Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles
Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles

Author: Michael Siek

Publisher: CRC Press

Published: 2011-12-16

Total Pages: 239

ISBN-13: 041562102X

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Accurate predictions of storm surge are of importance in many coastal areas in the world to avoid and mitigate its destructive impacts. For this purpose the physically-based (process) numerical models are typically utilized. However, in data-rich cases, one may use data-driven methods aiming at reconstructing the internal patterns of the modelled processes and relationships between the observed descriptive variables. This book focuses on data-driven modelling using methods of nonlinear dynamics and chaos theory. First, some fundamentals of physical oceanography, nonlinear dynamics and chaos, computational intelligence and European operational storm surge models are covered. After that a number of improvements in building chaotic models are presented: nonlinear time series analysis, multi-step prediction, phase space dimensionality reduction, techniques dealing with incomplete time series, phase error correction, finding true neighbours, optimization of chaotic model, data assimilation and multi-model ensemble prediction. The major case study is surge prediction in the North Sea, with some tests on a Caribbean Sea case. The modelling results showed that the enhanced predictive chaotic models can serve as an efficient tool for accurate and reliable short and mid-term predictions of storm surges in order to support decision-makers for flood prediction and ship navigation.

Deep Learning in Multi-step Prediction of Chaotic Dynamics
Deep Learning in Multi-step Prediction of Chaotic Dynamics

Author: Matteo Sangiorgio

Publisher: Springer Nature

Published: 2022-02-14

Total Pages: 111

ISBN-13: 3030944824

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The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.

Statistical Postprocessing of Ensemble Forecasts
Statistical Postprocessing of Ensemble Forecasts

Author: Stéphane Vannitsem

Publisher: Elsevier

Published: 2018-05-17

Total Pages: 364

ISBN-13: 012812248X

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Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner

Flood Forecasting Using Machine Learning Methods
Flood Forecasting Using Machine Learning Methods

Author: Fi-John Chang

Publisher: MDPI

Published: 2019-02-28

Total Pages: 376

ISBN-13: 3038975486

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Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.

Artificial Intelligence Methods in the Environmental Sciences
Artificial Intelligence Methods in the Environmental Sciences

Author: Sue Ellen Haupt

Publisher: Springer Science & Business Media

Published: 2008-11-28

Total Pages: 418

ISBN-13: 1402091192

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How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Thriving on Our Changing Planet
Thriving on Our Changing Planet

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

Published: 2019-01-20

Total Pages: 717

ISBN-13: 0309467578

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We live on a dynamic Earth shaped by both natural processes and the impacts of humans on their environment. It is in our collective interest to observe and understand our planet, and to predict future behavior to the extent possible, in order to effectively manage resources, successfully respond to threats from natural and human-induced environmental change, and capitalize on the opportunities â€" social, economic, security, and more â€" that such knowledge can bring. By continuously monitoring and exploring Earth, developing a deep understanding of its evolving behavior, and characterizing the processes that shape and reshape the environment in which we live, we not only advance knowledge and basic discovery about our planet, but we further develop the foundation upon which benefits to society are built. Thriving on Our Changing Planet presents prioritized science, applications, and observations, along with related strategic and programmatic guidance, to support the U.S. civil space Earth observation program over the coming decade.

Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting
Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting

Author: Lance Bosart

Publisher: Springer Science & Business Media

Published: 2013-01-06

Total Pages: 426

ISBN-13: 0933876688

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This long-anticipated monograph honoring scientist and teacher Fred Sanders includes 16 articles by various authors as well as dozens of unique photographs evoking Fred's character and the vitality of the scientific community he helped develop through his work. Editors Lance F. Bosart (University at Albany/SUNY) and Howard B. Bluestein (University of Oklahoma at Norman) have brought together contributions from luminary authors-including Kerry Emanuel, Robert Burpee, Edward Kessler, and Louis Uccellini-to honor Fred's work in the fields of forecasting, weather analysis, synoptic meteorology, and climatology. The result is a significant volume of work that represents a lasting record of Fred Sanders' influence on atmospheric science and legacy of teaching.

Understanding and Predicting the Gulf of Mexico Loop Current
Understanding and Predicting the Gulf of Mexico Loop Current

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

Published: 2018-05-23

Total Pages: 117

ISBN-13: 0309462207

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One of the most significant, energetic, yet not well understood, oceanographic features in the Americas is the Gulf of Mexico Loop Current System (LCS), consisting of the Loop Current (LC) and the Loop Current Eddies (LCEs) it sheds. Understanding the dynamics of the LCS is fundamental to understanding the Gulf of Mexico's full oceanographic system, and vice versa. Hurricane intensity, offshore safety, harmful algal blooms, oil spill response, the entire Gulf food chain, shallow water nutrient supply, the fishing industry, tourism, and the Gulf Coast economy are all affected by the position, strength, and structure of the LC and associated eddies. This report recommends a strategy for addressing the key gaps in general understanding of LCS processes, in order to instigate a significant improvement in predicting LC/LCE position, evolving structure, extent, and speed, which will increase overall understanding of Gulf of Mexico circulation and to promote safe oil and gas operations and disaster response in the Gulf of Mexico. This strategy includes advice on how to design a long-term observational campaign and complementary data assimilation and numerical modeling efforts.

Hydrological Data Driven Modelling
Hydrological Data Driven Modelling

Author: Renji Remesan

Publisher: Springer

Published: 2014-11-03

Total Pages: 261

ISBN-13: 3319092359

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This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

A Vast Machine
A Vast Machine

Author: Paul N. Edwards

Publisher: MIT Press

Published: 2010-03-12

Total Pages: 547

ISBN-13: 0262290715

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The science behind global warming, and its history: how scientists learned to understand the atmosphere, to measure it, to trace its past, and to model its future. Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, “sound science.” In A Vast Machine Paul Edwards has news for these skeptics: without models, there are no data. Today, no collection of signals or observations—even from satellites, which can “see” the whole planet with a single instrument—becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models. Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere—to measure it, trace its past, and model its future.