Download or read book Applications of Regional Ocean Ensemble Kalman Filter Data Assimilation written by Yi Li and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book On the Application of Data Assimilation in Regional Coastal Models written by Rafael Cañizares and published by CRC Press. This book was released on 2021-02-28 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work deals with the integration of sequential data assimilation techniques in regional coastal numerical models. Two suboptimal schemes of the Kalman filter are described in detail, both of which can approximate the results of the Kalman filter but at a much lower cost.
Download or read book Data Assimilation for Atmospheric Oceanic and Hydrologic Applications Vol II written by Seon Ki Park and published by Springer Science & Business Media. This book was released on 2013-05-22 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Download or read book Data Assimilation written by Geir Evensen and published by Springer Science & Business Media. This book was released on 2006-12-22 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.
Download or read book Data Assimilation for Atmospheric Oceanic and Hydrologic Applications Vol III written by Seon Ki Park and published by Springer. This book was released on 2016-12-26 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Download or read book Stochastic Processes and Filtering Theory written by Andrew H. Jazwinski and published by Courier Corporation. This book was released on 2013-04-15 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well. Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probability theory and stochastic processes, the author introduces and defines the problems of filtering, prediction, and smoothing. He presents the mathematical solutions to nonlinear filtering problems, and he specializes the nonlinear theory to linear problems. The final chapters deal with applications, addressing the development of approximate nonlinear filters, and presenting a critical analysis of their performance.
Download or read book Data Assimilation Methods Algorithms and Applications written by Mark Asch and published by SIAM. This book was released on 2016-12-29 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data assimilation is an approach that combines observations and model output, with the objective of improving the latter. This book places data assimilation into the broader context of inverse problems and the theory, methods, and algorithms that are used for their solution. It provides a framework for, and insight into, the inverse problem nature of data assimilation, emphasizing why and not just how. Methods and diagnostics are emphasized, enabling readers to readily apply them to their own field of study. Readers will find a comprehensive guide that is accessible to nonexperts; numerous examples and diverse applications from a broad range of domains, including geophysics and geophysical flows, environmental acoustics, medical imaging, mechanical and biomedical engineering, economics and finance, and traffic control and urban planning; and the latest methods for advanced data assimilation, combining variational and statistical approaches.
Download or read book Tests of an Ensemble Kalman Filter for Mesoscale and Regional scale Data Assimilation written by Zhiyong Meng and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation examines the performance of an ensemble Kalman filter (EnKF) implemented in a mesoscale model in increasingly realistic contexts from under a perfect model assumption and in the presence of significant model error with synthetic observations to real-world data assimilation in comparison to the three-dimensional variational (3DVar) method via both case study and month-long experiments. The EnKF is shown to be promising for future application in operational data assimilation practice. The EnKF with synthetic observations, which is implemented in the mesoscale model MM5, is very effective in keeping the analysis close to the truth under the perfect model assumption. The EnKF is most effective in reducing larger-scale errors but less effective in reducing errors at smaller, marginally resolvable scales. In the presence of significant model errors from physical parameterization schemes, the EnKF performs reasonably well though sometimes it can be significantly degraded compared to its performance under the perfect model assumption. Using a combination of different physical parameterization schemes in the ensemble (the so-called "multi-scheme" ensemble) can significantly improve filter performance due to the resulting better background error covariance and a smaller ensemble bias. The EnKF performs differently for different flow regimes possibly due to scale- and flow-dependent error growth dynamics and predictability. Real-data (including soundings, profilers and surface observations) are assimilated by directly comparing the EnKF and 3DVar and both are implemented in the Weather Research and Forecasting model. A case study and month-long experiments show that the EnKF is efficient in tracking observations in terms of both prior forecast and posterior analysis. The EnKF performs consistently better than 3DVar for the time period of interest due to the benefit of the EnKF from both using ensemble mean for state estimation and using a flow-dependent background error covariance. Proper covariance inflation and using a multi-scheme ensemble can significantly improve the EnKF performance. Using a multi-scheme ensemble results in larger improvement in thermodynamic variables than in other variables. The 3DVar system can benefit substantially from using a short-term ensemble mean for state estimate. Noticeable improvement is also achieved in 3DVar by including some flow dependence in its background error covariance.
Download or read book Nonlinear Data Assimilation written by Peter Jan Van Leeuwen and published by Springer. This book was released on 2015-07-22 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.
Download or read book Data Assimilation written by Pierre P. Brasseur and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data assimilation is considered a key component of numerical ocean model development and new data acquisition strategies. The basic concept of data assimilation is to combine real observations via estimation theory with dynamic models. Related methodologies exist in meteorology, geophysics and engineering. Of growing importance in physical oceanography, data assimilation can also be exploited in biological and chemical oceanography. Such techniques are now recognized as essential to understand the role of the ocean in a global change perspective. The book focuses on data processing algorithms for assimilation, current methods for the assimilation of biogeochemical data, strategy of model development, and the design of observational data for assimilation.
