cyclonic storm Vardah
Applied Acoustics 140 (2018) 256–262
Contents lists available at ScienceDirect
Applied Acoustics journal homepage: www.elsevier.com/locate/apacoust
Ocean ambient noise off Chennai due to very severe cyclonic storm Vardah ⁎
T
M. Ashokan , G. Latha, G. Raguraman All at National Institute of Ocean Technology, Pallikaranai, Chennai 600 100, India
A B S T R A C T
Ocean ambient noise during the passage of a very severe cyclonic storm, Vardah, in the Bay of Bengal has been studied and presented in this paper. A cyclone named Vardah formed near the Andaman and Nicobar Islands on December 6, 2016. The intensified cyclonic storm crossed the south-eastern coast of India in the afternoon hours (local time) of December 12, 2016. During the passage of this cyclone, ocean underwater ambient noise time series data were acquired from the shallow waters of the south-eastern coast of India. In this paper, the noises pertaining to heavy rain and light rain (drizzling) during the cyclonic event have been analysed along with the high wind noise, and the individual power spectra for both the noises have been estimated.
1. Introduction Noise measurements in the shallow waters of Indian seas have attracted a lot of attention, and ambient noise studies have been conducted at various locations in the last few years. Due to the waveguide nature of shallow waters and shipping activities, the acquisition and analysis of ambient noise have become difficult. However, the study of these sounds, using acoustic instruments, such as SONAR, Pinger, etc., is very essential for oceanographers. The predominant sources that cause higher noise levels in the underwater sound field are wind and rain. During a cyclone or storm event, noises pertaining to wind and rain would be much higher. Rain noise depends on the drop size and the resonance frequency. Smaller rain drops (light rain) produce sounds at frequencies greater than 14 kHz, while larger rain drops (heavy rain) produce sounds at frequencies lesser than 10 kHz. During a cyclonic event, rain noise is always dominated by heavy wind noise. Nystuen et al. [6] described that heavy rainfall and light rainfall create different acoustic frequencies: light drizzle creates frequencies in the band of 10–16 kHz while heavy rainfall with large raindrops creates noise in the band < 10 kHz. It is clearly understood that the frequency of sounds produced by heavy rainfall is inversely proportional to the drop size. Measurements of underwater sounds created by rain were acquired at three U.S. seaside locations during convective events by Black et al. [3] and high association was found between sound spectrum levels in the 4–10 kHz frequency band. Ambient noise levels during the cyclonic events in the South China Sea were examined by Wei et al. [11] in the course of the Asian Seas International Acoustics Experiment (ASIAEX) South China Research. Barry et al. [2] estimated different ambient sound spectra for different rainfall rates and wind speeds
⁎
through a sequence of discrimination methods. The critical underwater ambient noise power pertaining to the storm was estimated by Wilson and Makris [12]. Measurements of ambient noise in the Gulf of Mexico were carried out by Newcomb et al. [5] during Cyclone Lili and Tropical Hurricane Isidore. Ambient noise measurements containing the storm passage noise were acquired in the Gulf of Mexico by Orlin and Snyder [7]. Measured ambient noise levels for the New Jersey shelf during the storm associated with deep water measurements were demonstrated with waveguide properties by Knobles et al. [4]. While ambient noise features for sea states lesser than 4 have been widely studied in the shallow Bay of Bengal [8], noise features for sea states larger than 4 and also during extreme meteorological conditions have not been analysed extensively in the shallow waters of Indian seas. Studies pertaining to wind/wave noise in various shallow water locations of Indian seas have been carried out by the National Institute of Ocean Technology (NIOT). Analyses of ambient noise levels resulting from very severe cyclonic events have been reported by Ashokan et al. [1], utilising the data from the ocean underwater ambient noise measurement system along with the wind and rain sensors attached to a moored buoy. In this paper, noise datasets pertaining to rain events have been separated out from the time series and power spectral density estimation has been carried out. The time series data of wind speed have been investigated along with the wind noise spectrum. The rainfall events have been studied in connection with predominant wind data. Noise during the passage of Cyclone Vardah has been examined by studying the spectrum. Cyclone Vardah intensified into a severe cyclonic storm on December 9, 2016. The track of the cyclone and the location of underwater ambient noise measurement are shown in Fig. 1. The cyclone
Corresponding author. E-mail addresses: [email protected] (M. Ashokan), [email protected] (G. Latha), [email protected] (G. Raguraman).
