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Jeffrey A. Nystuen

Abstract

Rainfall estimation is difficult, especially in oceanic regions where land-based techniques are unavailable. Fortunately, rain produces a loud and unique sound underwater that can be used to detect and quantify rainfall. Laboratory studies of the sound generated by individual raindrops have provided the basis for a formal inversion of the naturally generated underwater ambient sound field. Field measurements of subtropical rainfall at the Atlantic Oceanographic and Meteorological Lab Rain Gauge Facility are used to demonstrate the forward (predicting the sound field given the rainfall drop size distribution) and the inverse problem (estimating the drop size distribution given the sound field). This acoustical rainfall analysis (ARA) algorithm was tested for several dozen rainfall events spanning six months and was found to provide excellent estimates of rainfall rate, rainfall accumulation, and rainfall reflectivity (the quantity sensed by radars). High temporal resolution (order 5–10 s) variations in drop size distribution within the rain can be studied using ARA.

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Jeffrey A. Nystuen

Abstract

Automatic rain gauges are needed to obtain rainfall statistics from remote locations and platforms. Many of these platforms cannot be serviced regularly, thus requiring unattended operations for many months. At such locations there is often a power consumption limitation requiring that the instrument operate at a fractional duty cycle. For instruments that measure rainfall rate rather than rainfall accumulation, both components of duty cycle—sample duration and sample interval—need to be considered. A 17-month-long record of rainfall was recorded in Miami, Florida. This location is subtropical and has an annual rainy season. These data are subsampled using different duty cycles to assess resulting sampling error. Using 1-min rainfall-rate samples, a duty cycle of 10% produces an expected standard deviation in monthly rainfall accumulation equal to 10% of the mean accumulation. This relationship held true for 15 of the 17 months of data. Two months with very low total accumulations (November 1993 and March 1994, both winter season months) had higher errors. For a fixed duty cycle, sampling error is proportional to both sampling duration and sample interval.

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Jeffrey A. Nystuen

Abstract

Different sized raindrops splashing on a water surface produce sound underwater that is distinctive and can be used to measure the drop size distribution in the rain. Five acoustically significant raindrop sizes are described. An inversion of the underwater sound to measure the drop size distribution in the rain is described and demonstrated. Limitations to the inversion include problems associated with the relative loudness of the largest drops (diameter over 3.5 mm), the relative quietness of the medium drops (diameter 1.2–2.0 mm), and the influence of wind to suppress the signal from the otherwise remarkably loud small drops (diameter 0.8–1.2 mm). Various measures of rainfall, including rainfall rate, equivalent radar reflectivity, median drop size, and other integrated moments of the drop size distribution are measured acoustically and used to examine rainfall research issues. The relationship between equivalent reflectivity and rainfall rate, the ZR diagram, is partitioned acoustically showing that parts of this diagram are occupied by rainfall containing specific drop populations. Rainfall type can be classified acoustically. And because of its relatively large catchment area, high temporal resolution analysis of rainfall is possible. This technique has inherent application in remote oceanic regions where measurements of rainfall are needed to help establish knowledge of the global distribution and intensity of rainfall.

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Jeffrey A. Nystuen

Abstract

Six different types of automatic rain gauges, including tipping bucket, weighing, capacitance, optical, disdrometer, and acoustical sensors, were deployed for 17 months (September 1993–January 1995) at the NOAA Atlantic Oceanographic and Meteorological Laboratory in Miami, Florida. Different rainfall conditions encountered during the experiment included wintertime stratiform frontal rainfall, intense springtime convective systems with extremely high rainfall rates (over 100 mm h−1), summertime convective storms, mesoscale convective systems in the rainy season (September–October), and one tropical storm (Tropical Storm Gordon). Overall, all of the rain gauges performed well, with intercorrelations of order 0.9 or better using 1-min rainfall rates and biases of less than 10%; however, each showed limitations under different rainfall situations. In particular, under extremely heavy rainfall rates (over 100 mm h−1), the disdrometer and tipping bucket rain gauges biased low, while the optical rain gauge biased high. Under light rainfall rates (under 2 mm h−1), the capacitance and tipping bucket rain gauges showed significant instrument noise using the 1-min sampling interval. The optical gauge was sensitive to the relative proportion of small to large raindrops within the rain. The raindrop distribution parameter N 0, the coefficient of the exponential fit to the drop size distribution, could be used to predict the optical gauge bias. When N 0 is large (relatively more small drops), the optical gauge biases high, and when N 0 is small (relatively more large drops), the optical gauge biases low. The acoustic rain measurement showed significant variability when compared to the other gauges. The acoustic measurement is very sensitive to the presence of very large raindrops (over 3.5 mm diameter) as these raindrops are extraordinarily loud underwater and prevent the smaller drop size populations from being heard and accurately counted when they are present. While the range of wind speeds encountered during the experiment was limited, wind did affect the performance of several of the gauges. At higher wind speeds (over 5 m s−1), the disdrometer and acoustic rain gauges biased low and the instrument noise of the capacitance gauge increased significantly.

