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008			220601s2020 sz | s |||| 0|eng d
020			_a9783031024900
024	7		_a10.1007/978-3-031-02490-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aGC10.4.M36 _b2020 EB
100	1		_aNichols, C. Reid _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687593
245	1	0	_aMarine Environmental Characterization _cby C. Reid Nichols, Kaustubha Raghukumar
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XIII, 91 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Ocean Systems Engineering _x2692-4471
505	0		_aPreface -- Acknowledgments -- Introduction -- Oceanographic Regions -- Example Critical Phenomena -- Systems and Sensors -- Data Quality -- Data Analysis -- Key Challenges -- Conclusions -- Glossary of Environmental Terminology -- Bibliography -- Authors' Biographies.
520			_aThe use of environmental data to support science, technology, and marine operations has evolved dramatically owing to long-term ocean observatories, unmanned platforms, satellite and coastal remote sensing, data assimilative numerical models, and high-speed communications. Actionable environmental information is regularly produced and communicated from quality-controlled measurements and skillful forecasts. The characterization of complex oceanographic processes is more difficult compared to inland features because of the difficulty in obtaining observations from often remote and hazardous locations. Regardless, coastal and ocean engineering projects and operations require the collection and analysis of meteorological and oceanographic data to fill information gaps and the running of numerical models to characterize regions of interest. Data analytics are also essential to integrate disparate marine data from national archives, in situ sensors, imagery, and numerical models to meet project requirements. Holistic marine environmental characterization is essential for data-driven decision making across the science and engineering lifecycle (e.g., research, production, operations, end-of-life). Many marine science and technology projects require the employment of an array of instruments and models to characterize spatially and temporally variable processes that may impact operations. Since certain environmental conditions will contribute to structural damage or operational disturbances, they are described using statistical parameters that have been standardized for engineering purposes. The statistical description should describe extreme conditions as well as long- and short-term variability. These data may also be used to verify and validate models and simulations. Environmental characterization covers the region where engineering projects or maritime operations take place. For vessels that operate across a variety of seaways, marine databases and models are essential to describe environmental conditions. Data, which are used for design and operations, must cover a sufficiently long time period to describe seasonal to sub-seasonal variations, multi-year, decadal, multi-decadal, and even climatological factors such as sea level rise, coastal winds, waves, and global ocean temperatures. Combined data types are essential for the computation of environmental loads for the region of interest. Typical factors include winds, waves, currents, and tides. Some regions may require consideration of biofouling, earthquakes, ice, salinity, soil conditions, temperature, tsunami, and visibility. Observations are also used for numerical forecasts, but errors may exist due to inexact physical assumptions and/or inaccurate initial data, which can cause errors to grow to unacceptable levels with increased forecasting times. Overall, marine environmental characterization tools, from observational data to numerical modeling, are critical to today's science, engineering, and marine operational disciplines.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9146507 _aEcología marina _xProceso de datos
650		7	_2embne _9146507 _aEcología marina _xModelos matemáticos
650		7	_2embne _9138147 _aOceanografía _xProceso de datos
700	1		_aRaghukumar, Kaustubha _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687594
776	0	8	_iPrinted edition: _z9783031003189
776	0	8	_iPrinted edition: _z9783031013621
776	0	8	_iPrinted edition: _z9783031036187
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02490-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI