Kooperativer Bibliotheksverbund

UID:

Umfang: 1 online resource (xx, 337 pages) , illustrations

ISBN: 0-12-820243-2

Inhalt: "Extreme Weather Forecasting reviews current knowledge about extreme weather events, including key elements and less well-known variables to accurately forecast them. The book covers multiple temporal scales as well as components of current weather forecasting systems. Sections cover case studies on successful forecasting as well as the impacts of extreme weather predictability, presenting a comprehensive and model agnostic review of best practices for atmospheric scientists and others who utilize extreme weather forecasts." -- Amazon.

Anmerkung: Front Cover -- Extreme Weather Forecasting -- Copyright Page -- Contents -- List of contributors -- Foreword -- Preface -- References -- 1 Overview of extreme weather events, impacts and forecasting techniques -- SUBCHAPTER 1.1 Definition of extreme weather events -- 1.1.1 Extreme heat -- 1.1.2 Extreme cold-severe winter storms -- 1.1.3 Tropical and extratropical storms -- 1.1.4 Severe convective storms -- 1.1.5 Extreme rainfall -- SUBCHAPTER 1.2 Weather forecasting -- SUBCHAPTER 1.3 Extreme weather forecasting in urban areas -- 1.3.1 Introduction -- 1.3.2 Urban heat island -- 1.3.3 Heat wave forecasting -- 1.3.4 Air quality modeling and prediction -- 1.3.5 Forecasting urban precipitation -- 1.3.6 Forecasting coastal urban flooding -- SUBCHAPTER 1.4 Wildfires and weather -- 1.4.1 Introduction: wildfires and weather-a coupled system -- 1.4.1.1 Wildfire impacts -- 1.4.1.2 Wildfire severity and weather -- 1.4.1.3 Wind storms, droughts, and storm outflows -- 1.4.1.4 Pyrocumulus and pyrocumulonimbus clouds -- 1.4.1.5 Wildfire emissions and transport -- 1.4.2 Wildfire prediction and risk assessment -- 1.4.2.1 Wildfire prediction -- 1.4.2.2 Wildfire risk assessment -- 1.4.3 Data requirements and data quality -- 1.4.3.1 Meteorological data -- 1.4.3.2 Fuel data -- 1.4.3.3 Fire perimeter data -- 1.4.3.4 Data assimilation -- 1.4.4 Wildfire prediction sensitivities and uncertainties -- 1.4.4.1 Sensitivity to weather forecast -- 1.4.4.2 Sensitivity to fuel characteristics -- 1.4.4.3 Sensitivity to ignition location and fire perimeter -- 1.4.4.4 Ensemble prediction for uncertainty quantification -- 1.4.5 Improved wildfire modeling for improved wildfire preparedness -- 1.4.5.1 Data collection, quality control, archiving, and standards -- 1.4.5.2 Wildfire spread parameterizations -- 1.4.5.3 Operational wildfire prediction and risk assessment systems. , References -- 2 Operational multiscale predictions of hazardous events -- 2.1 Introduction -- 2.2 Example case: 2015 European heatwave -- 2.3 Key factors of predictability -- 2.3.1 European heatwaves -- 2.3.2 European cold spells -- 2.3.3 Northwestern European windstorms -- 2.3.4 Precipitation extremes due to North-Atlantic cyclones -- 2.3.5 Precipitation extremes in southern Europe -- 2.3.6 Severe convection -- 2.4 Hazard forecasting -- 2.4.1 Hydrological processes and predictability of flood and droughts -- 2.4.2 Challenges -- 2.4.2.1 Type of hydrological, floods and drought forecasting, models -- 2.4.2.2 Improving usefulness of flood and drought forecasting systems -- 2.4.2.3 Hazard thresholds -- 2.4.2.4 Impact forecasting -- 2.4.2.5 Seamless forecasting -- 2.4.3 Fire risk -- 2.4.3.1 Forecasting fire at different spatial and temporal scales -- 2.4.4 Heat stress -- 2.4.4.1 Hazard forecasting -- 2.4.4.2 Discussion -- 2.5 Evaluation of hazardous events -- 2.5.1 Observations for evaluation -- 2.5.2 Evaluation metrics -- 2.6 Conclusion -- 2.7 Summary -- References -- 3 Forecasting extreme weather events and associated impacts: case studies -- SUBCHAPTER 3.1 Extreme heat -- 3.1.1 Introduction -- 3.1.1.1 Heat waves -- 3.1.1.2 Social vulnerability -- 3.1.1.3 Numerical weather forecasting -- 3.1.1.3.1 Deterministic versus ensemble forecasts -- 3.1.2 Data -- 3.1.2.1 North American Mesoscale Forecast System -- 3.1.2.2 Weather Underground -- 3.1.2.3 Socioeconomic -- 3.1.3 Methodology -- 3.1.3.1 Analog Ensemble independent search -- 3.1.3.2 Advantages and disadvantages of the Analog Ensemble technique -- 3.1.3.3 The Schaake Shuffle -- 3.1.3.4 Bias correction for rare events -- 3.1.3.5 Spatiotemporal downscaling -- 3.1.3.6 Accessibility -- 3.1.4 Results -- 3.1.5 Conclusions -- Acronyms -- References -- SUBCHAPTER 3.2 Atmospheric rivers -- 3.2.1 Introduction. , 3.2.2 Atmospheric river evolution -- 