https://doi.org/10.1016/j.apacoust.2018.06.008 Received 30 October 2017; Received in revised form 4 June 2018; Accepted 11 June 2018 0003-682X/ © 2018 Elsevier Ltd. All rights reserved.
Applied Acoustics 140 (2018) 256–262
M. Ashokan et al.
2. Materials, methods and instrumentation setup 18 N
18 N
16 N
16 N
14 N
14 N
12 N
12 N
10 N
Continuous passive acoustic time series measurements including noise resulting from Cyclone Vardah have been acquired from the shallow waters of the eastern coast of India during the period, November 30, 2016 to December 15, 2016, by using the autonomous ocean underwater ambient noise measuring system, indigenously developed by NIOT (Table 1). The system consists of a 21-element Vertical Linear Array (VLA) of omni-directional hydrophones with related data acquisition components and battery container as an enclosure, along with subsea floats and surface marker buoy in the mooring link. Hydrophones were positioned at a depth of 8 m, while the ocean depth is 17 m. The total length of the 21-element VLA is 1.5 m, and the element spacing is 0.075 m. The data from VLA’s first hydrophone positioned at a depth of 7.25 m were used for this analysis. The middle hydrophone in the 21-element VLA was positioned at a depth of 8 m. The last hydrophone in the 21-element VLA was positioned at a depth of 8.75 m. As per the experiment protocol of NIOT, the instruments were tested and calibrated at the Underwater Acoustic Test Facility of NIOT, which is the only research laboratory in India to be accredited by the National Accreditation Board for Testing and Calibration Laboratories (NABL). The system has the ability of sampling 21 channels simultaneously, with the sampling rate of 100 kHz per channel at a duration of 60 s every 3 h. The receiving sensitivity of the hydrophone is −168 dB rel 1 V μPa−1. Power spectral density of noise is calculated using Welch’s averaged modified periodogram technique. The data are split into smaller segments, with a Hamming window with a 50% overlap and Fast Fourier Transform accomplished. Wind speed data during the cyclone period have been obtained from a moored buoy deployed by Ocean Observation System Programme of NIOT, positioned very near to the ambient noise system location. The distance between the moored buoy and the ambient noise measurement system is 500 m. The extreme wind speed measured by the buoy was 26 m/s during the cyclone landfall.
10 N Ambient noise measurement system
74 E 76 E 78 E
8N
80 E 82 E 84 E 86 E 88 E
Fig. 1. Vardah cyclone path.
Table 1 Data collection period. S. No.
Experiment period
Number of days
Number of data sets
1.
30 Nov 2016 to 15 Dec 2016
15
120
Table 2 Selected noise data sets to estimate PSD during Vardah cyclone. Date and time of acquisition of noise data sets (UTC format – Date Month Year, HH:MM)
Wind speed (m/s)
Precipitation (mm)
12 12 12 12 12 12 12
16 23 18 23 15 09 04
143.09 143.09 143.09 143.09 99.85 48.62 48.62
Dec Dec Dec Dec Dec Dec Dec
2016, 2016, 2016, 2016, 2016, 2016, 2016,
02:30 Hrs 0530 Hrs 0830 Hrs 1130 Hrs 1430 Hrs 1730 Hrs 2030 Hrs
3. Results and discussion Noise data sets that are selected for estimating the Power Spectral Density (PSD) during the cyclone period have been shown in the Table 2. A total of seven records have been considered with a time interval of 3 h. Fig. 2 shows the Spectrogram of Vardah cyclone data on Dec 12, 2016. During the cyclone period, a typical increase of 5–10 dB in the lower band and 2–3 dB in the higher band of frequencies is observed. The measurements of wind speed and rainfall events during Cyclone Vardah are shown in Fig. 3. Fig. 4a shows the noise spectrum for different wind speeds during the entire day of the cyclone crossing
track and landfall time have been provided by the Indian Metrological Department (IMD), Chennai. The noise measurement system was positioned very close to the cyclone track. The cyclone dissipated on December 13, 2016. The rain noise spectrum during Cyclone Vardah has been analysed from the time series measurements.
