Articles | Volume 24, issue 4
https://doi.org/10.5194/nhess-24-1415-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-1415-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Apr 2024
Research article |
| 24 Apr 2024
| 24 Apr 2024
FOREWARNS: development and multifaceted verification of enhanced regional-scale surface water flood forecasts
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom
Cathryn E. Birch
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom
Steven J. Böing
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom
Thomas Willis
School of Geography, University of Leeds, Leeds, LS2 9JT, United Kingdom
Linda Speight
School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, United Kingdom
Aurore N. Porson
Met Office, Exeter, EX1 3PB, United Kingdom
Charlie Pilling
Flood Forecasting Centre, Exeter, EX1 3PB, United Kingdom
Kay L. Shelton
JBA Consulting, Skipton, BD23 3FD, United Kingdom
Mark A. Trigg
School of Civil Engineering, University of Leeds, Leeds, LS2 9JT, United Kingdom
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Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Declan L. Finney, Alan M. Blyth, Paul R. Field, Martin I. Daily, Benjamin J. Murray, Mengyu Sun, Paul J. Connolly, Zhiqiang Cui, and Steven Böing
EGUsphere, https://doi.org/10.5194/egusphere-2025-1227, https://doi.org/10.5194/egusphere-2025-1227, 2025
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We present observation-informed modelling from the Deep Convective Microphysics Experiment to study how environmental conditions and cloud processes affect anvil cloud albedo and radiation. Aerosols influencing cloud droplets or influencing ice formation yield varying radiative effects. We introduce fingerprint metrics to discern these effects. Using detailed observations and modelling, we offer insights into high cloud radiative effects and feedbacks.
Helen Hooker, Sarah Dance, David Mason, John Bevington, and Kay Shelton
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-178, https://doi.org/10.5194/hess-2024-178, 2024
Revised manuscript not accepted
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This study introduces a method that uses satellite data to enhance flood map selection for forecast-based financing applications. Tested on the 2022 Pakistan floods, it successfully triggered flood maps in four out of five regions, including those with urban areas. The approach ensures timely humanitarian aid by updating flood maps, even when initial triggers are missed, aiding in better disaster preparedness and risk management.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
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This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Declan L. Finney, Alan M. Blyth, Martin Gallagher, Huihui Wu, Graeme J. Nott, Michael I. Biggerstaff, Richard G. Sonnenfeld, Martin Daily, Dan Walker, David Dufton, Keith Bower, Steven Böing, Thomas Choularton, Jonathan Crosier, James Groves, Paul R. Field, Hugh Coe, Benjamin J. Murray, Gary Lloyd, Nicholas A. Marsden, Michael Flynn, Kezhen Hu, Navaneeth M. Thamban, Paul I. Williams, Paul J. Connolly, James B. McQuaid, Joseph Robinson, Zhiqiang Cui, Ralph R. Burton, Gordon Carrie, Robert Moore, Steven J. Abel, Dave Tiddeman, and Graydon Aulich
Earth Syst. Sci. Data, 16, 2141–2163, https://doi.org/10.5194/essd-16-2141-2024, https://doi.org/10.5194/essd-16-2141-2024, 2024
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The DCMEX (Deep Convective Microphysics Experiment) project undertook an aircraft- and ground-based measurement campaign of New Mexico deep convective clouds during July–August 2022. The campaign coordinated a broad range of instrumentation measuring aerosol, cloud physics, radar signals, thermodynamics, dynamics, electric fields, and weather. The project's objectives included the utilisation of these data with satellite observations to study the anvil cloud radiative effect.
Joseph Smith, Cathryn Birch, John Marsham, Simon Peatman, Massimo Bollasina, and George Pankiewicz
Nat. Hazards Earth Syst. Sci., 24, 567–582, https://doi.org/10.5194/nhess-24-567-2024, https://doi.org/10.5194/nhess-24-567-2024, 2024
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Nowcasting uses observations to make predictions of the atmosphere on short timescales and is particularly applicable to the Maritime Continent, where storms rapidly develop and cause natural disasters. This paper evaluates probabilistic and deterministic satellite nowcasting algorithms over the Maritime Continent. We show that the probabilistic approach is most skilful at small scales (~ 60 km), whereas the deterministic approach is most skilful at larger scales (~ 200 km).
Zhiqiang Cui, Alan Blyth, Ralph Burton, Sandrine Bony, Steven Böing, Alan Gadian, and Leif Denby
EGUsphere, https://doi.org/10.5194/egusphere-2023-1999, https://doi.org/10.5194/egusphere-2023-1999, 2023
Preprint archived
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Cumulus clouds near Barbados can influence how much heat and energy reaches the Earth's surface. A cluster of clouds resembling a flower is presented. Satellite images, dropsonde data, and weather data are used to understand how this cloud system developed. A significant feature was the appearance of a large area of rain at the centre of the cloud system during its later stages. The paper also studied the environmental conditions around the cloud system.
Helen Hooker, Sarah L. Dance, David C. Mason, John Bevington, and Kay Shelton
Nat. Hazards Earth Syst. Sci., 23, 2769–2785, https://doi.org/10.5194/nhess-23-2769-2023, https://doi.org/10.5194/nhess-23-2769-2023, 2023
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Ensemble forecasts of flood inundation produce maps indicating the probability of flooding. A new approach is presented to evaluate the spatial performance of an ensemble flood map forecast by comparison against remotely observed flooding extents. This is important for understanding forecast uncertainties and improving flood forecasting systems.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, https://doi.org/10.5194/gmd-16-1713-2023, 2023
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Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the UK. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
Mirianna Budimir, Alison Sneddon, Issy Nelder, Sarah Brown, Amy Donovan, and Linda Speight
Geosci. Commun., 5, 151–175, https://doi.org/10.5194/gc-5-151-2022, https://doi.org/10.5194/gc-5-151-2022, 2022
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This paper extracts key learning from two case studies (India and Mozambique), outlining solutions and approaches to challenges in developing forecast products. These lessons and solutions can be used by forecasters and practitioners to support the development of useful, appropriate, and co-designed forecast information for institutional decision-makers to support more effective early action in advance of disasters.
Mark V. Bernhofen, Mark A. Trigg, P. Andrew Sleigh, Christopher C. Sampson, and Andrew M. Smith
Nat. Hazards Earth Syst. Sci., 21, 2829–2847, https://doi.org/10.5194/nhess-21-2829-2021, https://doi.org/10.5194/nhess-21-2829-2021, 2021
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The use of different global datasets to calculate flood exposure can lead to differences in global flood exposure estimates. In this study, we use three global population datasets and a simple measure of a river’s flood susceptibility (based on the terrain alone) to explore how the choice of population data and the size of river represented in global flood models affect global and national flood exposure estimates.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
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The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Jennifer Saxby, Julia Crook, Simon Peatman, Cathryn Birch, Juliane Schwendike, Maria Valdivieso da Costa, Juan Manuel Castillo Sanchez, Chris Holloway, Nicholas P. Klingaman, Ashis Mitra, and Huw Lewis
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2021-46, https://doi.org/10.5194/wcd-2021-46, 2021
Preprint withdrawn
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This study assesses the ability of the new Met Office IND1 numerical model to simulate tropical cyclones and their associated hazards, such as high winds and heavy rainfall. The new system consists of both atmospheric and oceanic models coupled together, allowing us to explore the sensitivity of cyclones to important air–sea feedbacks. We find that the model can accurately simulate tropical cyclone position, structure, and intensity, which are crucial for predicting and mitigating hazards.
Cited articles
Aldridge, T., Gunawan, O., Moore, R. J., Cole, S. J., Boyce, G., and Cowling, R.: Developing an impact library for forecasting surface water flood risk, J. Flood Risk Manag., 13, 1–19, https://doi.org/10.1111/jfr3.12641, 2020.
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013.
Archer, D., O'Donnell, G., Lamb, R., Warren, S., and Fowler, H. J.: Historical flash floods in England: New regional chronologies and database, J. Flood Risk Manag., 12, e12526, https://doi.org/10.1111/JFR3.12526, 2019.
Archer, D. R. and Fowler, H. J.: Characterising flash flood response to intense rainfall and impacts using historical information and gauged data in Britain, J. Flood Risk Manag., 11, S121–S133, https://doi.org/10.1111/JFR3.12187, 2018.
Arnal, L., Ramos, M.-H., Coughlan de Perez, E., Cloke, H. L., Stephens, E., Wetterhall, F., van Andel, S. J., and Pappenberger, F.: Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game, Hydrol. Earth Syst. Sci., 20, 3109–3128, https://doi.org/10.5194/hess-20-3109-2016, 2016.
Arnal, L., Anspoks, L., Manson, S., Neumann, J., Norton, T., Stephens, E., Wolfenden, L., and Cloke, H. L.: “Are We talking just a bit of water out of bank? or is it Armageddon?” Front line perspectives on transitioning to probabilistic fluvial flood forecasts in England, Geosci. Commun., 3, 203–232, https://doi.org/10.5194/GC-3-203-2020, 2020.
Beck, J., Bouttier, F., Wiegand, L., Gebhardt, C., Eagle, C., and Roberts, N.: Development and verification of two convection-allowing multi-model ensembles over Western Europe, Q. J. Roy. Meteor. Soc., 142, 2808–2826, https://doi.org/10.1002/QJ.2870, 2016.
Bell, V. A., Kay, A. L., Jones, R. G., Moore, R. J., and Reynard, N. S.: Use of soil data in a grid-based hydrological model to estimate spatial variation in changing flood risk across the UK, J. Hydrol., 377, 335–350, https://doi.org/10.1016/j.jhydrol.2009.08.031, 2009.
Birch, C. E., Rabb, B. L., Böing, S. J., Shelton, K. L., Lamb, R., Hunter, N., Trigg, M. A., Hines, A., Taylor, A. L., Pilling, C., and Dale, M.: Enhanced surface water flood forecasts: User-led development and testing, J. Flood Risk Manag., 14, 1–15, https://doi.org/10.1111/jfr3.12691, 2021.
Böing, S. J., Birch, C. E., Rabb, B. L., and Shelton, K. L.: A percentile-based approach to rainfall scenario construction for surface-water flood forecasts, Meteorol. Appl., 27, 1–16, https://doi.org/10.1002/met.1963, 2020.
Böing, S. J., Maybee, B., and Willis, T.: sjboeing/fast_rainfall_processing: FOREWARNS publication version of code (1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.10987887, 2024.
Brousseau, P., Berre, L., Bouttier, F., and Desroziers, G.: Background-error covariances for a convective-scale data-assimilation system: AROME–France 3D-Var, Q. J. Roy. Meteor. Soc., 137, 409–422, https://doi.org/10.1002/QJ.750, 2011.
Brousseau, P., Seity, Y., Ricard, D., and Léger, J.: Improvement of the forecast of convective activity from the AROME-France system, Q. J. Roy. Meteor. Soc., 142, 2231–2243, https://doi.org/10.1002/QJ.2822, 2016.
de Bruijn, J. A., de Moel, H., Jongman, B., Wagemaker, J., and Aerts, J. C. J. H.: TAGGS: Grouping Tweets to Improve Global Geoparsing for Disaster Response, J. Geovisualization Spat. Anal., 2, 1–14, https://doi.org/10.1007/S41651-017-0010-6/TABLES/4, 2018.
de Bruijn, J. A., de Moel, H., Jongman, B., de Ruiter, M. C., Wagemaker, J., and Aerts, J. C. J. H.: A global database of historic and real-time flood events based on social media, Sci. Data, 6, 1–12, https://doi.org/10.1038/s41597-019-0326-9, 2019.
Chen, Y., Paschalis, A., Kendon, E., Kim, D., and Onof, C.: Changing Spatial Structure of Summer Heavy Rainfall, Using Convection-Permitting Ensemble, Geophys. Res. Lett., 48, e2020GL090903, https://doi.org/10.1029/2020GL090903, 2021.
Cole, S. J., Moore, R. J., and Mattingley, P. S.: Surface Water Flooding Component for NHP HIM: Phase 1 report. Contract Report to the Environment Agency, Research Contractor: Centre for Ecology & Hydrology, 55 pp., https://nora.nerc.ac.uk/id/eprint/513835 (last access: 18 April 2024), 2015.
Coles, D., Yu, D., Wilby, R. L., Green, D., and Herring, Z.: Beyond “flood hotspots”: Modelling emergency service accessibility during flooding in York, UK, J. Hydrol., 546, 419–436, https://doi.org/10.1016/J.JHYDROL.2016.12.013, 2017.
DEFRA: Surface Water Management: An Action Plan, Department for Environment Food and Rural Affairs, 41 pp., https://www.gov.uk/government/publications/surface-water-management-action-plan (last access: 19 April 2024), 2018.
Demeritt, D., Nobert, S., Cloke, H. L., and Pappenberger, F.: The European Flood Alert System and the communication, perception, and use of ensemble predictions for operational flood risk management, Hydrol. Process., 27, 147–157, https://doi.org/10.1002/HYP.9419, 2013.
Dottori, F., Kalas, M., Salamon, P., Bianchi, A., Alfieri, L., and Feyen, L.: An operational procedure for rapid flood risk assessment in Europe, Nat. Hazards Earth Syst. Sci., 17, 1111–1126, https://doi.org/10.5194/nhess-17-1111-2017, 2017.
Environment Agency: A National Assessment of Flood Risk, https://assets.publishing.service.gov.uk/media/5a7ba398ed915d4147621ad6/geho0609bqds-e-e.pdf (last access: 18 April 2024), 2009.
Environment Agency: What is the Risk of Flooding from Surface Water map?, 51 pp., https://assets.publishing.service.gov.uk/media/5db6ded540f0b6379a7acbb8/What-is-the-Risk-of-Flooding-from-Surface-Water-Map.pdf (last access: 18 April 2024), 2019.
Erickson, M. J., Kastman, J. S., Albright, B., Perfater, S., Nelson, J. A., Schumacher, R. S., and Herman, G. R.: Verification results from the 2017 HMT–WPC flash flood and intense rainfall experiment, J. Appl. Meteorol. Climatol., 58, 2591–2604, https://doi.org/10.1175/JAMC-D-19-0097.1, 2019.
Erickson, M. J., Albright, B., and Nelson, J. A.: Verifying and redefining the weather prediction center's excessive rainfall outlook forecast product, Weather Forecast., 36, 325–340, https://doi.org/10.1175/WAF-D-20-0020.1, 2021.
Ferro, C. A. T. and Stephenson, D. B.: Extremal dependence indices: Improved Verification measures for deterministic forecasts of rare binary events, Weather Forecast., 26, 699–713, https://doi.org/10.1175/WAF-D-10-05030.1, 2011.
Flack, D. L. A., Skinner, C. J., Hawkness-Smith, L., O'Donnell, G., Thompson, R. J., Waller, J. A., Chen, A. S., Moloney, J., Largeron, C., Xia, X., Blenkinsop, S., Champion, A. J., Perks, M. T., Quinn, N., and Speight, L. J.: Recommendations for improving integration in national end-to-end flood forecasting systems: An overview of the FFIR (Flooding From Intense Rainfall) programme, Water, 11, 725, https://doi.org/10.3390/w11040725, 2019.
Frick, J. and Hegg, C.: Can end-users' flood management decision making be improved by information about forecast uncertainty?, Atmos. Res., 100, 296–303, https://doi.org/10.1016/J.ATMOSRES.2010.12.006, 2011.
Frogner, I. L., Singleton, A. T., Køltzow, M., and Andrae, U.: Convection-permitting ensembles: Challenges related to their design and use, Q. J. Roy. Meteor. Soc., 145, 90–106, https://doi.org/10.1002/QJ.3525, 2019a.
Frogner, I. L., Andrae, U. L. F., Bojarova, J., Callado, A., Escribà, P. A. U., Feddersen, H., Hally, A., Kauhanen, J., Randriamampianina, R., Singleton, A., Smet, G., van der Veen, S., and Vignes, O. L. E.: HarmonEPS – The HARMONIE Ensemble Prediction System, Weather Forecast., 34, 1909–1937, https://doi.org/10.1175/WAF-D-19-0030.1, 2019b.
Georgakakos, K. P., Modrick, T. M., Shamir, E., Campbell, R., Cheng, Z., Jubach, R., Sperfslage, J. A., Spencer, C. R., and Banks, R.: The Flash Flood Guidance System Implementation Worldwide: A Successful Multidecadal Research-to-Operations Effort, B. Am. Meteorol. Soc., 103, E665–E679, https://doi.org/10.1175/BAMS-D-20-0241.1, 2022.