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Barry B. Ma and Jeffrey A. Nystuen

Abstract

Several years of long-term high temporal resolution ocean ambient noise data from the tropical Pacific Ocean are analyzed to detect oceanic rainfall. Ocean ambient noise generated by rainfall and wind are identified through an acoustic discrimination process. Once the spectra are classified, wind speed and rainfall rates are quantified using the empirical algorithms. Rainfall-rate time series have temporal resolutions of 1 min. These data provide a unique opportunity to study the rainfall events and patterns in two different climate regions, the intertropical convergence zone (ITCZ) of the tropical eastern Pacific (10° and 12°N, 95°W) and the equatorial western Pacific (0°, 165°E). At both locations the rain events have a mean rainfall of 15 mm h−1, but the events are longer in the eastern Pacific. After the rain event is defined, the probability that a rain event can be detected using the change in air–sea temperature often associated with the rainfall is investigated. The result shows that the rain event accompanied by the decrease of air temperature is a general feature, but that using the temperature difference to detect the rainfall has a very high false alarm rate, which makes it unsuitable for rainfall detection.

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Jeffrey A. Nystuen and Harry D. Selsor

Abstract

Weather observations are needed in remote oceanic regions to support numerical weather forecast models, to provide surface truth for satellite sensors, and to help understand global weather patterns. An acoustic mini-drifting buoy using no moving parts has been designed to meet operational naval demands for real-time monitoring of upper-ocean air–sea interface processes. This buoy is an air-deployable, standard sonobuoy-sized buoy that uses an Argos satellite link to transmit data to users. Interpretation of the ambient sound field allows classification of weather into five categories: wind, wind and drizzle, rain, high seas, and shipping contaminated. Quantitative estimates of wind speed are shown to be in agreement with the Special Sensor Microwave/Imager satellite sensor. Rainfall detection is confirmed and rainfall rate quantified using an acoustic rainfall-rate algorithm. Atmospheric pressure, air and sea temperature, and ambient sound levels are measured directly.

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Barry B. Ma and Jeffrey A. Nystuen

Abstract

Rainfall over the ocean is one of the most important climatic parameters for both oceanic and atmospheric science. Traditional accumulation-type rain gauges are difficult to operate at sea, and so an alternate technique using underwater sound has been developed. The technique of passive monitoring of the ocean rainfall using ambient sound depends on the accuracy of sound pressure level (SPL) detection. Consequently, absolute calibration of the hydrophone is desirable, but is difficult to achieve because typically the geometry of the laboratory calibration process does not fit the measurement geometry over the ocean. However, if one assumes that the sound signal that is generated by wind is universal then the wind signal can be used to provide an absolute calibration. Over 90 buoy months of ambient sound spectra have been collected on the Tropical Atmosphere Ocean (TAO) project array since 1998. By applying the Vagle et al. wind speed algorithm, the instrument noises and sensitivity bias for the absolute calibration of each acoustic rain gauge (ARG) are obtained. An acoustic discrimination process is developed to retrieve the pure geophysical signals. A new single-frequency rainfall-rate algorithm is proposed after comparing the ARG data with R.M. Young self-siphoning rain gauge data, collocated on the same moorings. The acoustic discrimination process and the rainfall algorithm are further tested at two other locations and are compared with R.M. Young rain gauges and the Tropical Rain Measuring Mission (TRMM) product 3B42. The acoustic rainfall accumulations show the comparable results in both long (year) and short (hours) time scales.