Vardah Cyclone
Fig. 2. Spectrogram of Vardah cyclone data on Dec 12, 2016. 257
Noise spectrum level (dB re 1μPa2/Hz)
8N
Applied Acoustics 140 (2018) 256–262
M. Ashokan et al.
Fig. 3. Wind speed events and Rainfall events.
Fig. 4a. Power spectra of heavy wind noise on Dec 12, 2016 for varying wind speeds.
Fig. 4b. 1/3 octave frequency band sound pressure levels for different wind speeds.
258
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Fig. 5. Power spectra of heavy rain and light rain on Dec 12, 2016.
Fig. 6. Sound spectra for convective rain and drizzling.
Fig. 7. Noise level at different frequencies for varying wind speeds.
and landfall. It is clearly observed from the power spectrum (Fig. 4a) that the noise levels vary from 80 dB to 102 dB in the band less than 1 kHz. The highest noise level has been detected in this frequency band at the wind speed of 23 m/s, i.e., during the cyclone landfall. It is clearly seen that high wind speed creates noise much higher than the heavy rain noise. Hence, wind noise is the controlling feature during very severe cyclonic events. The frequency spectra of the cyclone noise in the range less than 5 kHz are a result of the wind speed. Fig. 4b shows the one-third octave frequency band sound pressure levels for the wind speeds 23 m/s, 18 m/s, 16 m/s, 15 m/s, 09 m/s and 04 m/s for varying centre frequencies (plotted in logarithmic scale). It is clearly seen that the increase in wind speeds leads to the increase in noise levels. The power spectrum of noise pertaining to rainfall on December 12, 2016 is shown in Fig. 5. It has been observed from the power spectrum (Fig. 5) that the peak noise level due to heavy rain in the band 1–2 kHz has
reached the maximum at the time of Cyclone Vardah’s landfall, i.e., at 0530 h (UTC). The peak noise level pertaining to light rain (drizzling) has been detected after the cyclone landfall, i.e., at 0830 h (UTC). It is clearly seen from Fig. 5 that the heavy rainfall noise dominates in the frequency band less than 2 kHz (zoomed portion) during the cyclone landfall and light rain dominates in the frequency band higher than 10 kHz (zoomed portion) after the cyclone landfall. As in the case of convective storm systems, the presence of updrafts may often keep the raindrop aloft and allow the drops to grow in size, and as the drop size increases, the frequencies shift towards the lower band. Also, in convective rain events, the main section of the total rainfall is due to large raindrops [9]. Hence, the spectrum in Fig. 5 is primarily created by tremendously large raindrops. At the time of the cyclone, there was nil occurrence of anthropogenic noise and man-made noise. The spectrogram of Cyclone Vardah’s noise for the entire day during the cyclone
259
Applied Acoustics 140 (2018) 256–262
M. Ashokan et al.
Fig. 8a. Time series of heavy rainfall noise.
