Modern Approaches To Data Assimilation In Ocean Modeling

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Author: P. Malanotte-Rizzoli
Publisher: Elsevier
ISBN: 9780080536668
Size: 19.14 MB
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Modern Approaches To Data Assimilation In Ocean Modeling by P. Malanotte-Rizzoli


Original Title: Modern Approaches To Data Assimilation In Ocean Modeling

The field of oceanographic data assimilation is now well established. The main area of concern of oceanographic data assimilation is the necessity for systematic model improvement and ocean state estimation. In this respect, the book presents the newest, innovative applications combining the most sophisticated assimilation methods with the most complex ocean circulation models. Ocean prediction has also now emerged as an important area in itself. The book contains reviews of scientific oceanographic issues covering different time and space scales. The application of data assimilation methods can provide significant advances in the understanding of this subject. Also included are the first, recent developments in the forecasting of oceanic flows. Only original articles that have undergone full peer review are presented, to ensure the highest scientific quality. This work provides an excellent coverage of state-of-the-art oceanographic data assimilation.

Experimental Acoustic Inversion Methods For Exploration Of The Shallow Water Environment

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Author: Andrea Caiti
Publisher: Springer Science & Business Media
ISBN: 9401141126
Size: 41.22 MB
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Experimental Acoustic Inversion Methods For Exploration Of The Shallow Water Environment by Andrea Caiti


Original Title: Experimental Acoustic Inversion Methods For Exploration Of The Shallow Water Environment

In recent years, research on acoustic remote sensing of the ocean has evolved considerably, especially in studying complex physical and biological processes in shallow water environments. To review the state of the art, an international workshop was held at Carvoeiro, Portugal, in March 1999, bringing together leading international researchers in the field. In contrast to much of the recent theoretical work, emphasis was placed on the experimental validation of the techniques. This volume, based on presentations at this workshop, summarizes a range of diverse and innovative applications. The invited contributions explore the use of acoustics to measure bottom properties and morphology, as well as to probe buried objects within the sediment. Within the water column, sound is applied to imaging of oceanographic features such as currents and tides or monitoring of marine life. Another key theme is the use of sound to solve geometric inverse problems for precise tracking of undersea vehicles. Audience: This volume should be useful both to the novice seeking an introduction to the field and to advanced researchers interested in the latest developments in acoustic sensing of the ocean environment. The workshop was sponsored by the Fundação para a Ciêcia e a Tecnologia (Portuguese Foundation for Science and Technology).

Data Assimilation For Atmospheric Oceanic And Hydrologic Applications

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Author: Seon Ki Park
Publisher: Springer Science & Business Media
ISBN: 3642350887
Size: 79.52 MB
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Data Assimilation For Atmospheric Oceanic And Hydrologic Applications by Seon Ki Park


Original Title: Data Assimilation For Atmospheric Oceanic And Hydrologic Applications

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.

The Global Coastal Ocean

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Author: Kenneth H. Brink
Publisher: John Wiley & Sons Inc
ISBN:
Size: 40.76 MB
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The Global Coastal Ocean by Kenneth H. Brink


Original Title: The Global Coastal Ocean

This book is the first of a new sequence of Volumes of "The Sea" to be edited by Dr. Robinson and a new, distinguished editorial board. As oceanography has matured over the last decades, the previously separate interests of chemical, biological and physical oceanographers have converged, and these volumes will reflect the change in attitude in oceanography to a solid interdisciplinary approach. Other volumes planned for publication in 1998 and 1999 will include works on "Ocean History", "Biological-Physical Interactions in the Ocean", "Oceans and Climate", and "Ocean Science of Fisheries".

The Sea

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Author: Allan R. Robinson
Publisher: John Wiley & Sons Inc
ISBN: 9780471115434
Size: 72.11 MB
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The Sea by Allan R. Robinson


Original Title: The Sea

This book examines the general principles governing ocean phenomena in regions of shallow water near land. It emphasizes physical processes such as tested effects, global sea level changes, and sediment transport. Volume 10 examines the fundamental techniques and physical processes of oceanography and review the methodologies for both observational and modeling research. Volume 11 presents the results of 32 regional studies, including Eastern North America, the North Sea, the Mediterranean, and many more.

Biological Physical Interactions In The Sea

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Author: Allan R. Robinson
Publisher: Wiley
ISBN:
Size: 57.51 MB
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Biological Physical Interactions In The Sea by Allan R. Robinson


Original Title: Biological Physical Interactions In The Sea

This book is the third in a new sequence of volumes of The Sea to be edited by Dr. Allan Robinson and a new, distinguished editorial board. As oceanography has matured over recent decades, the once separate interests of chemical, biological, and physical oceanographers have converged. These volumes reflect the change in attitude in oceanography to a solid, interdisciplinary approach.

Adaptive Error Estimation In Linearized Ocean General Circulation Models

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Author: Michael Yurievich Chechelnitsky
Publisher:
ISBN:
Size: 76.98 MB
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Adaptive Error Estimation In Linearized Ocean General Circulation Models by Michael Yurievich Chechelnitsky


Original Title: Adaptive Error Estimation In Linearized Ocean General Circulation Models

Data assimilation methods, such as the Kalman filter, are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. In this study we address the problem of estimating model and measurement error statistics from observations. We start by testing the Myers and Tapley (1976, MT) method of adaptive error estimation with low-dimensional models. We then apply the MT method in the North Pacific (5°-60° N, 132°-252° E) to TOPEX/POSEIDON sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The MT method, closely related to the maximum likelihood methods of Belanger (1974) and Dee (1995), is shown to be sensitive to the initial guess for the error statistics and the type of observations. It does not provide information about the uncertainty of the estimates nor does it provide information about which structures of the error statistics can be estimated and which cannot. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). The CMA is both a powerful diagnostic tool for addressing theoretical questions and an efficient estimator for real data assimilation studies. It can be extended to estimate other statistics of the errors, trends, annual cycles, etc. Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. After removal of trends and annual cycles, the low frequency /wavenumber (periods> 2 months, wavelengths> 16°) TOPEX/POSEIDON sea level anomaly is of the order 6 cm2. The GCM explains about 40% of that variance. By covariance matching, it is estimated that 60% of the GCM-TOPEX/POSEIDON residual variance is consistent with the reduced state linear model. The CMA is then applied to TOPEX/POSEIDON sea level anomaly data and a linearization of a global GFDL GCM. The linearization, done in Fukumori et al.(1999), uses two vertical mode, the barotropic and the first baroclinic modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCMTOPEX/ POSEIDON residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large representation error, i.e. the dominance of the mesoscale eddies in the TIP signal, which are not part of the 20 by 10 GCM. Therefore, the impact of the observations on the assimilation is very small even after the adjustment of the error statistics. This work demonstrates that simultaneous estimation of the model and measurement error statistics for data assimilation with global ocean data sets and linearized GCMs is possible. However, the error covariance estimation problem is in general highly underdetermined, much more so than the state estimation problem. In other words there exist a very large number of statistical models that can be made consistent with the available data. Therefore, methods for obtaining quantitative error estimates, powerful though they may be, cannot replace physical insight. Used in the right context, as a tool for guiding the choice of a small number of model error parameters, covariance matching can be a useful addition to the repertory of tools available to oceanographers.

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