We present and analyze an alternative, more robust approach to the Welch's overlapped segment averaging (WOSA) spectral estimator. Our method computes sample percentiles instead of averaging over multiple periodograms to estimate power spectral densities (PSDs). Bias and variance of the proposed estimator are derived for varying sample sizes and arbitrary percentiles. We have found excellent agreement between our expressions and data sampled from a white Gaussian noise process.
Large scale studies of underwater noise during rain are important for assessing the ocean environment and enabling remote sensing of rain rates over the open ocean. In this study, approximately 3.5 yrs of acoustical and meteorological data recorded at the northeast Pacific continental margin are evaluated. The acoustic data are recorded at a sampling rate of 64 kHz and depths of 81 and 581 m at the continental shelf and slope, respectively. Rain rates and wind speeds are provided by surface buoys located in the vicinity of each hydrophone. Average power spectra have been computed for different rain rates and wind speeds, and linear and nonlinear regression have been performed. The main findings are (1) the linear regression slopes highly depends on the frequency range, rain rate, wind speed, and measurement depth; (2) noise levels during rain between 200 Hz and 10 kHz significantly increase with increasing wind speed; and (3) the highest correlation between the spectral level and rain rate occurs at 13 kHz, thus, coinciding with the spectral peak due to small raindrops. The results of this study indicate that previously proposed algorithms for estimating rain rates from acoustic data are not universally applicable but rather have to be adapted for different locations.
Large scale studies of underwater ambient noise during rainfall are important for assessing the ocean environment and enabling remote sensing of rainfall rates over the open ocean. In this study, we have evaluated approximately 3.5 years of acoustical and meteorological data recorded at the northeast Pacific Ocean continental shelf and slope. The acoustic data are recorded continuously at a sample rate of 64 kHz at 81 m depth and 581 m depth at the continental shelf and slope, respectively. The wind speeds and rainfall rates are provided by a surface buoy located in the vicinity of each hydrophone. Average power spectra have been computed for different rain rates and wind speeds, and linear and non-linear regression have been performed. The results are comparted between both measurement sites to evaluate the depth dependency of rain noise at the continental margin. In contrast to previous reports, we found that the rain noise levels between 100 Hz and 10 kHz are highly dependent on the prevailing wind speed. Our findings indicate that previously proposed algorithms for estimating rainfall rates from acoustic data are not universally applicable, but rather have to be adapted for different locations.
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