crossing and landfall, i.e., on December 12, 2016 is clearly shown in Fig. 6. The sound spectra for convective rain (heavy rain) and drizzling are shown in Fig. 6 which are similar to that described in Sara [10]. The noise spectra for different wind speeds are shown in Fig. 7. It is observed that the noise level increases with the increase in wind speed and decreases with the increase in frequency. In order to estimate rainfall from the acoustic measurements, the rain drop size needs to be determined and this can be achieved by analysing the time span of the rainfall drop. Butterworth filter for frequencies less than 10 kHz has been applied to the time series data pertaining to heavy rain (Fig. 8a). Fig. 8b shows the zoomed portion of a single heavy rainfall drop occurring at the 30th second. It is clearly understood from Fig. 8b that the lifespan of the large drop is 1 ms pertaining to heavy rainfall noise and the corresponding frequency falls at 7 kHz (Fig. 8c). To find the lifespan of the drizzling rainfall drop, Butterworth filter for frequencies higher than 10 kHz has been applied to the time series data (Fig. 9a). Fig. 9b shows the zoomed portion of a single drizzling rainfall drop occurring at the 20th second. It is clearly understood from Fig. 9b that the lifespan of the small drop is < 1 ms pertaining to the drizzling rainfall noise and the corresponding frequency falls at 14 kHz (Fig. 9c). Fig. 8b. Chopped portion of heavy rainfall drop.
Fig. 8c. Power spectrum of chopped portion of heavy rainfall drop.
260
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M. Ashokan et al.
Fig. 9a. Time series of light rainfall noise.
4. Conclusion Ocean ambient noise resulting from cyclonic winds and rainfall during the passage of Cyclone Vardah has been examined in the shallow waters off Chennai in the Bay of Bengal. Throughout the cyclone period, ambient noise levels were found to have increased by 5–10 dB from normal values in the frequency band less than 5 kHz and by 2–3 dB in the band higher than 10 kHz. It is inferred that while both rain and wind were present, the wind noise played a major role during this cyclonic event.
Acknowledgements The authors acknowledge the encouragement given by the Director of the National Institute of Ocean Technology to carry out this research work. The authors also thank the field support given by Mr. A. Thirunavukkarasu, Mr. C. Dhanaraj, Mr. P. Edwards Durai and Mr. K. Nithyanandam for measuring the ocean ambient noise. The support from Mrs. Malarkodi for testing and calibration of the hydrophone array is gratefully acknowledged. The authors thank the India Meteorological Department, Chennai, for providing the cyclone tracks and satellite and radar imageries.
Fig. 9b. Chopped portion of light rainfall drop.
Fig. 9c. Power spectrum of chopped portion of light rainfall drop.
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Applied Acoustics 140 (2018) 256–262
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References
[7] P. Orlin, M. Snyder, An Heuristic Ocean Ambient Noise Classifier, Pacific Rim Underwater Acoustics Conference Proceedings, Canada, 2007. [8] Ramji S, Latha G, Rajendran V, Ramakrishnan S. Wind dependence of ambient noise in shallow water of Bay of Bengal. Appl Acoust 2008;69(12):1294–8. [9] Sanjana MC, Latha G, Thirunavukkarasu A. Ambient noise during rough weather and cyclones in the shallow Bay of Bengal. Chin J Oceanol Limnol 2014;32(4):921–32. [10] Pensieri Sara, Bozzano Roberto, Nystuen Jeffrey A, Anagnostou Emmanouil N, Anagnostou Marios N, Bechini Renzo. Underwater Acoustic Measurements to Estimate Wind and Rainfall in the Mediterranean Sea. Advances in Meteorology 2015:1–18. [11] Wei RC, Chen CF, Newhall AE. A preliminary examination of the low-frequency ambient noise field in the South China Sea during the 2001 ASIAEX experiment. IEEE J Oceanic Eng 2004;29(4):1308–15. [12] Wilson JD, Makris NC. Ocean acoustic hurricane classification. J Acoust Soc Am 2006;119(1):168–81.
