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Extreme Weather Forecasting [Pehme köide]

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Edited by (Associate Professor, Associate Department Head, Department of Civil and Environmental Engineering, School of Engineering, University of Connecticut, CT, USA), Edited by (Civil and Environmental Engineering, Rutgers University, New Brunswick, NJ,)
  • Formaat: Paperback / softback, 358 pages, kõrgus x laius: 229x152 mm, kaal: 520 g, 140 illustrations (70 in full color); Illustrations, unspecified
  • Ilmumisaeg: 13-Oct-2022
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 012820124X
  • ISBN-13: 9780128201244
Teised raamatud teemal:
Extreme Weather ForecastingSuurem pilt
  • Formaat: Paperback / softback, 358 pages, kõrgus x laius: 229x152 mm, kaal: 520 g, 140 illustrations (70 in full color); Illustrations, unspecified
  • Ilmumisaeg: 13-Oct-2022
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 012820124X
  • ISBN-13: 9780128201244
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128201244.html
Märksõnad:
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.
  • Reviews recent developments in numerical prediction for better forecasting of extreme weather events
  • Covers causes and mechanisms of high impact extreme events and how to account for these variables when forecasting
  • Includes numerous case studies on successful forecasting, outlining why they worked
List of contributors
xi
Foreword xv
Preface xvii
1 Overview of extreme weather events, impacts and forecasting techniques
1(86)
1.1 Definition of extreme weather events
1(6)
Marina Astitha
Efthymios Nikolopoulos
1.1.1 Extreme heat
3(1)
1.1.2 Extreme cold--severe winter storms
3(2)
1.1.3 Tropical and extratropical storms
5(1)
1.1.4 Severe convective storms
5(1)
1.1.5 Extreme rainfall
6(1)
1.2 Weather forecasting
7(7)
Marina Astitha
Linus Magnusson
Efthymios Nikolopoulos
1.3 Extreme weather forecasting in urban areas
14(17)
Mukul Tewari
Zhihua Wang
Dan Chen
Quang-Van Doan
Hiroyuki Kusaka
Prathap Ramamurthy
Pallav Ray
1.3.1 Introduction
14(1)
1.3.2 Urban heat island
15(3)
1.3.3 Heat wave forecasting
18(4)
1.3.4 Air quality modeling and prediction
22(4)
1.3.5 Forecasting urban precipitation
26(3)
1.3.6 Forecasting coastal urban flooding
29(2)
1.4 Wildfires and weather
31(37)
Branko Kosovfc
Timothy W. Juliano
Amy DeCastro
Maria Frediani
Amanda Siems-Anderson
Pedro Jimenez
Domingo Munoz-Esparza
Jason C. Knievel
Masih Eghdami
1.4.1 Introduction: wildfires and weather--a coupled system
31(3)
1.4.1.1 Wildfire impacts
34(1)
1.4.1.2 Wildfire severity and weather
35(3)
1.4.1.3 Wind storms, droughts, and storm outflows
38(3)
1.4.1.4 Pyrocumulus and pyrocumulonimbus clouds
41(3)
1.4.1.5 Wildfire emissions and transport
44(2)
1.4.2 Wildfire prediction and risk assessment
46(1)
1.4.2.1 Wildfire prediction
46(4)
1.4.2.2 Wildfire risk assessment
50(3)
1.4.3 Data requirements and data quality
53(1)
1.4.3.1 Meteorological data
53(1)
1.4.3.2 Fuel data
54(3)
1.4.3.3 Fire perimeter data
57(3)
1.4.3.4 Data assimilation
60(1)
1.4.4 Wildfire prediction sensitivities and uncertainties
61(1)
1.4.4.1 Sensitivity to weather forecast
61(1)
1.4.4.2 Sensitivity to fuel characteristics
61(2)
1.4.4.3 Sensitivity to ignition location and fire perimeter
63(1)
1.4.4.4 Ensemble prediction for uncertainty quantification
64(1)
1.4.5 Improved wildfire modeling for improved wildfire preparedness
65(1)
1.4.5.1 Data collection, quality control, archiving, and standards
65(1)
1.4.5.2 Wildfire spread parameterizations
66(1)
1.4.5.3 Operational wildfire prediction and risk assessment systems
67(1)
References
68(19)
2 Operational multiscale predictions of hazardous events
87(44)
Linus Magnusson
C. Prudhomme
F. Di Giuseppe
C. Di Napoli
F. Pappenberger
2.1 Introduction
87(3)
2.2 Example case: 2015 European heatwave
90(4)
2.3 Key factors of predictability
94(6)
2.3.1 European heatwaves
95(1)
2.3.2 European cold spells
96(1)
2.3.3 Northwestern European windstorms
97(1)
2.3.4 Precipitation extremes due to North-Atlantic cyclones
98(1)
2.3.5 Precipitation extremes in southern Europe
98(1)
2.3.6 Severe convection
99(1)
2.4 Hazard forecasting
100(16)
2.4.1 Hydrological processes and predictability of flood and droughts
102(1)
2.4.2 Challenges
103(3)
2.4.2.1 Type of hydrological, floods and drought forecasting, models
106(1)
2.4.2.2 Improving usefulness of flood and drought forecasting systems
107(1)
2.4.2.3 Hazard thresholds
107(1)
2.4.2.4 Impact forecasting
108(1)
2.4.2.4 Seamless forecasting
109(1)
2.4.3 Fire risk
110(1)
2.4.3.1 Forecasting fire at different spatial and temporal scales
111(1)
2.4.4 Heat stress
112(1)
2.4.4.1 Hazard forecasting
113(2)
2.4.4.2 Discussion
115(1)
2.5 Evaluation of hazardous events
116(5)
2.5.1 Observations for evaluation
117(3)
2.5.2 Evaluation metrics
120(1)
2.6 Conclusion
121(1)
2.7 Summary
121(2)
References
123(8)
3 Forecasting extreme weather events and associated impacts: Case Studies
131(196)
3.1 Extreme heat
Martina Calovi
Weiming Hu
Laura Clemente
Guido Cervone
3.1.1 Introduction
131(2)
3.1.1.1 Heatwaves
133(2)
3.1.1.2 Social vulnerability
135(4)
3.1.1.3 Numerical weather forecasting
139(1)
3.1.2 Data
140(1)
3.1.2.1 North American Mesoscale Forecast System
141(1)
3.1.2.2 Weather Underground
142(2)
3.1.2.3 Socioeconomic
144(1)
3.1.3 Methodology
144(1)
3.1.3.1 Analog Ensemble independent search
144(2)
3.1.3.2 Advantages and disadvantages of the Analog Ensemble technique
146(1)
3.1.3.3 The Schaake Shuffle
146(2)
3.1.3.4 Bias correction for rare events
148(1)
3.1.3.5 Spatiotemporal downscaling
148(1)
3.1.3.6 Accessibility
149(2)
3.1.4 Results
151(14)
3.1.5 Conclusions
165(1)
Acronyms
166(1)
References
167(6)
3.2 Atmospheric rivers
Forest Cannon
Luca Delle Monache
3.2.1 Introduction
173(1)
3.2.2 Atmospheric river evolution
174(2)
3.2.2.1 Mesoscale predictability challenges in atmospheric rivers
176(1)
3.2.2.2 Precipitation generation in atmospheric rivers
177(1)
3.2.2.3 Factors modifying hydrologic impacts during atmospheric rivers
178(1)
3.2.3 Forecasting atmospheric rivers
178(2)
3.2.3.1 Initialization
180(3)
3.2.3.2 Parameterization
183(1)
3.2.3.3 Grid resolution
184(2)
3.2.4 Regional models
186(2)
3.2.5 Ensemble forecast systems
188(5)
3.2.6 Verification
193(2)
3.2.7 Decision support
195(1)
3.2.7.1 Calibration of atmospheric river forecasts
195(1)
3.2.7.2 Role of partnerships between forecasting agencies and stakeholders
195(1)
3.2.8 Summary
196(1)
References
196(4)
3.3 The hydrological Hillslope-Link Model for space-time prediction of streamflow: insights and applications at the Iowa Flood Center
Ricardo Mantilla
Witold F. Krajewski
Nicolas Velasquez
Scott Small
Tibebu Ayalew
Felipe Quintero
Navid Jadidoleslam
Morgan Fonley
3.3.1 Introduction
200(4)
3.3.2 A generic set of ordinary differential equations to model water flows in the landscape and the river network
204(6)
3.3.3 Domain decomposition and model inputs for the implementation of Hillslope-Link Model
210(1)
3.3.3.1 Horizonal landscape decomposition
210(1)
3.3.3.2 Configurations of hillslope-scale vertical and horizontal flows
210(1)
3.3.3.3 Meteorological inputs
211(2)
3.3.3.4 Streamflow gage stations
213(1)
3.3.3.5 Automated flood forecasting system
214(1)
3.3.4 Example of model performance using different configurations of vertical and horizonal fluxes at the hillslope scale
215(1)
3.3.4.1 The simplest closure relationship: constant runoff coefficient
215(3)
3.3.4.2 A variable runoff-coefficient model dependent on top-layer soil moisture and ponded water storage
218(2)
3.3.4.3 A novel nonlinear parameterization for subsurface flows
220(2)
3.3.5 Insights and real-time applications of the Hillslope-Link Model at the Iowa Flood Center
222(1)
3.3.5.1 Effect of rainfall resolution and spatial randomness
222(1)
3.3.5.2 Propagation of hillslope scale oscillations
223(2)
3.3.5.3 A case study: real-time prediction of the September 2016 flood event along the Cedar River
225(8)
3.3.6 Summary and conclusions
233(2)
3.3.7 Future work and upcoming challenges
235(1)
Acknowledgments
235(1)
References
235(4)
3.4 Social impacts: integrating dynamic social vulnerability in impact-based weather forecasting
Galateia Terti
Sandrine Anquetin
Isabelle Ruin
3.4.1 Drivers of social impacts from extreme weather events
239(1)
3.4.1.1 What is the role of human exposure and vulnerability in weather-related disasters?
239(2)
3.4.1.2 How is social vulnerability defined and measured?
241(1)
3.4.1.3 The space-time scales of human exposure: an intersection of the weather and vulnerability driving forces?
242(2)
3.4.1.4 How the concept of dynamic social vulnerability can support weather impacts prediction?
244(3)
3.4.2 The need for integrated forecasting tools to anticipate social impacts
247(1)
3.4.2.1 Are hazard forecasts sufficient to improve early warning systems?
247(3)
3.4.2.2 How to shift from hazard forecasts to impact-based forecasts?
250(2)
3.4.2.3 How vulnerability metrics can complement hydrologic forecasts toward impact estimation?
252(3)
3.4.3 Insights of methodological advances in modeling the coupled sociohydrometeorological system in high-impact weather events
255(1)
3.4.3.1 Examples of two aggregated and individual-based microscale interdisciplinary approaches
255(6)
3.4.3.2 Methodological comparison: strengths and weaknesses of the interdisciplinary modeling
261(3)
3.4.4 Toward operational decision-making in high-impact weather events: insights from a participatory role-playing experiment
264(4)
3.4.5 Conclusion
268(2)
References
270(8)
3.5 Landslides and debris flows
Daiia B. Kirschbaum
Sana Khan
3.5.1 Introduction
278(2)
3.5.2 Data and methodology
280(1)
3.5.2.1 Precipitation products
280(1)
3.5.2.2 Methodology
281(1)
3.5.3 Results
282(1)
3.5.3.1 Contiguous United States evaluation
282(6)
3.5.3.2 Global evaluation
288(4)
3.5.3.3 Case studies
292(5)
3.5.4 Discussion
297(4)
3.5.5 Conclusions
301(1)
Acknowledgments
302(1)
References
302(3)
3.6 Weather-induced power outages
Diego Cerrai
Emmanouil Anagnostou
3.6.1 Power grid outages and severe weather
305(2)
3.6.2 Modeling weather impact on the electric grid
307(5)
3.6.2.1 Power outages during tropical storms
312(3)
3.6.2.2 Power outages during extratropical rain and wind storms
315(3)
3.6.2.3 Power outages during thunderstorms
318(2)
3.6.2.4 Power outages during snow and ice storms
320(3)
References
323(4)
Afterword 327(2)
Index 329
Dr. Marina Astitha is an Associate Professor and the Associate Department Head at the Department of Civil and Environmental Engineering, University of Connecticut (UConn). Dr. Astithas expertise lie in the areas of atmospheric numerical modeling (weather and air quality) from regional to global scales. She is leading the Atmospheric Modeling and Air Quality Group since joining UConn in 2013. Her research program focuses on improving the prediction of extreme weather events, wind prediction for wind farm facilities, and integration of multi-media modeling systems with machine learning to solve environmental problems. She is committed in supporting, mentoring, and inspiring the next generation of engineers to innovate, lead and thrive in solving complex environmental problems and sustain a healthy, diverse and equitable society in the years to come Dr. Efthymios Nikolopoulos is Associate Professor at the Department of Civil and Environmental Engineering at Rutgers University. His research focuses on the integration of remote sensing observations with numerical and statistical modeling to advance understanding and predictability of water cycle components and weather-related hazards. Dr. Nikolopoulos has authored/co-authored more than 70 peer-reviewed publications and 8 book chapters in the areas of hydrometeorology, remote sensing of precipitation, flood hydrology and landslide/debris flow prediction. He is an Associate Editor for the Journal of Hydrology and the recipient of the NASA Earth System Science Graduate Fellowship and the Marie Curie Postdoctoral fellowship