Ocean Data Assimilation Guidance Using Uncertainty Forecasts PDF Download
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Author | : |
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Total Pages | : 9 |
Release | : 2010 |
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Download Ocean Data Assimilation Guidance Using Uncertainty Forecasts Book in PDF, ePub and Kindle
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).
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Publisher | : |
Total Pages | : 15 |
Release | : 2010 |
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Download Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean Book in PDF, ePub and Kindle
Error Subspace Statistical Estimation (ESSE), the uncertainty prediction and data assimilation methodology employed for real-time ocean forecasts, is based on a characterization and prediction of the largest uncertainties. This is carried out by evolving an error subspace of variable size. We use an ensemble of stochastic model simulations, initialized based on an estimate of the dominant initial uncertainties, to predict the error subspace of the model fields. The dominant error covariance (generated via an SVD of the ensemble generated error covariance matrix) is used for data assimilation. The resulting ocean fields are provided as the input to acoustic modeling, allowing for the prediction and study of the spatiotemporal variations in acoustic propagation. The ESSE procedure is a classic case of Many Task Computing: These codes are managed based on dynamic workflows for (1) the perturbation of the initial mean state, (2) the subsequent ensemble of stochastic PE model runs, (3) the continuous generation of the covariance matrix, (4) the successive computations of the SVD of the ensemble spread until a convergence criterion is satisfied, and (5) the data assimilation. Its ensemble nature makes it a many task data intensive application and its dynamic workflow gives it heterogeneity. Subsequent acoustics propagation modeling involves a very large ensemble of very short in duration acoustics runs. We study the execution characteristics and challenges of a distributed ESSE workflow on a large dedicated cluster and the usability of enhancing this with runs on Amazon EC2 and the Teragrid and the I/O challenges faced.
Author | : Shiqiu Peng |
Publisher | : Frontiers Media SA |
Total Pages | : 295 |
Release | : 2023-12-01 |
Genre | : Science |
ISBN | : 2832540600 |
Download Advances in Ocean Data Assimilation: Methodologies, Forecasting and Reanalysis Book in PDF, ePub and Kindle
Author | : P. Malanotte-Rizzoli |
Publisher | : Elsevier |
Total Pages | : 469 |
Release | : 1996-05-10 |
Genre | : Science |
ISBN | : 0080536662 |
Download Modern Approaches to Data Assimilation in Ocean Modeling Book in PDF, ePub and Kindle
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.
Author | : Jacob Peter Heuss |
Publisher | : |
Total Pages | : 99 |
Release | : 2021 |
Genre | : Dissertations, Academic |
ISBN | : |
Download Reduced Order Modeling for Stochastic Prediction and Data Assimilation Onboard Autonomous Platforms at Sea Book in PDF, ePub and Kindle
There are many significant challenges for unmanned autonomous platforms at sea including predicting the likely scenarios for the ocean environment, quantifying regional uncertainties, and updating forecasts of the evolving dynamics using their observations. Due to the operational constraints such as onboard power, memory, bandwidth, and space limitations, efficient adaptive reduced order models (ROMs) are needed for onboard predictions. In the first part, several reduced order modeling schemes for regional ocean forecasting onboard autonomous platforms at sea are described, investigated, and evaluated. We find that Dynamic Mode Decomposition (DMD), a data-driven dimensionality reduction algorithm, can be used for accurate predictions for short periods in ocean environments. We evaluate DMD methods for ocean PE simulations by comparing and testing several schemes including domain splitting, adjusting training size, and utilizing 3D inputs. Three new approaches that combine uncertainty with DMD are also investigated and found to produce practical and accurate results, especially if we employ either an ensemble of DMD forecasts or the DMD of an ensemble of forecasts. We also demonstrate some results from projecting / compressing high-fidelity forecasts using schemes such as POD projection and K-SVD for sparse representation due to showing promise for distributing forecasts efficiently to remote vehicles. In the second part, we combine DMD methods with the GMM-DO filter to produce DMD forecasts with Bayesian data assimilation that can quickly and efficiently be computed onboard an autonomous platform. We compare the accuracy of our results to traditional DMD forecasts and DMD with Ensemble Kalman Filter (EnKF) forecast results and show that in Root Mean Square Error (RMSE) sense as well as error field sense, that the DMD with GMM-DO errors are smaller and the errors grow slower in time than the other mentioned schemes. We also showcase the DMD of the ensemble method with GMM-DO. We conclude that due to its accurate and computationally efficient results, it could be readily applied onboard autonomous platforms. Overall, our contributions developed and integrated stochastic DMD forecasts and efficient Bayesian GMM-DO updates of the DMD state and parameters, learning from the limited gappy observation data sets.