[1] Ashokan M, Latha G, Ramesh R. Analysis of rain noise in shallow waters of Bay of Bengal during cyclonic storm JAL. Indian J Geo-Marine Sci 2015;44(6):795–9. [2] Ma Barry B, Nystuen Jeffrey A, Lien R-Chieh. Prediction of underwater sound levels from rain and wind. J Acoust Soc Am 2005;117:3555–65. [3] Black PG, Proni JR, Wilkerson JC, Samsbury CE. Oceanic Rainfall Detection and Classification in Tropical and Subtropical Mesoscale Convective Systems Using Underwater Acoustic Methods. Mon Weather Rev 1997;125(9):2014–42. [4] Knobles DP, Joshi SM, Gaul RD, Graber HC, Williams NJ. Analysis of wind-driven ambient noise in a shallow water environment with a sandy seabed. J Acoust Soc Am Express Lett 2008;124(3):157–62. [5] Newcomb, JJ, Snyder, MA, Hillstrom, WR and Goodman, R., Measurements of Ambient Noise during Extreme Wind Conditions in the Gulf of Mexico, Proceedings of the Oceans, Vancouver, 2007, 1–6. [6] Nystuen JA, McGlothin CC, Cook MS. The underwater sound generated by heavy rainfall. J Acoust Soc Am 1993;93:3169–77.
262
Contents lists available at ScienceDirect
Applied Acoustics journal homepage: www.elsevier.com/locate/apacoust
Ocean ambient noise off Chennai due to very severe cyclonic storm Vardah ⁎
T
M. Ashokan , G. Latha, G. Raguraman All at National Institute of Ocean Technology, Pallikaranai, Chennai 600 100, India
A B S T R A C T
Ocean ambient noise during the passage of a very severe cyclonic storm, Vardah, in the Bay of Bengal has been studied and presented in this paper. A cyclone named Vardah formed near the Andaman and Nicobar Islands on December 6, 2016. The intensified cyclonic storm crossed the south-eastern coast of India in the afternoon hours (local time) of December 12, 2016. During the passage of this cyclone, ocean underwater ambient noise time series data were acquired from the shallow waters of the south-eastern coast of India. In this paper, the noises pertaining to heavy rain and light rain (drizzling) during the cyclonic event have been analysed along with the high wind noise, and the individual power spectra for both the noises have been estimated.
1. Introduction Noise measurements in the shallow waters of Indian seas have attracted a lot of attention, and ambient noise studies have been conducted at various locations in the last few years. Due to the waveguide nature of shallow waters and shipping activities, the acquisition and analysis of ambient noise have become difficult. However, the study of these sounds, using acoustic instruments, such as SONAR, Pinger, etc., is very essential for oceanographers. The predominant sources that cause higher noise levels in the underwater sound field are wind and rain. During a cyclone or storm event, noises pertaining to wind and rain would be much higher. Rain noise depends on the drop size and the resonance frequency. Smaller rain drops (light rain) produce sounds at frequencies greater than 14 kHz, while larger rain drops (heavy rain) produce sounds at frequencies lesser than 10 kHz. During a cyclonic event, rain noise is always dominated by heavy wind noise. Nystuen et al. [6] described that heavy rainfall and light rainfall create different acoustic frequencies: light drizzle creates frequencies in the band of 10–16 kHz while heavy rainfall with large raindrops creates noise in the band < 10 kHz. It is clearly understood that the frequency of sounds produced by heavy rainfall is inversely proportional to the drop size. Measurements of underwater sounds created by rain were acquired at three U.S. seaside locations during convective events by Black et al. [3] and high association was found between sound spectrum levels in the 4–10 kHz frequency band. Ambient noise levels during the cyclonic events in the South China Sea were examined by Wei et al. [11] in the course of the Asian Seas International Acoustics Experiment (ASIAEX) South China Research. Barry et al. [2] estimated different ambient sound spectra for different rainfall rates and wind speeds
⁎
through a sequence of discrimination methods. The critical underwater ambient noise power pertaining to the storm was estimated by Wilson and Makris [12]. Measurements of ambient noise in the Gulf of Mexico were carried out by Newcomb et al. [5] during Cyclone Lili and Tropical Hurricane Isidore. Ambient noise measurements containing the storm passage noise were acquired in the Gulf of Mexico by Orlin and Snyder [7]. Measured ambient noise levels for the New Jersey shelf during the storm associated with deep water measurements were demonstrated with waveguide properties by Knobles et al. [4]. While ambient noise features for sea states lesser than 4 have been widely studied in the shallow Bay of Bengal [8], noise features for sea states larger than 4 and also during extreme meteorological conditions have not been analysed extensively in the shallow waters of Indian seas. Studies pertaining to wind/wave noise in various shallow water locations of Indian seas have been carried out by the National Institute of Ocean Technology (NIOT). Analyses of ambient noise levels resulting from very severe cyclonic events have been reported by Ashokan et al. [1], utilising the data from the ocean underwater ambient noise measurement system along with the wind and rain sensors attached to a moored buoy. In this paper, noise datasets pertaining to rain events have been separated out from the time series and power spectral density estimation has been carried out. The time series data of wind speed have been investigated along with the wind noise spectrum. The rainfall events have been studied in connection with predominant wind data. Noise during the passage of Cyclone Vardah has been examined by studying the spectrum. Cyclone Vardah intensified into a severe cyclonic storm on December 9, 2016. The track of the cyclone and the location of underwater ambient noise measurement are shown in Fig. 1. The cyclone
Corresponding author. E-mail addresses: [email protected] (M. Ashokan), [email protected] (G. Latha), [email protected] (G. Raguraman).