Download or read book Kalman Filters written by Ginalber Luiz Serra and published by BoD – Books on Demand. This book was released on 2018-02-21 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent issues on theory and practice of Kalman filters, with a comprehensive treatment of a selected number of concepts, techniques, and advanced applications. From an interdisciplinary point of view, the contents from each chapter bring together an international scientific community to discuss the state of the art on Kalman filter-based methodologies for adaptive/distributed filtering, optimal estimation, dynamic prediction, nonstationarity, robot navigation, global navigation satellite systems, moving object tracking, optical communication systems, and active power filters, among others. The theoretical and methodological foundations combined with extensive experimental explanation make this book a reference suitable for students, practicing engineers, and researchers in sciences and engineering.
Download or read book Data Assimilation written by Geir Evensen and published by Springer Verlag. This book was released on 2007 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. In particular, this webpage contains a complete ensemble Kalman filter assimilation system, which forms an ideal starting point for a user who wants to implement the ensemble Kalman filter with his/her own dynamical model. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and thegeneral properties of the ensemble algorithms, is available here for the first time.
Download or read book Inverse Problem Theory and Methods for Model Parameter Estimation written by Albert Tarantola and published by SIAM. This book was released on 2005-01-01 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: While the prediction of observations is a forward problem, the use of actual observations to infer the properties of a model is an inverse problem. Inverse problems are difficult because they may not have a unique solution. The description of uncertainties plays a central role in the theory, which is based on probability theory. This book proposes a general approach that is valid for linear as well as for nonlinear problems. The philosophy is essentially probabilistic and allows the reader to understand the basic difficulties appearing in the resolution of inverse problems. The book attempts to explain how a method of acquisition of information can be applied to actual real-world problems, and many of the arguments are heuristic.
Download or read book Atmospheric Data Analysis written by Roger Daley and published by Cambridge University Press. This book was released on 1993-11-26 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended to fill a void in the atmospheric science literature, this self-contained text outlines the physical and mathematical basis of all aspects of atmospheric analysis as well as topics important in several other fields outside of it, including atmospheric dynamics and statistics.
Download or read book Ocean Data Assimilation Guidance Using Uncertainty Forecasts written by and published by . This book was released on 2010 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses preliminary tests on using predicted forecast errors to estimate the impact of observations in correcting the Naval Research Laboratory (NRI.) tide resolving, high resolution regional version of the Navy Coastal Ocean Model (RNCOM) assimilating local observations processed through the NRI. Coupled Ocean Data Assimilation (NCODA) system. Since there will always be a shortfall of data to constraint all sources of uncertainty there is an obvious advantage to optimally guide observations to reduce model errors that could be producing the most negative impacts. The importance of this topic has been further heightened in oceanic applications by the advent of Underwater Automated Vehicles (UAVs) that can bring persistent observations but need to be told where to go and when, following regular schedules. This works tests a technique named the Ensemble Transform Kalman Filter (ETKF) that can be used to automate such adaptive sampling guidance and has been successfully applied for atmospheric modeling optimization. The ETKF uses an ensemble of state-fields from a certain initialization time and rapid low rank solutions of the Kalman filter equations to estimate integrated predicted error reduction for selected target ensemble variables, or combinations of variables, over areas and forecast ranges of interest. The error estimates are produced through independent RNCOM runs using perturbed forcing and initial conditions constrained at each analysis time by new estimates of the analysis errors as provided by NCODA, using a technique named Ensemble Transform (ET).
Download or read book Ensemble Data Assimilation and Breeding in the Ocean Chesapeake Bay and Mars written by and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: My dissertation focuses on studying instabilities of different time scales using breeding and data assimilation in the oceans, as well as the Martian atmosphere. The breeding method of Toth and Kalnay finds the perturbations that grow naturally in a dynamical system like the atmosphere or the ocean. Here breeding is applied to a global ocean model forced by reanalysis winds in order to identify instabilities on weekly and monthly timescales. The method is extended to show how the energy equations for the bred vectors can be derived with only very minimal approximations and used to assess the physical mechanisms that give rise to the instabilities. Tropical Instability Waves in the tropical Pacific are diagnosed, confirming the existence of bands of both baroclinic and barotropic energy conversions indicated by earlier studies. For regional prediction of smaller timescale phenomena, an advanced data assimilation system has been developed for the Chesapeake Bay Forecast System, a regional Earth System Prediction model. To accomplish this, the Regional Ocean Modeling System (ROMS) implementation on the Chesapeake Bay has been interfaced with the Local Ensemble Transform Kalman Filter (LETKF). The LETKF is among the most advanced data assimilation methods and is very effective for large, non-linear dynamical systems in both sparse and dense data coverage situations. In perfect model experiments using ChesROMS, the filter converges quickly and reduces the analysis and subsequent forecast errors in the temperature, salinity, and velocity fields. This error reduction has proved fairly robust to sensitivity studies such as reduced data coverage and realistic data coverage experiments. The LETKF also provides a method for error estimation and facilitates the investigation of the spatial distribution of the error. This information has been used to determine areas where more monitoring is needed. The LETKF framework is also applied here to a global model of the Martian atmosph
Download or read book Design and Implementation of a Parallel Multivariate Ensemble Kalman Filter for the Poseidon Ocean General Circulation Model written by and published by . This book was released on 2001 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
