DYNAMIC VISUALIZATION OF SPATIO-TEMPORAL PROCESS MODEL BASED ON NetCDF AND OPTIMAL INTERPOLATION FOR MARINE ENVIRONMENT

2020 ◽  
Vol 19 (11) ◽  
pp. 1957-1967
Author(s):  
Wang Xianghong ◽  
Xu Shenghua ◽  
Liu Jiping ◽  
Yang Yi ◽  
Luo An ◽  
...  
Keyword(s):  
Marine Environment ◽  
Process Model ◽  
Optimal Interpolation ◽  
Dynamic Visualization ◽  
Model Based ◽  
Temporal Process ◽  
Spatio Temporal

Reconstructing Sea Surface Dynamics Using a Linear Koopman Kalman Filter

2021 ◽  
Author(s):  
Said Ouala ◽  
Ronan Fablet ◽  
Ananda Pascual Pascual ◽  
Bertrand Chapron ◽  
Fabrice Collard ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Linear Representation ◽  
Sea Surface ◽  
Dynamical Model ◽  
Optimal Interpolation ◽  
Model Based ◽  
Trade Offs ◽  
Non Linear ◽  
Spatio Temporal

<p>Spatio-temporal interpolation applications are important in the context of ocean surface modeling. Current state-of-the-art techniques typically rely either on optimal interpolation or on model-based approaches which explicitly exploit a dynamical model. While the optimal interpolation suffers from smoothing issues making it unreliable in retrieving fine-scale variability, the selection and parametrization of a dynamical model, when considering model-based data assimilation strategies, remains a complex issue since several trade-offs between the model's complexity and its applicability in sea surface data assimilation need to be carefully addressed. For these reasons, deriving new data assimilation architectures that can perfectly exploit the observations and the current advances in signal processing, modeling and artificial intelligence is crucial.</p><p>In this work, we explore new advances in data-driven data assimilation to exploit the classical Kalman filter in the interpolation of spatio-temporal fields. The proposed algorithm is written in an end-to-end differentiable setting in order to allow for the learning of the linear dynamical model from a data assimilation cost. Furthermore, the linear model is formulated on a space of observables, rather than the space of observations, which allows for perfect replication of non-linear dynamics when considering periodic and quasi-periodic limit sets and providing a decent (short-term) forecast of chaotic ones. One of the main advantages of the proposed architecture is its simplicity since it utilises a linear representation coupled with a Kalman filter. Interestingly, our experiments show that exploiting such a linear representation leads to better data assimilation when compared to non-linear filtering techniques, on numerous applications, including the sea level anomaly reconstruction from satellite remote sensing observations.</p>

The Representation and Organizational of Geographical Spatio-temporal Process Case

2010 ◽  
Vol 11 (6) ◽  
pp. 845-853
Author(s):  
Xinzhong YANG ◽  
Yunyan DU ◽  
Fenzhen SU ◽  
Min JI ◽  
Lijing WANG
Keyword(s):  
Temporal Process ◽  
Spatio Temporal

Semantics of OWL-S process model based on temporal description logic

2013 ◽  
Vol 33 (1) ◽  
pp. 266-269 ◽  
Author(s):  
Ming LI ◽  
Shiyi LIU ◽  
Fuzhong NIAN
Keyword(s):  
Process Model ◽  
Description Logic ◽  
Model Based

The Evaluation of Efficient Process Model Based on Survey

2018 ◽  
Author(s):  
Thulasee Krishna Dr. S. ◽  
Sreekanth Dr.S. ◽  
Dharanidhar K. N.
Keyword(s):  
Process Model ◽  
Efficient Process ◽  
Model Based
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