Peter Webley – författare

Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 200. Trajectory-based (“Lagrangian”) atmospheric transport and dispersion modeling has gained in popularity and sophistication over the previous several decades. It is common practice now for researchers around the world to apply Lagrangian models to a wide spectrum of issues. Lagrangian Modeling of the Atmosphere is a comprehensive volume that includes sections on Lagrangian modeling theory, model applications, and tests against observations. Published by the American Geophysical Union as part of the Geophysical Monograph Series.

Comprehensive coverage of trajectory-based atmospheric dispersion modeling Important overview of a widely used modeling tool Sections look at modeling theory, application of models, and tests against observations

Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 200. Trajectory-based (“Lagrangian”) atmospheric transport and dispersion modeling has gained in popularity and sophistication over the previous several decades. It is common practice now for researchers around the world to apply Lagrangian models to a wide spectrum of issues. Lagrangian Modeling of the Atmosphere is a comprehensive volume that includes sections on Lagrangian modeling theory, model applications, and tests against observations. Published by the American Geophysical Union as part of the Geophysical Monograph Series.

Comprehensive coverage of trajectory-based atmospheric dispersion modeling Important overview of a widely used modeling tool Sections look at modeling theory, application of models, and tests against observations

Uncertainties are pervasive in natural hazards, and it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties to inform modeling efforts.  In this monograph we provide a broad, cross-disciplinary overview of issues relating to uncertainties faced in natural hazard and risk assessment.  We introduce some basic tenets of uncertainty analysis, discuss issues related to communication and decision support, and offer numerous examples of analyses and modeling approaches that vary by context and scope.  Contributors include scientists from across the full breath of the natural hazard scientific community, from those in real-time analysis of natural hazards to those in the research community from academia and government.  Key themes and highlights include: Substantial breadth and depth of analysis in terms of the types of natural hazards addressed, the disciplinary perspectives represented, and the number of studies includedTargeted, application-centered analyses with a focus on development and use of modeling techniques to address various sources of uncertaintyEmphasis on the impacts of climate change on natural hazard processes and outcomesRecommendations for cross-disciplinary and science transfer across natural hazard sciencesThis volume will be an excellent resource for those interested in the current work on uncertainty classification/quantification and will document common and emergent research themes to allow all to learn from each other and build a more connected but still diverse and ever growing community of scientists.Read an interview with the editors to find out more:https://eos.org/editors-vox/reducing-uncertainty-in-hazard-prediction

Uncertainties are pervasive in natural hazards, and it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties to inform modeling efforts.  In this monograph we provide a broad, cross-disciplinary overview of issues relating to uncertainties faced in natural hazard and risk assessment.  We introduce some basic tenets of uncertainty analysis, discuss issues related to communication and decision support, and offer numerous examples of analyses and modeling approaches that vary by context and scope.  Contributors include scientists from across the full breath of the natural hazard scientific community, from those in real-time analysis of natural hazards to those in the research community from academia and government.  Key themes and highlights include:

Substantial breadth and depth of analysis in terms of the types of natural hazards addressed, the disciplinary perspectives represented, and the number of studies included Targeted, application-centered analyses with a focus on development and use of modeling techniques to address various sources of uncertainty Emphasis on the impacts of climate change on natural hazard processes and outcomes Recommendations for cross-disciplinary and science transfer across natural hazard sciences

This volume will be an excellent resource for those interested in the current work on uncertainty classification/quantification and will document common and emergent research themes to allow all to learn from each other and build a more connected but still diverse and ever growing community of scientists.Read an interview with the editors to find out more:https://eos.org/editors-vox/reducing-uncertainty-in-hazard-prediction

Uncertainties are pervasive in natural hazards, and it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties to inform modeling efforts.  In this monograph we provide a broad, cross-disciplinary overview of issues relating to uncertainties faced in natural hazard and risk assessment.  We introduce some basic tenets of uncertainty analysis, discuss issues related to communication and decision support, and offer numerous examples of analyses and modeling approaches that vary by context and scope.  Contributors include scientists from across the full breath of the natural hazard scientific community, from those in real-time analysis of natural hazards to those in the research community from academia and government.  Key themes and highlights include:

Substantial breadth and depth of analysis in terms of the types of natural hazards addressed, the disciplinary perspectives represented, and the number of studies included Targeted, application-centered analyses with a focus on development and use of modeling techniques to address various sources of uncertainty Emphasis on the impacts of climate change on natural hazard processes and outcomes Recommendations for cross-disciplinary and science transfer across natural hazard sciences

This volume will be an excellent resource for those interested in the current work on uncertainty classification/quantification and will document common and emergent research themes to allow all to learn from each other and build a more connected but still diverse and ever growing community of scientists.Read an interview with the editors to find out more:https://eos.org/editors-vox/reducing-uncertainty-in-hazard-prediction