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echopype: Toward Interoperable and Scalable Ocean Sonar Data Processing

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posted on 23.07.2019, 20:34 by Wu-Jung Lee, Kavin Nguyen, Valentina Staneva

Long term sonar observations provide an unique view at the ocean’s water column which combined with other measurements from cabled arrays, ship surveys and bouys provide an opportunity for comprehensive study of the ocean ecosystem. However, the integration of the terabytes of existing observations is hindered by proprietary software, incompatible data formats, and lack of large scale data processing tools. To address these problems, we develop echopype, an open source Python package which aims to enhance the interoperability and scalability of sonar data processing. It converts instrument-specific echosounder data formats into a standard netcdf format, which facilitates further parallel processing and visualization with Python’s xarray library. We demonstrate how the package can be used to obtain almost-realtime sonar observations from the Ocean Observatories Initiative with a few lines of code. This poster was presented in July 2019 at the Earth Science Information Partners (ESIP) Summer Meeting held in Tacoma, Washington.


NSF EAGER: Developing a temporally adaptive decomposition framework for analyzing long-term echosounder time series


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