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Zhongxiang Zhao, Eric A. D’Asaro, and Jeffrey A. Nystuen

Abstract

Underwater ambient sound levels beneath tropical cyclones were measured using hydrophones onboard Lagrangian floats, which were air deployed in the paths of Hurricane Gustav (2008) and Typhoons Megi (2010) and Fanapi (2010). The sound levels at 40 Hz–50 kHz from 1- to 50-m depth were measured at wind speeds up to 45 m s−1. The measurements reveal a complex dependence of the sound level on wind speed due to the competing effects of sound generation by breaking wind waves and sound attenuation by quiescent bubbles. Sound level increases monotonically with increasing wind speed only for low frequencies (<200 Hz). At higher frequencies (>200 Hz), sound level first increases and then decreases with increasing wind speed. There is a wind speed that produces a maximum sound level for each frequency; the wind speed of the maximum sound level decreases with frequency. Sound level at >20 kHz mostly decreases with wind speed over the wind range 15–45 m s−1. The sound field is nearly uniform with depth in the upper 50 m with nearly all sound attenuation limited to the upper 2 m at all measured frequencies. A simple model of bubble trajectories based on the measured float trajectories finds that resonant bubbles at the high-frequency end of the observations (25 kHz) could easily be advected deeper than 2 m during tropical cyclones. Thus, bubble rise velocity alone cannot explain the lack of sound attenuation at these depths.

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Jeffrey A. Nystuen, Michael J. McPhaden, and H. Paul Freitag

Abstract

Surface measurements of precipitation in oceanic environments have proven especially difficult to obtain because traditional technologies such as tipping-bucket rain gauges are unsuitable for deployment from oceanic platforms such as ships and moorings. Recently, the Pacific Marine Environmental Laboratory of the National Oceanic and Atmospheric Administration has modified a collection gauge, the R. M. Young Company rain gauge, for long-term deployment on deep ocean moorings. This instrumentation package was deployed during part of the South China Sea Monsoon Experiment. Also deployed on the same mooring were two acoustic rain gauges (ARGs) that monitor precipitation through the interpretation of the high-frequency, from 500 to 50 000 Hz, underwater sound field. The mooring was located at 20°22.2′N, 116°31.2′E and was in place from 7 April–5 June 1998. Unfortunately, pirates stole the surface instrumentation on 6 May 1998, limiting data from the R. M. Young rain gauge to satellite transmissions prior to the attack. The ARGs survived the attack and reported data throughout the deployment. The acoustic data are interpreted to provide quantification of wind speed; detection, classification, and quantification of rainfall; and the detection and quantification of near-surface bubble layers. Percentage-of-time-raining data from the two rainfall measurements are in excellent agreement. Based on comparison with the R. M. Young rain gauge data, modified acoustic rainfall algorithms are proposed. The acoustic detection of several instances of high near-surface bubble injections during extremely heavy rainfall is described.

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Jeffrey A. Nystuen, John R. Proni, Peter G. Black, and John C. Wilkerson

Abstract

Automatic rain gauge systems are required to collect rainfall data at remote locations, especially oceanic sites where logistics prevent regular visits. Rainfall data from six different types of automatic rain gauge systems have been collected for a set of summertime subtropical rain events and for a set of wintertime rain events at Miami, Florida. The rain gauge systems include three types of collection gauges: weighing, capacitance, and tipping bucket; two gauges that inherently measure rainfall rate: optical scintillation and underwater acoustical inversion; and one gauge that detects individual raindrops: the disdrometer. All of these measurement techniques perform well; that is, they produce rainfall estimates that are highly correlated to one another. However, each method has limitations. The collection gauges are affected by flow irregularities between the catchment basin and the measurement chambers. This affects the accuracy of rainfall-rate measurements from these instruments, especially at low rainfall rates. In the case of the capacitance gauge, errors in 1-min rainfall rates can exceed +10 mm h−1. The rainfall rate gauges showed more scatter than the collection gauges for rainfall rates over 5 mm h−1, and the scatter was relatively independent of rainfall rate. Changes in drop size distribution within an event could not be used to explain the scatter observed in the optical rain gauge data. The acoustical inversion method can be used to measure the drop size distribution, allowing rainfall classification and estimation of other rain parameters—for example, reflectivity or liquid water content—in addition to rainfall rate. The acoustical inversion method has the advantage of an extremely large catchment area, resulting in very high time resolution. The disdrometer showed a large scatter relative to the other rain gauge systems for low rainfall rates. This is consistent with the small catchment area for the disdrometer system.

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