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  • 1
    Call number: AWI A13-01-0151
    In: Applied mathematical sciences
    Type of Medium: Monograph available for loan
    Pages: XII, 485 S.
    ISBN: 0387964754
    Series Statement: Applied mathematical sciences 60
    Branch Library: AWI Library
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  • 2
    Call number: 5/M 16.89607
    In: Geophysical monograph
    Description / Table of Contents: Home / Earth, Space & Environmental Sciences / Geology & Geophysics / Geology & Geophysics Extreme Events: Observations, Modeling, and Economics Mario Chavez (Editor), Michael Ghil (Editor), Jaime Urrutia-Fucugauchi (Editor) ISBN: 978-1-119-15701-4 438 pages March 2016, Wiley-Blackwell Extreme Events: Observations, Modeling, and Economics (1119157013) cover image Read an Excerpt Description The monograph covers the fundamentals and the consequences of extreme geophysical phenomena like asteroid impacts, climatic change, earthquakes, tsunamis, hurricanes, landslides, volcanic eruptions, flooding, and space weather. This monograph also addresses their associated, local and worldwide socio-economic impacts. The understanding and modeling of these phenomena is critical to the development of timely worldwide strategies for the prediction of natural and anthropogenic extreme events, in order to mitigate their adverse consequences. This monograph is unique in as much as it is dedicated to recent theoretical, numerical and empirical developments that aim to improve: (i) the understanding, modeling and prediction of extreme events in the geosciences, and, (ii) the quantitative evaluation of their economic consequences. The emphasis is on coupled, integrative assessment of the physical phenomena and their socio-economic impacts. With its overarching theme, Extreme Events: Observations, Modeling and Economics will be relevant to and become an important tool for researchers and practitioners in the fields of hazard and risk analysis in general, as well as to those with a special interest in climate change, atmospheric and oceanic sciences, seismo-tectonics, hydrology, and space weather.
    Type of Medium: Monograph available for loan
    ISBN: 9781119157014
    Series Statement: Geophysical monograph series 214
    Classification: B.
    Branch Library: GFZ Library
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  • 3
    Call number: M 92.0464 ; AWI A17-97-0414
    In: Proceedings of the International School of Physics Enrico Fermi
    Type of Medium: Monograph available for loan
    Pages: XXI, 449 S.
    ISBN: 0444869360
    Series Statement: Proceedings of the International School of Physics "Enrico Fermi" course 88
    Classification: A.2.10.
    Language: English
    Location: Upper compact magazine
    Location: Upper compact magazine
    Branch Library: GFZ Library
    Branch Library: AWI Library
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  • 4
    Publication Date: 2019-01-25
    Description: The role that dynamics plays in estimating the state of the atmosphere from incomplete and noisy data is reviewed. Objective analysis represents an attempt at relying mostly on the data and minimizing the role of dynamics in the estimation. Data assimilation tries to balance properly the roles of dynamical and observational information. Sequential estimation is presented as the proper framework for understanding this balance, and the Kalman filter as the ideal, optimal procedure for data assimilation. The optimal filter computes forecast error covariances of a given atmospheric model exactly, and hence data assimilation should be closely connected with predictability studies. This connection is described, and consequences drawn for currently active areas of the atmospheric and related sciences, namely, mesoscale meteorology, long range forecasting, and upper ocean dynamics. Possibilities offered by judicious data assimilation in understanding barotropic adjustment, a phenomenon that appears to play a crucial role in atmospheric behavior on the scale of weeks to months, and hence in long range forecasting are addressed.
    Keywords: METEOROLOGY AND CLIMATOLOGY
    Type: NAS-NRC Proceedings of the First National Workshop on the Global Weather Experiment, Vol. 2, Pt. 2; p 794-802
    Format: text
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  • 5
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    In:  Other Sources
    Publication Date: 2018-06-08
    Description: This work aims at identifying possible variations of the Earth's climate over the last century.
    Type: Journal of American Statistical Associates
    Format: text
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  • 6
    Publication Date: 2018-06-08
    Description: The full nonlinear Kalman filter sequential algorithm is, in theory, well-suited to the four-dimensional data assimilation problem in large-scale atmospheric and oceanic problems. However, it was later discovered that this algorithm can be very sensitive to computer roundoff, and that results may cease to be meaningful as time advances. Implementations of a modified Kalman filter are given.
    Format: text
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  • 7
    Publication Date: 2018-06-11
    Description: Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm.
    Keywords: Statistics and Probability
    Type: Monthly Weather Review; Volume 135; 186-202
    Format: text
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  • 8
    Publication Date: 2018-06-08
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  • 9
    Publication Date: 2018-06-08
    Description: Variability of the total solar irradiance (TSI) is a ptential contributor to changes in global mean temperature on time scales longer than the relaxation time of the upper ocean (i.e. a few years).
    Format: text
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  • 10
    Publication Date: 2018-06-08
    Description: Variability of the total solar irradiance is a potential contributor to changes in global mean temperature on time scales longer than the relaxation time of the upper ocean.
    Format: text
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