3.2.2.1 Mesoscale predictability challenges in atmospheric rivers -- 3.2.2.2 Precipitation generation in atmospheric rivers -- 3.2.2.3 Factors modifying hydrologic impacts during atmospheric rivers -- 3.2.3 Forecasting atmospheric rivers -- 3.2.3.1 Initialization -- 3.2.3.2 Parameterization -- 3.2.3.3 Grid resolution -- 3.2.4 Regional models -- 3.2.5 Ensemble forecast systems -- 3.2.6 Verification -- 3.2.7 Decision support -- 3.2.7.1 Calibration of atmospheric river forecasts -- 3.2.7.2 Role of partnerships between forecasting agencies and stakeholders -- 3.2.8 Summary -- References -- SUBCHAPTER 3.3 The hydrological Hillslope-Link Model for space-time prediction of streamflow: insights and applications at ... -- 3.3.1 Introduction -- 3.3.2 A generic set of ordinary differential equations to model water flows in the landscape and the river network -- 3.3.3 Domain decomposition and model inputs for the implementation of Hillslope-Link Model -- 3.3.3.1 Horizonal landscape decomposition -- 3.3.3.2 Configurations of hillslope-scale vertical and horizontal flows -- 3.3.3.3 Meteorological inputs -- 3.3.3.4 Streamflow gage stations -- 3.3.3.5 Automated flood forecasting system -- 3.3.4 Example of model performance using different configurations of vertical and horizonal fluxes at the hillslope scale -- 3.3.4.1 The simplest closure relationship: constant runoff coefficient -- 3.3.4.2 A variable runoff-coefficient model dependent on top-layer soil moisture and ponded water storage -- 3.3.4.3 A novel nonlinear parameterization for subsurface flows -- 3.3.5 Insights and real-time applications of the Hillslope-Link Model at the Iowa Flood Center -- 3.3.5.1 Effect of rainfall resolution and spatial randomness -- 3.3.5.2 Propagation of hillslope scale oscillations. , 3.3.5.3 A case study: real-time prediction of the September 2016 flood event along the Cedar River -- 3.3.5.3.1 Model setup -- 3.3.5.3.2 Model results -- 3.3.6 Summary and conclusions -- 3.3.7 Future work and upcoming challenges -- Acknowledgments -- References -- SUBCHAPTER 3.4 Social impacts: integrating dynamic social vulnerability in impact-based weather forecasting -- 3.4.1 Drivers of social impacts from extreme weather events -- 3.4.1.1 What is the role of human exposure and vulnerability in weather-related disasters? -- 3.4.1.2 How is social vulnerability defined and measured? -- 3.4.1.3 The space-time scales of human exposure: an intersection of the weather and vulnerability driving forces? -- 3.4.1.4 How the concept of dynamic social vulnerability can support weather impacts prediction? -- 3.4.2 The need for integrated forecasting tools to anticipate social impacts -- 3.4.2.1 Are hazard forecasts sufficient to improve early warning systems? -- 3.4.2.2 How to shift from hazard forecasts to impact-based forecasts? -- 3.4.2.3 How vulnerability metrics can complement hydrologic forecasts toward impact estimation? -- 3.4.3 Insights of methodological advances in modeling the coupled sociohydrometeorological system in high-impact weather events -- 3.4.3.1 Examples of two aggregated and individual-based microscale interdisciplinary approaches -- 3.4.3.2 Methodological comparison: strengths and weaknesses of the interdisciplinary modeling -- 3.4.4 Toward operational decision-making in high-impact weather events: insights from a participatory role-playing experiment -- 3.4.5 Conclusion -- References -- SUBCHAPTER 3.5 Landslides and debris flows -- 3.5.1 Introduction -- 3.5.2 Data and methodology -- 3.5.2.1 Precipitation products -- 3.5.2.1.1 Goddard Earth Observing System-Forecast. , 3.5.2.1.2 Integrated Multi-satellitE retrievals for global precipitation measurement -- 3.5.2.1.3 Multi-Radar/Multi-Sensor -- 3.5.2.2 Methodology -- 3.5.3 Results -- 3.5.3.1 Contiguous United States evaluation -- 3.5.3.2 Global evaluation -- 3.5.3.3 Case studies -- 3.5.3.3.1 Postfire debris flows -- 3.5.3.3.2 Rio de Janeiro extreme rainfall -- 3.5.3.3.3 Hurricane Eta in Central America -- 3.5.4 Discussion -- 3.5.5 Conclusions -- Acknowledgments -- References -- SUBCHAPTER 3.6 Weather-induced power outages -- 3.6.1 Power grid outages and severe weather -- 3.6.2 Modeling weather impact on the electric grid -- 3.6.2.1 Power outages during tropical storms -- 3.6.2.2 Power outages during extratropical rain and wind storms -- 3.6.2.3 Power outages during thunderstorms -- 3.6.2.4 Power outages during snow and ice storms -- References -- Afterword -- Index -- Back Cover.

