Sampling, feasibility, and priors in data assimilation
نویسندگان
چکیده
منابع مشابه
Sampling, Feasibility, and Priors in Data Assimilation
Importance sampling algorithms are discussed in detail, with an emphasis on implicit sampling, and applied to data assimilation via particle filters. Implicit sampling makes it possible to use the data to find high-probability samples at relatively low cost, making the assimilation more efficient. A new analysis of the feasibility of data assimilation is presented, showing in detail why feasibi...
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For efficient progress, model properties and measurement needs can adapt to oceanic events and interactions as they occur. The combination of models and data via data assimilation can also be adaptive. These adaptive concepts are discussed and exemplified within the context of comprehensive real-time ocean observing and prediction systems. Novel adaptive modeling approaches based on simplified ...
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Data Assimilation in operational models like atmospheric or Ocean models is almost impossible without posing many assumptions due to the complication of the model that is usually very high-dimensional and also due to non-linearity of the observation operator used to map the state space to the measurement space. Ensemble Kalman filter (EnKF) is the most popular ensemble-based data assimilation a...
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ژورنال
عنوان ژورنال: Discrete and Continuous Dynamical Systems
سال: 2016
ISSN: 1078-0947
DOI: 10.3934/dcds.2016.36.4227