Data Integration for the Gulf of Mexico Satellite Observations

2009 ◽  
Author(s):  
Longzhuang Li ◽  
Yonghuai Liu ◽  
Srujan Kothapally ◽  
Chuihui Jin ◽  
Anil Nalluri
Keyword(s):  
Data Integration ◽  
Gulf Of Mexico ◽  
Satellite Observations

Wide-azimuth seismic and potential field data integration for salt recognition in the southern Gulf of Mexico

2018 ◽  
Vol 37 (12) ◽  
pp. 902-907 ◽  
Author(s):  
Stefano Panepinto ◽  
Jianchun Dai ◽  
Wilson Ibañez ◽  
Ivan Guerra ◽  
Michael O'Briain ◽  
...  
Keyword(s):  
Data Integration ◽  
Gulf Of Mexico ◽  
Potential Field ◽  
Field Data ◽  
Potential Field Data ◽  
Southern Gulf Of Mexico

scholarly journals Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico

Ocean Science ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. 1141-1156
Author(s):  
Bin Wang ◽  
Katja Fennel ◽  
Liuqian Yu
Keyword(s):  
Data Assimilation ◽  
Gulf Of Mexico ◽  
A Priori ◽  
Three Dimensional ◽  
Biological Properties ◽  
Satellite Observations ◽  
Sea Surface ◽  
Biogeochemical Models ◽  
Optical Module ◽  
Argo Profiles

Abstract. Given current threats to ocean ecosystem health, there is a growing demand for accurate biogeochemical hindcasts, nowcasts, and predictions. Provision of such products requires data assimilation, i.e., a comprehensive strategy for incorporating observations into biogeochemical models, but current data streams of biogeochemical observations are generally considered insufficient for the operational provision of such products. This study investigates to what degree the assimilation of satellite observations in combination with a priori model calibration by sparse BGC-Argo profiles can improve subsurface biogeochemical properties. The multivariate deterministic ensemble Kalman filter (DEnKF) has been implemented to assimilate physical and biological observations into a three-dimensional coupled physical–biogeochemical model, the biogeochemical component of which has been calibrated by BGC-Argo float data for the Gulf of Mexico. Specifically, observations of sea surface height, sea surface temperature, and surface chlorophyll were assimilated, and profiles of both physical and biological variables were updated based on the surface information. We assessed whether this leads to improved subsurface distributions, especially of biological properties, using observations from five BGC-Argo floats that were not assimilated. An alternative light parameterization that was tuned a priori using BGC-Argo observations was also applied to test the sensitivity of data assimilation impact on subsurface biological properties. Results show that assimilation of the satellite data improves model representation of major circulation features, which translate into improved three-dimensional distributions of temperature and salinity. The multivariate assimilation also improves the agreement of subsurface nitrate through its tight correlation with temperature, but the improvements in subsurface chlorophyll were modest initially due to suboptimal choices of the model's optical module. Repeating the assimilation run by using the alternative light parameterization greatly improved the subsurface distribution of chlorophyll. Therefore, even sparse BGC-Argo observations can provide substantial benefits for biogeochemical prediction by enabling a priori model tuning. Given that, so far, the abundance of BGC-Argo profiles in the Gulf of Mexico and elsewhere has been insufficient for sequential assimilation, updating 3D biological properties in a model that has been well calibrated is an intermediate step toward full assimilation of the new data types.

scholarly journals Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico

2021 ◽  
Author(s):  
Bin Wang ◽  
Katja Fennel ◽  
Liuqian Yu
Keyword(s):  
Gulf Of Mexico ◽  
A Priori ◽  
Biological Properties ◽  
Satellite Observations ◽  
Sea Surface ◽  
Biogeochemical Model ◽  
Biological Variables ◽  
Biogeochemical Models ◽  
Optical Module ◽  
Argo Profiles

Abstract. Given current threats to ocean ecosystem health, there is a growing demand for accurate biogeochemical hindcasts, nowcasts, and predictions. Provision of such products requires data assimilation, i.e., a comprehensive strategy for incorporating observations into biogeochemical models, but current data streams of biogeochemical observations are generally considered insufficient for the operational provision of such products. This study investigates to what degree the satellite observations in combination with sparse BGC Argo profiles can improve subsurface biogeochemical properties. The multivariate Deterministic Ensemble Kalman Filter (DEnKF) has been implemented to assimilate physical and biological observations into a biogeochemical model of the Gulf of Mexico. First, the biogeochemical model component was tuned using BGC-Argo observations. Then, observations of sea surface height, sea surface temperature, and surface chlorophyll were assimilated, and profiles of both physical and biological variables were updated based on the surface information. We assessed whether this leads to improved subsurface distributions, especially of biological properties, using observations from five BGC-Argo floats that were not assimilated, but used in the a priori tuning. Results show that assimilation of the satellite data improves model representation of major circulation features, which translate into improved three-dimensional distributions of temperature and salinity. The multivariate assimilation also improves the agreement of subsurface nitrate through its tight correlation with temperature, but the improvements in subsurface chlorophyll were modest initially due to suboptimal choices of the model’s optical module. Repeating the assimilation run after adjusting light attenuation parameterization through further a priori tuning greatly improved the subsurface distribution of chlorophyll. Therefore, even sparse BGC-Argo observations can provide substantial benefits to biogeochemical prediction by enabling a priori model tuning. Given that, so far, the abundance of BGC-Argo profiles in the Gulf of Mexico and elsewhere is insufficient for sequential assimilation, updating 3D biological properties in a model that has been well calibrated is an intermediate step toward full assimilation of the new data types.

scholarly journals Satellite observations of forest resilience to hurricanes along the northern Gulf of Mexico

2020 ◽  
Vol 472 ◽  
pp. 118243 ◽  
Author(s):  
Chengcheng Gang ◽  
Shufen Pan ◽  
Hanqin Tian ◽  
Zhuonan Wang ◽  
Rongting Xu ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Satellite Observations ◽  
Northern Gulf Of Mexico ◽  
Forest Resilience
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