Weitere Ausg.: Print version: Astitha, Marina Extreme Weather Forecasting San Diego : Elsevier,c2022 ISBN 9780128201244

Sprache: Englisch

UID:

Umfang: 1 online resource (xx, 337 pages) , illustrations

ISBN: 0-12-820243-2

Inhalt: "Extreme Weather Forecasting reviews current knowledge about extreme weather events, including key elements and less well-known variables to accurately forecast them. The book covers multiple temporal scales as well as components of current weather forecasting systems. Sections cover case studies on successful forecasting as well as the impacts of extreme weather predictability, presenting a comprehensive and model agnostic review of best practices for atmospheric scientists and others who utilize extreme weather forecasts." -- Amazon.

Anmerkung: Front Cover -- Extreme Weather Forecasting -- Copyright Page -- Contents -- List of contributors -- Foreword -- Preface -- References -- 1 Overview of extreme weather events, impacts and forecasting techniques -- SUBCHAPTER 1.1 Definition of extreme weather events -- 1.1.1 Extreme heat -- 1.1.2 Extreme cold-severe winter storms -- 1.1.3 Tropical and extratropical storms -- 1.1.4 Severe convective storms -- 1.1.5 Extreme rainfall -- SUBCHAPTER 1.2 Weather forecasting -- SUBCHAPTER 1.3 Extreme weather forecasting in urban areas -- 1.3.1 Introduction -- 1.3.2 Urban heat island -- 1.3.3 Heat wave forecasting -- 1.3.4 Air quality modeling and prediction -- 1.3.5 Forecasting urban precipitation -- 1.3.6 Forecasting coastal urban flooding -- SUBCHAPTER 1.4 Wildfires and weather -- 1.4.1 Introduction: wildfires and weather-a coupled system -- 1.4.1.1 Wildfire impacts -- 1.4.1.2 Wildfire severity and weather -- 1.4.1.3 Wind storms, droughts, and storm outflows -- 1.4.1.4 Pyrocumulus and pyrocumulonimbus clouds -- 1.4.1.5 Wildfire emissions and transport -- 1.4.2 Wildfire prediction and risk assessment -- 1.4.2.1 Wildfire prediction -- 1.4.2.2 Wildfire risk assessment -- 1.4.3 Data requirements and data quality -- 1.4.3.1 Meteorological data -- 1.4.3.2 Fuel data -- 1.4.3.3 Fire perimeter data -- 1.4.3.4 Data assimilation -- 1.4.4 Wildfire prediction sensitivities and uncertainties -- 1.4.4.1 Sensitivity to weather forecast -- 1.4.4.2 Sensitivity to fuel characteristics -- 1.4.4.3 Sensitivity to ignition location and fire perimeter -- 1.4.4.4 Ensemble prediction for uncertainty quantification -- 1.4.5 Improved wildfire modeling for improved wildfire preparedness -- 1.4.5.1 Data collection, quality control, archiving, and standards -- 1.4.5.2 Wildfire spread parameterizations -- 1.4.5.3 Operational wildfire prediction and risk assessment systems. , References -- 2 Operational multiscale predictions of hazardous events -- 2.1 Introduction -- 2.2 Example case: 2015 European heatwave -- 2.3 Key factors of predictability -- 2.3.1 European heatwaves -- 2.3.2 European cold spells -- 2.3.3 Northwestern European windstorms -- 2.3.4 Precipitation extremes due to North-Atlantic cyclones -- 2.3.5 Precipitation