Using Ambient Sound to Passively Monitor Sea Surface Processes


Jeffrey A Nystuen and Barry Ma

Applied Physics Laboratory

University of Washington

1013 NE 40th Street

Seattle,Washington 98105 USA




            Making measurements of surface weather conditions in oceanic regions is relatively difficult, and yet these data are needed for many types of climatic and process studies of air-sea interaction.  Passive monitoring of the underwater sound field offers a means to make these measurements, as several air-sea interaction processes are responsible for the production and modification of underwater sound in the ocean.  In the frequency range from 500 Hertz to 50 kHz, natural sources of underwater sound include breaking wind waves and precipitation.  Unique characteristics of these sound sources allow them to be identified and then quantified.  Furthermore, distortion of these acoustic signals by sub-surface ambient bubbles permits the detection and quantification of the near-surface bubbles, a potential indication of gas transfer and sea state condition.  Since 1999 long-term ambient sound measurements have been made from several of the deep ocean moorings making up part of the Tropical Atmosphere Ocean (TAO) array in the tropical Pacific Ocean.  Examples of acoustic wind speed measurement, rainfall detection and measurement, and ambient bubble detection are presented.  This acoustic remote sensing technique is passive and can be made from a variety of oceanic platforms, including surface moorings, drifters or bottom-mounted systems.  It introduces no acoustic disturbance into the environment and thus poses no potential harm to marine mammals or other forms of life in the ocean.  [Work sponsored by ONR, NSF and NOAA.]


Keywords: Ambient Noise, Rainfall Measurement, Wind Speed Measurement, Bubbles



Remote sensing of ambient sound in the ocean provides information about the processes generating the sound and about the intervening media modifying the sound.  This allows passive monitoring of environmental conditions from simple and robust sensors, namely hydrophones.  Hydrophones can be deployed from a wide variety of platforms including surface1,2 and subsurface moorings3,4 and from drifters5.  As with other remote sensing techniques, the hydrophones are deployed away from the surface and do not interfere with the physical processes being measured.  Furthermore, as a passive remote sensing measurement, there is no acoustic disturbance into the environment, minimizing potential harm to marine mammals or other forms of marine life. And it also means that fouling, and the likelihood of vandalism and theft are reduced.

In the frequency range from 200-50,000 Hertz, naturally generated sound at the sea surface is predominately produced by wind-driven breaking waves and precipitation.  In turn, these physical processes generate sound principally through the production of bubbles during splashing at the ocean surface.  And on the scale of individual bubbles, the sound is the resonant ring of newly formed individual bubbles within the splashes6,7.  Because wind-driven breaking waves and raindrop splashes generate different distributions of  bubbles sizes, the sound from breaking waves can be distinguished from the sound of precipitation.  This allows each sound source to be identified and then quantitatively measured.

Bubbles can also absorb sound.  Smaller bubbles are mixed downward into the ocean surface by turbulence to form clouds, plumes and layers.  Sound newly generated at the surface must pass through these bubble structures into order to reach the measurement hydrophone.  The ambient bubbles absorb sound principally at their resonant frequency and consequently distort the shape of the measured sound spectrum. By quantifying this distortion, a measurement of the ambient bubble population can be made8,9.  This passive measurement of the ambient bubble population has been observed in high wind conditions8 and during extremely heavy rainfall10.


Figure 1.  Examples of sound spectra recorded from geophysical sources.  The effect of ambient bubbles to depress the observed sound from an extreme rain event (200 mm/hr) is shown by comparing the spectrum five minutes after the start of the event to the initial spectrum.  These data are from an ocean surface mooring in the South China Sea2.  The ARG was deployed at 20 m depth.


                Examples of the spectral signal for different geophysical sound sources are shown in Fig. 1. The sound generated by wind has a distinctive shape, but is relatively quiet when compared to the sound generated by rain.  Drizzle has a distinctive peak in the spectrum from 13-25 kHz, and the sound of heavy rain is very, very loud.  Ambient bubbles depress the sound levels at frequencies above 20 kHz.  Monitoring rainfall using ambient sound is of particular interest as fresh water flux into the ocean is an important part of the energy balance between the atmosphere and ocean, and is extremely difficult to measure on all time and space scales.  Rainfall produces a dominant sound underwater when it is present. 

