An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil
Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (S...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Costa, Carine G. R. [verfasserIn] Leite, José Roberto B. [verfasserIn] Castro, Belmiro M. [verfasserIn] Blumberg, Alan F. [verfasserIn] Georgas, Nickitas [verfasserIn] Dottori, Marcelo [verfasserIn] Jordi, Antoni [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Ocean dynamics - Berlin : Springer, 1948, 70(2019), 2 vom: 09. Dez., Seite 257-271 |
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| Übergeordnetes Werk: |
volume:70 ; year:2019 ; number:2 ; day:09 ; month:12 ; pages:257-271 |
| Links: |
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| DOI / URN: |
10.1007/s10236-019-01314-x |
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| Katalog-ID: |
SPR00921321X |
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| 100 | 1 | |a Costa, Carine G. R. |e verfasserin |4 aut | |
| 245 | 1 | 3 | |a An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil |
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| 520 | |a Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.$ 0^{∘} $S, 46.$ 3^{∘} $W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets. | ||
| 650 | 4 | |a Storm tides |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Operational forecasting |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Estuaries |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Santos |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Numerical modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a South Brazil Bight |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Leite, José Roberto B. |e verfasserin |4 aut | |
| 700 | 1 | |a Castro, Belmiro M. |e verfasserin |4 aut | |
| 700 | 1 | |a Blumberg, Alan F. |e verfasserin |4 aut | |
| 700 | 1 | |a Georgas, Nickitas |e verfasserin |4 aut | |
| 700 | 1 | |a Dottori, Marcelo |e verfasserin |4 aut | |
| 700 | 1 | |a Jordi, Antoni |e verfasserin |4 aut | |
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10.1007/s10236-019-01314-x doi (DE-627)SPR00921321X (SPR)s10236-019-01314-x-e DE-627 ger DE-627 rakwb eng 550 ASE 38.90 bkl Costa, Carine G. R. verfasserin aut An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.$ 0^{∘} $S, 46.$ 3^{∘} $W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets. Storm tides (dpeaa)DE-He213 Operational forecasting (dpeaa)DE-He213 Estuaries (dpeaa)DE-He213 Santos (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 South Brazil Bight (dpeaa)DE-He213 Leite, José Roberto B. verfasserin aut Castro, Belmiro M. verfasserin aut Blumberg, Alan F. verfasserin aut Georgas, Nickitas verfasserin aut Dottori, Marcelo verfasserin aut Jordi, Antoni verfasserin aut Enthalten in Ocean dynamics Berlin : Springer, 1948 70(2019), 2 vom: 09. Dez., Seite 257-271 (DE-627)337809313 (DE-600)2063267-8 1616-7228 nnns volume:70 year:2019 number:2 day:09 month:12 pages:257-271 https://dx.doi.org/10.1007/s10236-019-01314-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.90 ASE AR 70 2019 2 09 12 257-271 |
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10.1007/s10236-019-01314-x doi (DE-627)SPR00921321X (SPR)s10236-019-01314-x-e DE-627 ger DE-627 rakwb eng 550 ASE 38.90 bkl Costa, Carine G. R. verfasserin aut An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.$ 0^{∘} $S, 46.$ 3^{∘} $W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets. Storm tides (dpeaa)DE-He213 Operational forecasting (dpeaa)DE-He213 Estuaries (dpeaa)DE-He213 Santos (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 South Brazil Bight (dpeaa)DE-He213 Leite, José Roberto B. verfasserin aut Castro, Belmiro M. verfasserin aut Blumberg, Alan F. verfasserin aut Georgas, Nickitas verfasserin aut Dottori, Marcelo verfasserin aut Jordi, Antoni verfasserin aut Enthalten in Ocean dynamics Berlin : Springer, 1948 70(2019), 2 vom: 09. Dez., Seite 257-271 (DE-627)337809313 (DE-600)2063267-8 1616-7228 nnns volume:70 year:2019 number:2 day:09 month:12 pages:257-271 https://dx.doi.org/10.1007/s10236-019-01314-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.90 ASE AR 70 2019 2 09 12 257-271 |
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10.1007/s10236-019-01314-x doi (DE-627)SPR00921321X (SPR)s10236-019-01314-x-e DE-627 ger DE-627 rakwb eng 550 ASE 38.90 bkl Costa, Carine G. R. verfasserin aut An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.$ 0^{∘} $S, 46.$ 3^{∘} $W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets. Storm tides (dpeaa)DE-He213 Operational forecasting (dpeaa)DE-He213 Estuaries (dpeaa)DE-He213 Santos (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 South Brazil Bight (dpeaa)DE-He213 Leite, José Roberto B. verfasserin aut Castro, Belmiro M. verfasserin aut Blumberg, Alan F. verfasserin aut Georgas, Nickitas verfasserin aut Dottori, Marcelo verfasserin aut Jordi, Antoni verfasserin aut Enthalten in Ocean dynamics Berlin : Springer, 1948 70(2019), 2 vom: 09. Dez., Seite 257-271 (DE-627)337809313 (DE-600)2063267-8 1616-7228 nnns volume:70 year:2019 number:2 day:09 month:12 pages:257-271 https://dx.doi.org/10.1007/s10236-019-01314-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.90 ASE AR 70 2019 2 09 12 257-271 |
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10.1007/s10236-019-01314-x doi (DE-627)SPR00921321X (SPR)s10236-019-01314-x-e DE-627 ger DE-627 rakwb eng 550 ASE 38.90 bkl Costa, Carine G. R. verfasserin aut An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.$ 0^{∘} $S, 46.$ 3^{∘} $W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets. Storm tides (dpeaa)DE-He213 Operational forecasting (dpeaa)DE-He213 Estuaries (dpeaa)DE-He213 Santos (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 South Brazil Bight (dpeaa)DE-He213 Leite, José Roberto B. verfasserin aut Castro, Belmiro M. verfasserin aut Blumberg, Alan F. verfasserin aut Georgas, Nickitas verfasserin aut Dottori, Marcelo verfasserin aut Jordi, Antoni verfasserin aut Enthalten in Ocean dynamics Berlin : Springer, 1948 70(2019), 2 vom: 09. Dez., Seite 257-271 (DE-627)337809313 (DE-600)2063267-8 1616-7228 nnns volume:70 year:2019 number:2 day:09 month:12 pages:257-271 https://dx.doi.org/10.1007/s10236-019-01314-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.90 ASE AR 70 2019 2 09 12 257-271 |
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10.1007/s10236-019-01314-x doi (DE-627)SPR00921321X (SPR)s10236-019-01314-x-e DE-627 ger DE-627 rakwb eng 550 ASE 38.90 bkl Costa, Carine G. R. verfasserin aut An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.$ 0^{∘} $S, 46.$ 3^{∘} $W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets. Storm tides (dpeaa)DE-He213 Operational forecasting (dpeaa)DE-He213 Estuaries (dpeaa)DE-He213 Santos (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 South Brazil Bight (dpeaa)DE-He213 Leite, José Roberto B. verfasserin aut Castro, Belmiro M. verfasserin aut Blumberg, Alan F. verfasserin aut Georgas, Nickitas verfasserin aut Dottori, Marcelo verfasserin aut Jordi, Antoni verfasserin aut Enthalten in Ocean dynamics Berlin : Springer, 1948 70(2019), 2 vom: 09. Dez., Seite 257-271 (DE-627)337809313 (DE-600)2063267-8 1616-7228 nnns volume:70 year:2019 number:2 day:09 month:12 pages:257-271 https://dx.doi.org/10.1007/s10236-019-01314-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.90 ASE AR 70 2019 2 09 12 257-271 |
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English |
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Enthalten in Ocean dynamics 70(2019), 2 vom: 09. Dez., Seite 257-271 volume:70 year:2019 number:2 day:09 month:12 pages:257-271 |
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Enthalten in Ocean dynamics 70(2019), 2 vom: 09. Dez., Seite 257-271 volume:70 year:2019 number:2 day:09 month:12 pages:257-271 |
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Storm tides Operational forecasting Estuaries Santos Numerical modeling South Brazil Bight |
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Costa, Carine G. R. @@aut@@ Leite, José Roberto B. @@aut@@ Castro, Belmiro M. @@aut@@ Blumberg, Alan F. @@aut@@ Georgas, Nickitas @@aut@@ Dottori, Marcelo @@aut@@ Jordi, Antoni @@aut@@ |
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2019-12-09T00:00:00Z |
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R.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.$ 0^{∘} $S, 46.$ 3^{∘} $W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. 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Costa, Carine G. R. |
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Costa, Carine G. R. ddc 550 bkl 38.90 misc Storm tides misc Operational forecasting misc Estuaries misc Santos misc Numerical modeling misc South Brazil Bight An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil |
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550 ASE 38.90 bkl An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil Storm tides (dpeaa)DE-He213 Operational forecasting (dpeaa)DE-He213 Estuaries (dpeaa)DE-He213 Santos (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 South Brazil Bight (dpeaa)DE-He213 |
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ddc 550 bkl 38.90 misc Storm tides misc Operational forecasting misc Estuaries misc Santos misc Numerical modeling misc South Brazil Bight |
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ddc 550 bkl 38.90 misc Storm tides misc Operational forecasting misc Estuaries misc Santos misc Numerical modeling misc South Brazil Bight |
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An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil |
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An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil |
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Costa, Carine G. R. Leite, José Roberto B. Castro, Belmiro M. Blumberg, Alan F. Georgas, Nickitas Dottori, Marcelo Jordi, Antoni |
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Costa, Carine G. R. |
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10.1007/s10236-019-01314-x |
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operational forecasting system for physical processes in the santos-sao vicente-bertioga estuarine system, southeast brazil |
| title_auth |
An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil |
| abstract |
Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.$ 0^{∘} $S, 46.$ 3^{∘} $W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets. |
| abstractGer |
Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.$ 0^{∘} $S, 46.$ 3^{∘} $W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets. |
| abstract_unstemmed |
Abstract We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.$ 0^{∘} $S, 46.$ 3^{∘} $W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets. |
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An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil |
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| score |
7.3990517 |