extremes in southern Europe -- 2.3.6 Severe convection -- 2.4 Hazard forecasting -- 2.4.1 Hydrological processes and predictability of flood and droughts -- 2.4.2 Challenges -- 2.4.2.1 Type of hydrological, floods and drought forecasting, models -- 2.4.2.2 Improving usefulness of flood and drought forecasting systems -- 2.4.2.3 Hazard thresholds -- 2.4.2.4 Impact forecasting -- 2.4.2.5 Seamless forecasting -- 2.4.3 Fire risk -- 2.4.3.1 Forecasting fire at different spatial and temporal scales -- 2.4.4 Heat stress -- 2.4.4.1 Hazard forecasting -- 2.4.4.2 Discussion -- 2.5 Evaluation of hazardous events -- 2.5.1 Observations for evaluation -- 2.5.2 Evaluation metrics -- 2.6 Conclusion -- 2.7 Summary -- References -- 3 Forecasting extreme weather events and associated impacts: case studies -- SUBCHAPTER 3.1 Extreme heat -- 3.1.1 Introduction -- 3.1.1.1 Heat waves -- 3.1.1.2 Social vulnerability -- 3.1.1.3 Numerical weather forecasting -- 3.1.1.3.1 Deterministic versus ensemble forecasts -- 3.1.2 Data -- 3.1.2.1 North American Mesoscale Forecast System -- 3.1.2.2 Weather Underground -- 3.1.2.3 Socioeconomic -- 3.1.3 Methodology -- 3.1.3.1 Analog Ensemble independent search -- 3.1.3.2 Advantages and disadvantages of the Analog Ensemble technique -- 3.1.3.3 The Schaake Shuffle -- 3.1.3.4 Bias correction for rare events -- 3.1.3.5 Spatiotemporal downscaling -- 3.1.3.6 Accessibility -- 3.1.4 Results -- 3.1.5 Conclusions -- Acronyms -- References -- SUBCHAPTER 3.2 Atmospheric rivers -- 3.2.1 Introduction. , 3.2.2 Atmospheric river evolution -- 3.2.2.1 Mesoscale predictability challenges in atmospheric rivers -- 3.2.2.2 Precipitation generation in atmospheric rivers -- 3.2.2.3 Factors modifying hydrologic impacts during atmospheric rivers -- 3.2.3 Forecasting atmospheric rivers -- 3.2.3.1 Initialization -- 3.2.3.2 Parameterization -- 3.2.3.3 Grid resolution -- 3.2.4 Regional models -- 3.2.5 Ensemble forecast systems -- 3.2.6 Verification -- 3.2.7 Decision support -- 3.2.7.1 Calibration of atmospheric river forecasts -- 3.2.7.2 Role of partnerships between forecasting agencies and stakeholders -- 3.2.8 Summary -- References -- SUBCHAPTER 3.3 The hydrological Hillslope-Link Model for space-time prediction of streamflow: insights and applications at ... -- 3.3.1 Introduction -- 3.3.2 A generic set of ordinary differential equations to model water flows in the landscape and the river network -- 3.3.3 Domain decomposition and model inputs for the implementation of Hillslope-Link Model -- 3.3.3.1 Horizonal landscape decomposition -- 3.3.3.2 Configurations of hillslope-scale vertical and horizontal flows -- 3.3.3.3 Meteorological inputs -- 3.3.3.4 Streamflow gage stations -- 3.3.3.5 Automated flood forecasting system -- 3.3.4 Example of model performance using different configurations of vertical and horizonal fluxes at the hillslope scale -- 3.3.4.1 The simplest closure relationship: constant runoff coefficient -- 3.3.4.2 A variable runoff-coefficient model dependent on top-layer soil moisture and ponded water storage -- 3.3.4.3 A novel nonlinear parameterization for subsurface flows -- 3.3.5 Insights and real-time applications of the Hillslope-Link Model at the Iowa Flood Center -- 3.3.5.1 Effect of rainfall resolution and spatial randomness -- 3.3.5.2 Propagation of hillslope scale oscillations. , 3.3.5.3 A case study: real-time prediction of the September 2016 flood event along the Cedar River -- 3.3.5.3.1 Model setup -- 3.3.5.3.2 Model results -- 3.3.6 Summary and conclusions -- 3.3.7 Future work and upcoming challenges -- Acknowledgments -- References -- SUBCHAPTER 3.4 Social impacts: integrating dynamic social vulnerability in impact-based weather forecasting -- 3.4.1 Drivers of social impacts from extreme weather events -- 3.4.1.1 What is the role of human exposure and vulnerability in weather-related disasters? -- 3.4.1.2 How is social vulnerability defined and measured? -- 3.4.1.3 The space-time scales of human exposure: an intersection of the weather and vulnerability driving forces? -- 3.4.1.4 How the concept of dynamic social vulnerability can support weather impacts prediction? -- 3.4.2 The need for integrated forecasting tools to anticipate social impacts -- 3.4.2.1 Are hazard forecasts sufficient to improve early warning systems? -- 3.4.2.2 How to shift from hazard forecasts to impact-based forecasts? -- 3.4.2.3 How vulnerability metrics can complement hydrologic forecasts toward impact estimation? -- 3.4.3 Insights of methodological advances in modeling the coupled sociohydrometeorological system in high-impact weather events -- 3.4.3.1 Examples of two aggregated and individual-based microscale interdisciplinary approaches -- 3.4.3.2 Methodological comparison: strengths and weaknesses of the interdisciplinary modeling -- 3.4.4 Toward operational decision-making in high-impact weather events: insights from a participatory role-playing experiment -- 3.4.5 Conclusion -- References -- SUBCHAPTER 3.5 Landslides and debris flows -- 3.5.1 Introduction -- 3.5.2 Data and methodology -- 3.5.2.1 Precipitation products -- 3.5.2.1.1 Goddard Earth Observing System-Forecast. , 3.5.2.1.2 Integrated Multi-satellitE retrievals for global precipitation measurement -- 3.5.2.1.3 Multi-Radar/Multi-Sensor -- 3.5.2.2 Methodology -- 3.5.3 Results -- 3.5.3.1 Contiguous United States evaluation -- 3.5.3.2 Global evaluation -- 3.5.3.3 Case studies -- 3.5.3.3.1 Postfire debris flows -- 3.5.3.3.2 Rio de Janeiro extreme rainfall -- 3.5.3.3.3 Hurricane Eta in Central America -- 3.5.4 Discussion -- 3.5.5 Conclusions -- Acknowledgments -- References -- SUBCHAPTER 3.6 Weather-induced power outages -- 3.6.1 Power grid outages and severe weather -- 3.6.2 Modeling weather impact on the electric grid -- 3.6.2.1 Power outages during tropical storms -- 3.6.2.2 Power outages during extratropical rain and wind storms -- 3.6.2.3 Power outages during thunderstorms -- 3.6.2.4 Power outages during snow and ice storms -- References -- Afterword -- Index -- Back Cover.

Weitere Ausg.: Print version: Astitha, Marina Extreme Weather Forecasting San Diego : Elsevier,c2022 ISBN 9780128201244

Sprache: Englisch

UID:

Umfang: 1 online resource (xx, 337 pages) , illustrations

ISBN: 0-12-820243-2

Inhalt: "Extreme Weather Forecasting reviews current knowledge about extreme weather events, including key elements and less well-known variables to accurately forecast them. The book covers multiple temporal scales as well as components of current weather forecasting systems. Sections cover case studies on successful forecasting as well as the impacts of extreme weather predictability, presenting a comprehensive and model agnostic review of best practices for atmospheric scientists and others who utilize extreme weather forecasts." -- Amazon.

Anmerkung: Front Cover -- Extreme Weather Forecasting -- Copyright Page -- Contents -- List of contributors -- Foreword -- Preface -- References -- 1 Overview of extreme weather events, impacts and forecasting techniques -- SUBCHAPTER 1.1 Definition of extreme weather events -- 1.1.1 Extreme heat -- 1.1.2 Extreme cold-severe winter storms -- 1.1.3 Tropical and extratropical storms -- 1.1.4 Severe convective storms -- 1.1.5 Extreme rainfall -- SUBCHAPTER 1.2 Weather forecasting -- SUBCHAPTER 1.3 Extreme weather forecasting in urban areas -- 1.3.1 Introduction -- 1.3.2 Urban heat island -- 1.3.3 Heat wave forecasting -- 1.3.4 Air quality modeling and prediction -- 1.3.5 Forecasting urban precipitation -- 1.3.6 Forecasting coastal urban flooding -- SUBCHAPTER 1.4 Wildfires and weather -- 1.4.1 Introduction: wildfires and weather-a coupled system -- 1.4.1.1 Wildfire impacts -- 1.4.1.2 Wildfire severity and weather -- 1.4.1.3 Wind storms, droughts, and storm outflows -- 1.4.1.4 Pyrocumulus and pyrocumulonimbus clouds -- 1.4.1.5 Wildfire emissions and transport -- 1.4.2 Wildfire prediction and risk assessment -- 1.4.2.1 Wildfire prediction -- 1.4.2.2 Wildfire risk assessment -- 1.4.3 Data requirements and data quality -- 1.4.3.1 Meteorological data -- 1.4.3.2 Fuel data -- 1.4.3.3 Fire perimeter data -- 1.4.3.4 Data assimilation -- 1.4.4 Wildfire prediction sensitivities and uncertainties -- 1.4.4.1 Sensitivity to weather forecast -- 1.4.4.2 Sensitivity to fuel characteristics -- 1.4.4.3 Sensitivity to ignition location and fire perimeter -- 1.4.4.4 Ensemble prediction for uncertainty quantification -- 1.4.5 Improved wildfire modeling for improved wildfire preparedness -- 1.4.5.1 Data collection, quality control, archiving, and standards -- 1.4.5.2 Wildfire spread parameterizations -- 1.4.5.3 Operational wildfire prediction and risk assessment systems. , References -- 2 Operational multiscale predictions of hazardous events -- 2.1 Introduction -- 2.2 Example case: 2015 European heatwave -- 2.3 Key factors of predictability -- 2.3.1 European heatwaves -- 2.3.2 European cold spells -- 2.3.3 Northwestern European windstorms -- 2.3.4 Precipitation extremes due to North-Atlantic cyclones -- 2.3.5 Precipitation extremes in southern Europe -- 2.3.6 Severe convection -- 2.4 Hazard forecasting -- 2.4.1 Hydrological processes and predictability of flood and droughts -- 2.4.2 Challenges -- 2.4.2.1 Type of hydrological, floods and drought forecasting, models -- 2.4.2.2 Improving usefulness of flood and drought forecasting systems -- 2.4.2.3 Hazard thresholds -- 2.4.2.4 Impact forecasting -- 2.4.2.5 Seamless forecasting -- 2.4.3 Fire risk -- 2.4.3.1 Forecasting fire at different spatial and temporal scales -- 2.4.4 Heat stress -- 2.4.4.1 Hazard forecasting -- 2.4.4.2 Discussion -- 2.5 Evaluation of hazardous events -- 2.5.1 Observations for evaluation -- 2.5.2 Evaluation metrics -- 2.6 Conclusion -- 2.7 Summary -- References -- 3 Forecasting extreme weather events and associated impacts: case studies -- SUBCHAPTER 3.1 Extreme heat -- 3.1.1 Introduction -- 3.1.1.1 Heat waves -- 3.1.1.2 Social vulnerability -- 3.1.1.3 Numerical weather forecasting -- 3.1.1.3.1 Deterministic versus ensemble forecasts -- 3.1.2 Data -- 3.1.2.1 North American Mesoscale Forecast System -- 3.1.2.2 Weather Underground -- 3.1.2.3 Socioeconomic -- 3.1.3 Methodology -- 3.1.3.1 Analog Ensemble independent search -- 3.1.3.2 Advantages and disadvantages of the Analog Ensemble technique -- 3.1.3.3 The Schaake Shuffle -- 3.1.3.4 Bias correction for rare events -- 3.1.3.5 Spatiotemporal downscaling -- 3.1.3.6 Accessibility -- 3.1.4 Results -- 3.1.5 Conclusions -- Acronyms -- References -- SUBCHAPTER 3.2 Atmospheric rivers -- 3.2.1 Introduction. , 3.2.2 Atmospheric river evolution -- 3.2.2.1 Mesoscale predictability challenges in atmospheric rivers -- 3.2.2.2 Precipitation generation in atmospheric rivers -- 3.2.2.3 Factors modifying hydrologic impacts during atmospheric rivers -- 3.2.3 Forecasting atmospheric rivers -- 3.2.3.1 Initialization -- 3.2.3.2 Parameterization -- 3.2.3.3 Grid resolution -- 3.2.4 Regional models -- 3.2.5 Ensemble forecast systems -- 3.2.6 Verification -- 3.2.7 Decision support -- 3.2.7.1 Calibration of atmospheric river forecasts -- 3.2.7.2 Role of partnerships between forecasting agencies and stakeholders -- 3.2.8 Summary -- References -- SUBCHAPTER 3.3 The hydrological Hillslope-Link Model for space-time prediction of streamflow: insights and applications at ... -- 3.3.1 Introduction -- 3.3.2 A generic set of ordinary differential equations to model water flows in the landscape and the river network -- 3.3.3 Domain decomposition and model inputs for the implementation of Hillslope-Link Model -- 3.3.3.1 Horizonal landscape decomposition -- 3.3.3.2 Configurations of hillslope-scale vertical and horizontal flows -- 3.3.3.3 Meteorological inputs -- 3.3.3.4 Streamflow gage stations -- 3.3.3.5 Automated flood forecasting system -- 3.3.4 Example of model performance using different configurations of vertical and horizonal fluxes at the hillslope scale -- 3.3.4.1 The simplest closure relationship: constant runoff coefficient -- 3.3.4.2 A variable runoff-coefficient model dependent on top-layer soil moisture and ponded water storage -- 3.3.4.3 A novel nonlinear parameterization for