                To take advantage of this acoustic signal, Acoustic Rain Gauges (ARGs) have been designed and built for autonomous deployment on ocean surface moorings. The Pacific Marine Environmental Laboratory (PMEL), National Atmospheric and Ocean Administration (NOAA) has established the Tropical Atmosphere Ocean (TAO) array of roughly 70 ATLAS (Autonomous Temperature Line Acquisition System) ocean surface moorings across the tropical Pacific Ocean to monitor environmental conditions11.  Several of these surface moorings, in particular, moorings at 8°, 10° and 12°N, 95°W and at 0°, 165°E, have been augmented with ARGs. Long-term time series are now being collected to establish scientific confidence in the acoustic measurements of rainfall and wind speed. This paper reports preliminary comparisons of ARG measurements of rainfall and wind speed with surface instrumentation on these moorings.




Acoustic Rain Gauges (ARGs)

                The Acoustic Rain Gauges (ARGs) consist of an ITC-8263 hydrophone, signal pre-amplifiers and a recording computer (Tattletale-8). The nominal sensitivity of these instruments is -160 dB relative to 1 V/mPa and the equivalent oceanic background noise level of the pre-amplifier system is about 28 dB relative to 1 mPa2Hz-1.  Band-pass filters are present to reduce saturation from low frequency sound (high pass at 300 Hz) and aliasing from above 50 kHz (low pass at 40 kHz). The ITC-8263 hydrophone sensitivity also rolls off above its resonance frequency, about 40 kHz. A data collection sequence consists of four 1024 point time series collected at 100 kHz (10.24 ms each) separated by 5 seconds.  Each time series is fast Fourier transformed (FFT) to obtain a 512-point (0-50 kHz) power spectrum. These four spectra were averaged together and spectrally compressed to 64 frequency bins, with frequency resolution of 200 Hz from 100-3000 Hz and 1 kHz from 3-50 kHz. These spectra are evaluated individually to detect the acoustic signature of rainfall and then are recorded internally.

                The overall temporal sampling strategy is designed to allow the instrument to record data for up to one year and yet detect the relatively short rainfall events present in the tropics12.  In order to achieve this, the ARG is designed to enter a low power mode "sleep mode" between each data sample.  For these deployments, the ARGs "sleep" for 8 minutes and then sample the sound field. If "rain" is detected, the sampling rate changes to 1 minute (or 4 minutes if "drizzle" is detected) and stays at the higher sampling rate until rain is no longer detected.  Some "noise" will trigger the high sampling mode and must be removed from the data.

                A sound source at a free surface, the ocean surface, is an acoustic dipole, radiating sound energy downward in a cos2q pattern where q is the zenith angle. This allows the intensity of surface generated sound at some depth, h, below the surface to be given by:


where I0 is the sound intensity at the surface and atten(p) describes the attenuation due to geometric spreading and absorption along the acoustic path, p. If the sound source is uniform at the surface and absorption and refraction are neglected, the measurement should be independent of depth. For any particular deployment, the attenuation along the acoustic path can be complicated, but have only resulted in minor corrections in other studies1.  The ARGs have been deployed at 38 m depth on the mooring lines (wire cable). The depth was chosen to be above the thermocline, lessening the effects of acoustic refraction, and to maximize sampling area, so that the buoy itself does not occupy a significant portion of the effective listening area. Equation (1) can be used to estimate the effective sampling area at the surface.  Neglecting refraction and absorption, 90% of the signal is arriving from a sampling area equal to:


sampling area @ p(3h)2                                                                                                                                                                                                         (2)


where h is the depth of the ARG. The integrating area of the hydrophone is important for two reasons.  First, rainfall is inhomogeneous on all scales, but rainfall measurements are needed on large temporal or spatial scales.  An instrument with a large inherent sampling area should produce a better “mean” rainfall statistic. Second, the large spatial sampling allows the short temporal sampling periods being used for each data sample to include many individual raindrop splashes.


Acoustical Measurements of Wind Speed and Rainfall Rate

                Inversion of the underwater ambient sound field consists of two general components: identifying the source of the sound, and then quantifying it.  Acoustic classification of weather5 has identified four ocean surface features producing distinctive features in the sound spectrum from 1-50 kHz.  These are wind, drizzle, heavy rain and ambient bubbles present (Fig. 1).  Once classification is obtained, there are several algorithms available to quantify wind speed1 and precipitation10. 