https://doi.org/10.1016/j.apacoust.2018.06.008 Received 30 October 2017; Received in revised form 4 June 2018; Accepted 11 June 2018 0003-682X/ © 2018 Elsevier Ltd. All rights reserved.
Applied Acoustics 140 (2018) 256–262
M. Ashokan et al.
2. Materials, methods and instrumentation setup 18 N
18 N
16 N
16 N
14 N
14 N
12 N
12 N
10 N
Continuous passive acoustic time series measurements including noise resulting from Cyclone Vardah have been acquired from the shallow waters of the eastern coast of India during the period, November 30, 2016 to December 15, 2016, by using the autonomous ocean underwater ambient noise measuring system, indigenously developed by NIOT (Table 1). The system consists of a 21-element Vertical Linear Array (VLA) of omni-directional hydrophones with related data acquisition components and battery container as an enclosure, along with subsea floats and surface marker buoy in the mooring link. Hydrophones were positioned at a depth of 8 m, while the ocean depth is 17 m. The total length of the 21-element VLA is 1.5 m, and the element spacing is 0.075 m. The data from VLA’s first hydrophone positioned at a depth of 7.25 m were used for this analysis. The middle hydrophone in the 21-element VLA was positioned at a depth of 8 m. The last hydrophone in the 21-element VLA was positioned at a depth of 8.75 m. As per the experiment protocol of NIOT, the instruments were tested and calibrated at the Underwater Acoustic Test Facility of NIOT, which is the only research laboratory in India to be accredited by the National Accreditation Board for Testing and Calibration Laboratories (NABL). The system has the ability of sampling 21 channels simultaneously, with the sampling rate of 100 kHz per channel at a duration of 60 s every 3 h. The receiving sensitivity of the hydrophone is −168 dB rel 1 V μPa−1. Power spectral density of noise is calculated using Welch’s averaged modified periodogram technique. The data are split into smaller segments, with a Hamming window with a 50% overlap and Fast Fourier Transform accomplished. Wind speed data during the cyclone period have been obtained from a moored buoy deployed by Ocean Observation System Programme of NIOT, positioned very near to the ambient noise system location. The distance between the moored buoy and the ambient noise measurement system is 500 m. The extreme wind speed measured by the buoy was 26 m/s during the cyclone landfall.
10 N Ambient noise measurement system
74 E 76 E 78 E
8N
80 E 82 E 84 E 86 E 88 E
Fig. 1. Vardah cyclone path.
Table 1 Data collection period. S. No.
Experiment period
Number of days
Number of data sets
1.
30 Nov 2016 to 15 Dec 2016
15
120
Table 2 Selected noise data sets to estimate PSD during Vardah cyclone. Date and time of acquisition of noise data sets (UTC format – Date Month Year, HH:MM)
Wind speed (m/s)
Precipitation (mm)
12 12 12 12 12 12 12
16 23 18 23 15 09 04
143.09 143.09 143.09 143.09 99.85 48.62 48.62
Dec Dec Dec Dec Dec Dec Dec
2016, 2016, 2016, 2016, 2016, 2016, 2016,
02:30 Hrs 0530 Hrs 0830 Hrs 1130 Hrs 1430 Hrs 1730 Hrs 2030 Hrs
3. Results and discussion Noise data sets that are selected for estimating the Power Spectral Density (PSD) during the cyclone period have been shown in the Table 2. A total of seven records have been considered with a time interval of 3 h. Fig. 2 shows the Spectrogram of Vardah cyclone data on Dec 12, 2016. During the cyclone period, a typical increase of 5–10 dB in the lower band and 2–3 dB in the higher band of frequencies is observed. The measurements of wind speed and rainfall events during Cyclone Vardah are shown in Fig. 3. Fig. 4a shows the noise spectrum for different wind speeds during the entire day of the cyclone crossing
track and landfall time have been provided by the Indian Metrological Department (IMD), Chennai. The noise measurement system was positioned very close to the cyclone track. The cyclone dissipated on December 13, 2016. The rain noise spectrum during Cyclone Vardah has been analysed from the time series measurements.
Vardah Cyclone
Fig. 2. Spectrogram of Vardah cyclone data on Dec 12, 2016. 257
Noise spectrum level (dB re 1μPa2/Hz)
8N
Applied Acoustics 140 (2018) 256–262
M. Ashokan et al.
Fig. 3. Wind speed events and Rainfall events.
Fig. 4a. Power spectra of heavy wind noise on Dec 12, 2016 for varying wind speeds.
Fig. 4b. 1/3 octave frequency band sound pressure levels for different wind speeds.
258
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M. Ashokan et al.
Fig. 5. Power spectra of heavy rain and light rain on Dec 12, 2016.
Fig. 6. Sound spectra for convective rain and drizzling.
Fig. 7. Noise level at different frequencies for varying wind speeds.
and landfall. It is clearly observed from the power spectrum (Fig. 4a) that the noise levels vary from 80 dB to 102 dB in the band less than 1 kHz. The highest noise level has been detected in this frequency band at the wind speed of 23 m/s, i.e., during the cyclone landfall. It is clearly seen that high wind speed creates noise much higher than the heavy rain noise. Hence, wind noise is the controlling feature during very severe cyclonic events. The frequency spectra of the cyclone noise in the range less than 5 kHz are a result of the wind speed. Fig. 4b shows the one-third octave frequency band sound pressure levels for the wind speeds 23 m/s, 18 m/s, 16 m/s, 15 m/s, 09 m/s and 04 m/s for varying centre frequencies (plotted in logarithmic scale). It is clearly seen that the increase in wind speeds leads to the increase in noise levels. The power spectrum of noise pertaining to rainfall on December 12, 2016 is shown in Fig. 5. It has been observed from the power spectrum (Fig. 5) that the peak noise level due to heavy rain in the band 1–2 kHz has
reached the maximum at the time of Cyclone Vardah’s landfall, i.e., at 0530 h (UTC). The peak noise level pertaining to light rain (drizzling) has been detected after the cyclone landfall, i.e., at 0830 h (UTC). It is clearly seen from Fig. 5 that the heavy rainfall noise dominates in the frequency band less than 2 kHz (zoomed portion) during the cyclone landfall and light rain dominates in the frequency band higher than 10 kHz (zoomed portion) after the cyclone landfall. As in the case of convective storm systems, the presence of updrafts may often keep the raindrop aloft and allow the drops to grow in size, and as the drop size increases, the frequencies shift towards the lower band. Also, in convective rain events, the main section of the total rainfall is due to large raindrops [9]. Hence, the spectrum in Fig. 5 is primarily created by tremendously large raindrops. At the time of the cyclone, there was nil occurrence of anthropogenic noise and man-made noise. The spectrogram of Cyclone Vardah’s noise for the entire day during the cyclone
259
Applied Acoustics 140 (2018) 256–262
M. Ashokan et al.
Fig. 8a. Time series of heavy rainfall noise.
