Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements

The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D ( x , y , z ) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D ( x , y ) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ° C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Champenois, Bianca [verfasserIn] Sapsis, Themistoklis [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Ocean temperature Temporal convolutional network Uncertainty quantification

Übergeordnetes Werk:	Enthalten in: Physica / D - Amsterdam [u.a.] : Elsevier, 1980, 459
Übergeordnetes Werk:	volume:459

DOI / URN:	10.1016/j.physd.2023.134026

Katalog-ID:	ELV066746531

Internformat


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100	1		\|a Champenois, Bianca \|e verfasserin \|0 (orcid)0000-0002-3922-3055 \|4 aut
245	1	0	\|a Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements
264		1	\|c 2023
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520			\|a The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D ( x , y , z ) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D ( x , y ) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ° C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making.
650		4	\|a Ocean temperature
650		4	\|a Temporal convolutional network
650		4	\|a Uncertainty quantification
700	1		\|a Sapsis, Themistoklis \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.physd.2023.134026 doi (DE-627)ELV066746531 (ELSEVIER)S0167-2789(23)00380-9 DE-627 ger DE-627 rda eng 530 VZ 30.20 bkl 33.25 bkl 31.00 bkl Champenois, Bianca verfasserin (orcid)0000-0002-3922-3055 aut Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D ( x , y , z ) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D ( x , y ) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ° C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making. Ocean temperature Temporal convolutional network Uncertainty quantification Sapsis, Themistoklis verfasserin aut Enthalten in Physica / D Amsterdam [u.a.] : Elsevier, 1980 459 Online-Ressource (DE-627)266015166 (DE-600)1466587-6 (DE-576)074959867 1872-8022 nnns volume:459 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 Nichtlineare Dynamik VZ 33.25 Thermodynamik statistische Physik VZ 31.00 Mathematik: Allgemeines VZ AR 459
spelling	10.1016/j.physd.2023.134026 doi (DE-627)ELV066746531 (ELSEVIER)S0167-2789(23)00380-9 DE-627 ger DE-627 rda eng 530 VZ 30.20 bkl 33.25 bkl 31.00 bkl Champenois, Bianca verfasserin (orcid)0000-0002-3922-3055 aut Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D ( x , y , z ) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D ( x , y ) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ° C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making. Ocean temperature Temporal convolutional network Uncertainty quantification Sapsis, Themistoklis verfasserin aut Enthalten in Physica / D Amsterdam [u.a.] : Elsevier, 1980 459 Online-Ressource (DE-627)266015166 (DE-600)1466587-6 (DE-576)074959867 1872-8022 nnns volume:459 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 Nichtlineare Dynamik VZ 33.25 Thermodynamik statistische Physik VZ 31.00 Mathematik: Allgemeines VZ AR 459
allfields_unstemmed	10.1016/j.physd.2023.134026 doi (DE-627)ELV066746531 (ELSEVIER)S0167-2789(23)00380-9 DE-627 ger DE-627 rda eng 530 VZ 30.20 bkl 33.25 bkl 31.00 bkl Champenois, Bianca verfasserin (orcid)0000-0002-3922-3055 aut Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D ( x , y , z ) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D ( x , y ) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ° C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making. Ocean temperature Temporal convolutional network Uncertainty quantification Sapsis, Themistoklis verfasserin aut Enthalten in Physica / D Amsterdam [u.a.] : Elsevier, 1980 459 Online-Ressource (DE-627)266015166 (DE-600)1466587-6 (DE-576)074959867 1872-8022 nnns volume:459 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 Nichtlineare Dynamik VZ 33.25 Thermodynamik statistische Physik VZ 31.00 Mathematik: Allgemeines VZ AR 459
allfieldsGer	10.1016/j.physd.2023.134026 doi (DE-627)ELV066746531 (ELSEVIER)S0167-2789(23)00380-9 DE-627 ger DE-627 rda eng 530 VZ 30.20 bkl 33.25 bkl 31.00 bkl Champenois, Bianca verfasserin (orcid)0000-0002-3922-3055 aut Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D ( x , y , z ) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D ( x , y ) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ° C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making. Ocean temperature Temporal convolutional network Uncertainty quantification Sapsis, Themistoklis verfasserin aut Enthalten in Physica / D Amsterdam [u.a.] : Elsevier, 1980 459 Online-Ressource (DE-627)266015166 (DE-600)1466587-6 (DE-576)074959867 1872-8022 nnns volume:459 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 Nichtlineare Dynamik VZ 33.25 Thermodynamik statistische Physik VZ 31.00 Mathematik: Allgemeines VZ AR 459
allfieldsSound	10.1016/j.physd.2023.134026 doi (DE-627)ELV066746531 (ELSEVIER)S0167-2789(23)00380-9 DE-627 ger DE-627 rda eng 530 VZ 30.20 bkl 33.25 bkl 31.00 bkl Champenois, Bianca verfasserin (orcid)0000-0002-3922-3055 aut Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D ( x , y , z ) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D ( x , y ) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ° C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making. Ocean temperature Temporal convolutional network Uncertainty quantification Sapsis, Themistoklis verfasserin aut Enthalten in Physica / D Amsterdam [u.a.] : Elsevier, 1980 459 Online-Ressource (DE-627)266015166 (DE-600)1466587-6 (DE-576)074959867 1872-8022 nnns volume:459 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 Nichtlineare Dynamik VZ 33.25 Thermodynamik statistische Physik VZ 31.00 Mathematik: Allgemeines VZ AR 459
language	English
source	Enthalten in Physica / D 459 volume:459
sourceStr	Enthalten in Physica / D 459 volume:459
format_phy_str_mv	Article
bklname	Nichtlineare Dynamik Thermodynamik statistische Physik Mathematik: Allgemeines
institution	findex.gbv.de
topic_facet	Ocean temperature Temporal convolutional network Uncertainty quantification
dewey-raw	530
isfreeaccess_bool	false
container_title	Physica / D
authorswithroles_txt_mv	Champenois, Bianca @@aut@@ Sapsis, Themistoklis @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	266015166
dewey-sort	3530
id	ELV066746531
language_de	englisch
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author	Champenois, Bianca
spellingShingle	Champenois, Bianca ddc 530 bkl 30.20 bkl 33.25 bkl 31.00 misc Ocean temperature misc Temporal convolutional network misc Uncertainty quantification Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements
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topic_title	530 VZ 30.20 bkl 33.25 bkl 31.00 bkl Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements Ocean temperature Temporal convolutional network Uncertainty quantification
topic	ddc 530 bkl 30.20 bkl 33.25 bkl 31.00 misc Ocean temperature misc Temporal convolutional network misc Uncertainty quantification
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title	Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements
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title_full	Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements
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title_sort	machine learning framework for the real-time reconstruction of regional 4d ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements
title_auth	Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements
abstract	The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D ( x , y , z ) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D ( x , y ) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ° C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making.
abstractGer	The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D ( x , y , z ) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D ( x , y ) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ° C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making.
abstract_unstemmed	The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D ( x , y , z ) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D ( x , y ) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0 . 97 ° C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making.
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title_short	Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements
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Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements

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