Acoustical Rainfall Rate Measurements

                Two types of acoustic rainfall rate algorithms are available. Because different raindrop sizes have distinctive acoustic signatures underwater, the underwater sound can be decomposed into components associated with each drop size.  This allows an acoustic measure of the drop size distribution in the rain10. Once a drop size distribution is obtained, then rainfall rate can be calculated. While the sound field can be "inverted" to measure drop size distribution, the algorithm used here is a simpler empirical algorithm relating the sound level at 5 kHz (SPL5) to the rainfall rate R.  This algorithm is based on empirical correlation of sound levels to 10-minute accumulation data from the R.M. Young rain gauge using data from 0°, 165°E (March 2000-July 2001), and from 10°N, 95°W (Dec 2000-Mar 2001).


log10(R) = (SPL5 - 42.4)/15.4                                                                                                                                                                                               (3)

Acoustical Wind Speed Measurements

            An algorithm for the acoustic quantification of wind speed is available1.  After sound records containing noise, including precipitation, are removed, the sound level at 8 kHz, SPL8, is empirically related to 10-m height wind speed, U10, by 




Comparison Data

R.M. Young rain gauges

                Precipitation measurements on Next Generation ATLAS moorings are made using R.M. Young Model 50202 precipitation gauges, which have been modified by PMEL for integration into the ATLAS electronics13. The sensors are mounted approximately at 3 m above mean sea level on the buoy tower. These sensors have a 100 cm2 catchment cylinder mounted atop a funnel which leads water into a cylindrical measuring tube. Water height within the tube is determined by measuring capacitance. The measuring tube has a storage capacity of 500 ml, representing 50 mm of rainfall accumulation, after which it automatically drains via a siphon. Siphon events take about 30 seconds, and are typically identified by sharp declines in volume for 2 consecutive samples. In real-time processing, these events are ignored.

                The R.M. Young gauge reports a water level within its collection chamber each minute, and calculates the difference to obtain a rainfall rate for that minute. Inspection of 1 minute rain data from recovered moorings of the TAO array indicates that instrumental noise levels are generally low, a few tenth of mm hr-1, relative to the signals of interest14.  The estimated instrumental error for 10-minute derived rainfall rates is 0.4 mm hr-1.  Other sources of noise include undercatch of rainfall in high winds, excessive buoy motion, sea spray, and evaporation from the cylinder. These errors are extremely difficult to quantify.


TRMM Precipitation Product 3B42

                The Tropical Rain Measuring Mission (TRMM) is a satellite dedicated to the measurement of precipitation in the tropics.  Rainfall sensors include a precipitation radar (PR), a microwave imager (TMI) and a visible/infrared sensor (VIRS).  Data from these instruments are combined in a variety of ways to produce rainfall products which are available through the NASA Goddard Distributed Active Archive Center (DAAC)15.  The rainfall product 3B-42 is a daily precipitation product spatially averaged on a 1º by 1º grid using the infrared sensor (VIRS) with monthly calibration adjustments from the combined PR/TMI data (Product 2B31). 




                The desired geophysical signal is usually persistent and, in the case of rain, very loud.  However, in the ocean, there are other underwater sounds which can interfere with acoustical weather measurements.  Sound spectra not consistent with known geophysical signals (wind, rain and drizzle) are assumed to be "noise" and are removed from the data record. This is done by objectively using features of the spectra, i.e. levels, slopes and peaks to identify the sound source, and also subjectively by examining all events that triggered the high sampling mode of the ARGs.  Loud low frequency noise was present for extended periods of time during several of the deployments.  It is thought that this is flow/splash noise associated with strong local currents on the surface float of the mooring.  At the end of several deployments, physical rattling of the mounting cage is also thought to produce loud low frequency noise.  At these times, acoustical measurements of surface processes could not be obtained.  At other times, short duration noises due to shipping or biological activity are recorded.  In general, these do not interfere with the geophysical interpretation of the sound signal.