crossing and landfall, i.e., on December 12, 2016 is clearly shown in Fig. 6. The sound spectra for convective rain (heavy rain) and drizzling are shown in Fig. 6 which are similar to that described in Sara [10]. The noise spectra for different wind speeds are shown in Fig. 7. It is observed that the noise level increases with the increase in wind speed and decreases with the increase in frequency. In order to estimate rainfall from the acoustic measurements, the rain drop size needs to be determined and this can be achieved by analysing the time span of the rainfall drop. Butterworth filter for frequencies less than 10 kHz has been applied to the time series data pertaining to heavy rain (Fig. 8a). Fig. 8b shows the zoomed portion of a single heavy rainfall drop occurring at the 30th second. It is clearly understood from Fig. 8b that the lifespan of the large drop is 1 ms pertaining to heavy rainfall noise and the corresponding frequency falls at 7 kHz (Fig. 8c). To find the lifespan of the drizzling rainfall drop, Butterworth filter for frequencies higher than 10 kHz has been applied to the time series data (Fig. 9a). Fig. 9b shows the zoomed portion of a single drizzling rainfall drop occurring at the 20th second. It is clearly understood from Fig. 9b that the lifespan of the small drop is < 1 ms pertaining to the drizzling rainfall noise and the corresponding frequency falls at 14 kHz (Fig. 9c). Fig. 8b. Chopped portion of heavy rainfall drop.
Fig. 8c. Power spectrum of chopped portion of heavy rainfall drop.
260
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Fig. 9a. Time series of light rainfall noise.
4. Conclusion Ocean ambient noise resulting from cyclonic winds and rainfall during the passage of Cyclone Vardah has been examined in the shallow waters off Chennai in the Bay of Bengal. Throughout the cyclone period, ambient noise levels were found to have increased by 5–10 dB from normal values in the frequency band less than 5 kHz and by 2–3 dB in the band higher than 10 kHz. It is inferred that while both rain and wind were present, the wind noise played a major role during this cyclonic event.
Acknowledgements The authors acknowledge the encouragement given by the Director of the National Institute of Ocean Technology to carry out this research work. The authors also thank the field support given by Mr. A. Thirunavukkarasu, Mr. C. Dhanaraj, Mr. P. Edwards Durai and Mr. K. Nithyanandam for measuring the ocean ambient noise. The support from Mrs. Malarkodi for testing and calibration of the hydrophone array is gratefully acknowledged. The authors thank the India Meteorological Department, Chennai, for providing the cyclone tracks and satellite and radar imageries.
Fig. 9b. Chopped portion of light rainfall drop.
Fig. 9c. Power spectrum of chopped portion of light rainfall drop.
261
Applied Acoustics 140 (2018) 256–262
M. Ashokan et al.
References
[7] P. Orlin, M. Snyder, An Heuristic Ocean Ambient Noise Classifier, Pacific Rim Underwater Acoustics Conference Proceedings, Canada, 2007. [8] Ramji S, Latha G, Rajendran V, Ramakrishnan S. Wind dependence of ambient noise in shallow water of Bay of Bengal. Appl Acoust 2008;69(12):1294–8. [9] Sanjana MC, Latha G, Thirunavukkarasu A. Ambient noise during rough weather and cyclones in the shallow Bay of Bengal. Chin J Oceanol Limnol 2014;32(4):921–32. [10] Pensieri Sara, Bozzano Roberto, Nystuen Jeffrey A, Anagnostou Emmanouil N, Anagnostou Marios N, Bechini Renzo. Underwater Acoustic Measurements to Estimate Wind and Rainfall in the Mediterranean Sea. Advances in Meteorology 2015:1–18. [11] Wei RC, Chen CF, Newhall AE. A preliminary examination of the low-frequency ambient noise field in the South China Sea during the 2001 ASIAEX experiment. IEEE J Oceanic Eng 2004;29(4):1308–15. [12] Wilson JD, Makris NC. Ocean acoustic hurricane classification. J Acoust Soc Am 2006;119(1):168–81.
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