subsurface flows -- 3.3.5 Insights and real-time applications of the Hillslope-Link Model at the Iowa Flood Center -- 3.3.5.1 Effect of rainfall resolution and spatial randomness -- 3.3.5.2 Propagation of hillslope scale oscillations. , 3.3.5.3 A case study: real-time prediction of the September 2016 flood event along the Cedar River -- 3.3.5.3.1 Model setup -- 3.3.5.3.2 Model results -- 3.3.6 Summary and conclusions -- 3.3.7 Future work and upcoming challenges -- Acknowledgments -- References -- SUBCHAPTER 3.4 Social impacts: integrating dynamic social vulnerability in impact-based weather forecasting -- 3.4.1 Drivers of social impacts from extreme weather events -- 3.4.1.1 What is the role of human exposure and vulnerability in weather-related disasters? -- 3.4.1.2 How is social vulnerability defined and measured? -- 3.4.1.3 The space-time scales of human exposure: an intersection of the weather and vulnerability driving forces? -- 3.4.1.4 How the concept of dynamic social vulnerability can support weather impacts prediction? -- 3.4.2 The need for integrated forecasting tools to anticipate social impacts -- 3.4.2.1 Are hazard forecasts sufficient to improve early warning systems? -- 3.4.2.2 How to shift from hazard forecasts to impact-based forecasts? -- 3.4.2.3 How vulnerability metrics can complement hydrologic forecasts toward impact estimation? -- 3.4.3 Insights of methodological advances in modeling the coupled sociohydrometeorological system in high-impact weather events -- 3.4.3.1 Examples of two aggregated and individual-based microscale interdisciplinary approaches -- 3.4.3.2 Methodological comparison: strengths and weaknesses of the interdisciplinary modeling -- 3.4.4 Toward operational decision-making in high-impact weather events: insights from a participatory role-playing experiment -- 3.4.5 Conclusion -- References -- SUBCHAPTER 3.5 Landslides and debris flows -- 3.5.1 Introduction -- 3.5.2 Data and methodology -- 3.5.2.1 Precipitation products -- 3.5.2.1.1 Goddard Earth Observing System-Forecast. , 3.5.2.1.2 Integrated Multi-satellitE retrievals for global precipitation measurement -- 3.5.2.1.3 Multi-Radar/Multi-Sensor -- 3.5.2.2 Methodology -- 3.5.3 Results -- 3.5.3.1 Contiguous United States evaluation -- 3.5.3.2 Global evaluation -- 3.5.3.3 Case studies -- 3.5.3.3.1 Postfire debris flows -- 3.5.3.3.2 Rio de Janeiro extreme rainfall -- 3.5.3.3.3 Hurricane Eta in Central America -- 3.5.4 Discussion -- 3.5.5 Conclusions -- Acknowledgments -- References -- SUBCHAPTER 3.6 Weather-induced power outages -- 3.6.1 Power grid outages and severe weather -- 3.6.2 Modeling weather impact on the electric grid -- 3.6.2.1 Power outages during tropical storms -- 3.6.2.2 Power outages during extratropical rain and wind storms -- 3.6.2.3 Power outages during thunderstorms -- 3.6.2.4 Power outages during snow and ice storms -- References -- Afterword -- Index -- Back Cover.

Weitere Ausg.: Print version: Astitha, Marina Extreme Weather Forecasting San Diego : Elsevier,c2022 ISBN 9780128201244

Sprache: Englisch