Figure 2 shows time series of oceanic underwater sound at 3 different frequencies.  The different sources of sound are identified by comparing spectral intensity levels, spectral shapes and temporal variances of sound intensities.  Fig. 3 shows the quantitative acoustic interpretation of the time series shown in Fig. 2.  Wind speed agreement is excellent for winds above 3 m/s.  For wind speeds below 3 m/s, there is no wave breaking at the ocean surface, and thus there is no acoustic signal with which to measure the wind speed.  Acoustic detection of rainfall events is excellent.


Figure 2. An example of a time series of oceanic underwater sound at 3 different frequencies (3, 8.5 and 21 kHz) recorded from an Acoustical Rain Gauge (ARG) mounted on a deep ocean mooring at 40 meters depth.  The mooring is part of the NOAA TAO array and is located at 10ºN, 95ºW. 



Figure 3. Geophysical interpretation of the ambient sound time series shown in Fig. 2.  Quantitative comparison of the acoustic measurement to surface-mounted R.M. Young sensors is shown.


Validation of Wind Speed Measurements


Figure 4.  A comparison of wind speed measurements from the ATLAS mooring anemometer and the ARG for December 1999 at 8°N, 95°W. 


                Figure 4 shows an example of the comparison of acoustical and surface measurements for wind speed. The buoy winds have been corrected to equivalent 10 m height using the COARE V2.5b bulk flux algorithms16 and then smoothed with a 30-minute binomial filter. The mean absolute difference between the ARG measurement and the anemometer is 0.5 ± 0.4 m/s. The bias is less than 0.1 m/s.  Note that the acoustic wind speed algorithm does not allow values less than 2.2 m/s.  Similar agreement is observed for the other months. 


Validation of Rainfall Measurements


                Rainfall accumulations for the Year 2000 at the TAO moorings at 8°, 10° and 12°N, 95°W are shown in Figure 5.  This part of the ocean has a distinctive rainy season beginning in May and lasting into October.  Exact agreement between the TRMM estimate and the other two methods should not be expected.  The sampling strategies are very different (spatial averaging versus high temporal resolution). However, seasonal agreement should be expected.  And except for a few “events,” this agreement is observed.  But the figure also shows the difficulty associated with obtaining rainfall measurements at sea.  The comparison suggests that the R.M. Young (RMY) gauge at 12°N, 95°W did not work at the beginning of the rainy season, but started working later in the season, and that the RMY gauge did not work well at 10°N, 95°W.  Furthermore, the surface instrumentation was stolen at 8°N, 95°W in July, but the sub-surface instrumentation, including the ARG was not taken.  Nevertheless, quantitative agreement between the RMY measurement and the ARG measurement when both instruments are working is promising.  Data collection is ongoing and further comparisons will become available.



Figure 5.  Comparison of rainfall accumulation for the Year 2000 at the TAO moorings at 8°, 10° and 12°N, 95°W using the acoustic measurement (ARG), surface rain gauges (RMY) and TRMM satellite estimates.  The shading regions indicate times when persistent local mooring noise prevented acoustic measurements.  The ARGs were deployed at 38 m depth on the mooring lines.  This figure highlights differences.  If single large events are missed by an instrument, the curves diverge.  Times when the curves are parallel indicate agreement. 




The first deployments of ARGs on ocean surface moorings show the promise of passive acoustic remote sensing to make measurements of important air-sea processes, in particular, for rainfall detection and measurement, wind speed measurement and ambient bubble detection.  The acoustic wind speed measurement shows excellent agreement with the ATLAS mooring anemometers, with a bias of less than 0.1 m/s and an absolute mean difference of 0.5 ± 0.4 m/s for wind speeds from 2-12 m/s (30-minute smoothed wind speed data).  Acoustic detection of rain events is excellent.  Quantitative estimates of rainfall accumulations between the acoustic method, surface rain gauges and satellite estimates show only general agreement, and highlight the difficulty of making rainfall measurements at sea.



The TAO Project Office, Dr. Michael McPhaden, Director, at NOAA/PMEL maintains the TAO mooring arrays and has facilitated the deployment and recovery of the ARGs on the TAO moorings.  Development of the ARGs for deployment on ocean moorings was sponsored by the Pan American Climate System (PACS) program in the Office of Global Programs (OGP) of NOAA. Additional funding is from the National Science Foundation and the Office of Naval Research (ONR) Ocean Acoustics. Dr. Weimin Wang, NOAA/PMEL, made the calculations for the adjusted 10 m wind speeds for the buoy anemometer.



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