Author | : Eric P. Chassignet |
Publisher | : Springer Science & Business Media |
Total Pages | : 600 |
Release | : 2006-02-02 |
Genre | : Science |
ISBN | : 9781402039812 |
Download Ocean Weather Forecasting Book in PDF, ePub and Kindle
This volume covers a wide range of topics and summarizes our present knowledge in ocean modeling, ocean observing systems, and data assimilation. The Global Ocean Data Assimilation Experiment (GODAE) provides a framework for these efforts: a global system of observations, communications, modeling, and assimilation that will deliver regular, comprehensive information on the state of the oceans, engendering wide utility and availability for maximum benefit to the community.
Author | : Pierre P. Brasseur |
Publisher | : Springer Science & Business Media |
Total Pages | : 303 |
Release | : 2013-06-29 |
Genre | : Science |
ISBN | : 3642789390 |
Download Data Assimilation Book in PDF, ePub and Kindle
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.
Author | : Sebastian Reich |
Publisher | : Cambridge University Press |
Total Pages | : 308 |
Release | : 2015-05-14 |
Genre | : Computers |
ISBN | : 1316299422 |
Download Probabilistic Forecasting and Bayesian Data Assimilation Book in PDF, ePub and Kindle
In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas.
Author | : Leo H. Holthuijsen |
Publisher | : Cambridge University Press |
Total Pages | : 9 |
Release | : 2010-02-04 |
Genre | : Science |
ISBN | : 1139462520 |
Download Waves in Oceanic and Coastal Waters Book in PDF, ePub and Kindle
Waves in Oceanic and Coastal Waters describes the observation, analysis and prediction of wind-generated waves in the open ocean, in shelf seas, and in coastal regions with islands, channels, tidal flats and inlets, estuaries, fjords and lagoons. Most of this richly illustrated book is devoted to the physical aspects of waves. After introducing observation techniques for waves, both at sea and from space, the book defines the parameters that characterise waves. Using basic statistical and physical concepts, the author discusses the prediction of waves in oceanic and coastal waters, first in terms of generalised observations, and then in terms of the more theoretical framework of the spectral energy balance. He gives the results of established theories and also the direction in which research is developing. The book ends with a description of SWAN (Simulating Waves Nearshore), the preferred computer model of the engineering community for predicting waves in coastal waters.
Author | : Haraldur Olafsson |
Publisher | : Elsevier |
Total Pages | : 366 |
Release | : 2020-11-25 |
Genre | : Computers |
ISBN | : 0128157100 |
Download Uncertainties in Numerical Weather Prediction Book in PDF, ePub and Kindle
Uncertainties in Numerical Weather Prediction is a comprehensive work on the most current understandings of uncertainties and predictability in numerical simulations of the atmosphere. It provides general knowledge on all aspects of uncertainties in the weather prediction models in a single, easy to use reference. The book illustrates particular uncertainties in observations and data assimilation, as well as the errors associated with numerical integration methods. Stochastic methods in parameterization of subgrid processes are also assessed, as are uncertainties associated with surface-atmosphere exchange, orographic flows and processes in the atmospheric boundary layer. Through a better understanding of the uncertainties to watch for, readers will be able to produce more precise and accurate forecasts. This is an essential work for anyone who wants to improve the accuracy of weather and climate forecasting and interested parties developing tools to enhance the quality of such forecasts. Provides a comprehensive overview of the state of numerical weather prediction at spatial scales, from hundreds of meters, to thousands of kilometers Focuses on short-term 1-15 day atmospheric predictions, with some coverage appropriate for longer-term forecasts Includes references to climate prediction models to allow applications of these techniques for climate simulations