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Regional Ocean Data Assimilation
Annual Review of Marine Science, 2015This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions.
Edwards, Christopher A. +3 more
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2015
Data assimilation is a powerful technique which has been widely applied in investigations of the atmosphere, ocean, and land surface. It combines observation data and the underlying dynamical principles governing the system to provide an estimate of the state of the system which is better than could be obtained using just the data or the model alone ...
Zhihua Zhang, John C. Moore
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Data assimilation is a powerful technique which has been widely applied in investigations of the atmosphere, ocean, and land surface. It combines observation data and the underlying dynamical principles governing the system to provide an estimate of the state of the system which is better than could be obtained using just the data or the model alone ...
Zhihua Zhang, John C. Moore
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Data Learning: Integrating Data Assimilation and Machine Learning
Journal of Computer Science, 2021Caterina Buizza +13 more
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Introduction to Data Assimilation
2011The basic purpose of data assimilation is to combine different sources of information to estimate at best the state of a system. These sources generally are observations (data), a numerical model and error statistics. Why not simply use observations? First, because observations are sparse or partial in geophysics.
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Assimilation of Operational Data
2010In this chapter we focus on the observations that are available for operational, real-time applications in meteorology, i.e., for numerical weather prediction (NWP). Many in situ observations can be treated as point-wise measurements. Their influence on the analysis is expected to be localized and smoothed according to the specified background error ...
Jean-Noël Thépaut, Erik Andersson
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Quarterly Journal of the Royal Meteorological Society, 2005
AbstractAll data assimilation systems are affected by biases, caused by problems with the data, by approximations in the observation operators used to simulate the data, by limitations of the assimilating model, or by the assimilation methodology itself.
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AbstractAll data assimilation systems are affected by biases, caused by problems with the data, by approximations in the observation operators used to simulate the data, by limitations of the assimilating model, or by the assimilation methodology itself.
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Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review
Climate Dynamics, 2020Shaoqing Zhang +12 more
semanticscholar +1 more source
2009
Data assimilation—the set of techniques whereby information from observing systems and models is combined optimally—is rapidly becoming prominent for study of the Earth system, especially for climate predictions. This chapter presents the broad principles of data assimilation, details the main approaches (Bayesian methods, Optimal Interpolation, 3Dand ...
H. Loisel, C. Jamet
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Data assimilation—the set of techniques whereby information from observing systems and models is combined optimally—is rapidly becoming prominent for study of the Earth system, especially for climate predictions. This chapter presents the broad principles of data assimilation, details the main approaches (Bayesian methods, Optimal Interpolation, 3Dand ...
H. Loisel, C. Jamet
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Data Assimilation and Information
2010In this introductory chapter we provide an overview of the connection between the data assimilation methodology and the concept of information, whether embodied in observations or models. In this context, we provide a step by step introduction to the need for data assimilation, culminating in an easy to understand description of the data assimilation ...
Richard Ménard +2 more
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