Golding, B., Roberts, N., Leoncini, G., Mylne, K., and Swinbank, R.: MOGREPS-UK convection-permitting ensemble products for surface water flood forecasting: Rationale and first results, J. Hydrometeorol., 17, 1383–1406, https://doi.org/10.1175/JHM-D-15-0083.1, 2016.
Golding, B. (Ed.): Towards the “Perfect” Weather Warning, Springer International Publishing, https://doi.org/10.1007/978-3-030-98989-7, 2022.
Gourley, J. J., Erlingis, J. M., Hong, Y., and Wells, E. B.: Evaluation of tools used for monitoring and forecasting flash floods in the united states, Weather Forecast., 27, 158–173, https://doi.org/10.1175/WAF-D-10-05043.1, 2012.
Greater London Authority: Surface Water Flooding in London: Rountable Progress Report, 44 pp., https://www.london.gov.uk/sites/default/files/flooding_progress_report_final_1.pdf (last access: 18 April 2024), 2022.
Green, D., Yu, D., Pattison, I., Wilby, R., Bosher, L., Patel, R., Thompson, P., Trowell, K., Draycon, J., Halse, M., Yang, L., and Ryley, T.: City-scale accessibility of emergency responders operating during flood events, Nat. Hazards Earth Syst. Sci., 17, 1–16, https://doi.org/10.5194/nhess-17-1-2017, 2017.
Hagelin, S., Son, J., Swinbank, R., McCabe, A., Roberts, N., and Tennant, W.: The Met Office convective-scale ensemble, MOGREPS-UK, Q. J. Roy. Meteor. Soc., 143, 2846–2861, https://doi.org/10.1002/qj.3135, 2017.
Hand, W. H., Fox, N. I., and Collier, C. G.: A study of twentieth-century extreme rainfall events in the United Kingdom with implications for forecasting, Meteorol. Appl., 11, 15–31, https://doi.org/10.1017/S1350482703001117, 2004.
Harrison, D. L., Driscoll, S. J., and Kitchen, M.: Improving precipitation estimates from weather radar using quality control and correction techniques, Meteorol. Appl., 7, 135–144, https://doi.org/10.1017/S1350482700001468, 2000.
Harrison, D. L., Norman, K., Pierce, C., and Gaussiat, N.: Radar products for hydrological applications in the UK, Proc. Inst. Civ. Eng. Water Manag., 165, 89–103, https://doi.org/10.1680/wama.2012.165.2.89, 2012.
Hawcroft, M., Lavender, S., Copsey, D., Milton, S., Rodríguez, J., Tennant, W., Webster, S., and Cowan, T.: The Benefits of Ensemble Prediction for Forecasting an Extreme Event: The Queensland Floods of February 2019, Mon. Weather Rev., 149, 2391–2408, https://doi.org/10.1175/MWR-D-20-0330.1, 2021.
Henonin, J., Russo, B., Mark, O., and Gourbesville, P.: Real-time urban flood forecasting and modelling – a state of the art, J. Hydroinform., 15, 717–736, https://doi.org/10.2166/HYDRO.2013.132, 2013.
Herman, G. R. and Schumacher, R. S.: Flash flood verification: Pondering precipitation proxies, J. Hydrometeorol., 19, 1753–1776, https://doi.org/10.1175/JHM-D-18-0092.1, 2018.
Jackson, H.: Urban flash flooding in England – an exciting new project, Circ. – Newsl. Br. Hydrol. Soc., 156, 14–15, 2023.
Kendon, E. J., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., and Senior, C. A.: Heavier summer downpours with climate change revealed by weather forecast resolution model, Nat. Clim. Chang., 4, 570–576, https://doi.org/10.1038/nclimate2258, 2014.
Kendon, M.: Met Office case studies of past severe weather events: Heavy rainfall July 2019, 8 pp., https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/weather/learn-about/uk-past-events/interesting/2019/2019_008_july_rainfall.pdf (last access: 18 April 2024), 2019.
Kirk, P. J., Clark, M. R., and Creed, E.: Weather Observations Website, Weather, 76, 47–49, https://doi.org/10.1002/WEA.3856, 2021.
Klasa, C., Arpagaus, M., Walser, A., and Wernli, H.: An evaluation of the convection-permitting ensemble COSMO-E for three contrasting precipitation events in Switzerland, Q. J. Roy. Meteor. Soc., 144, 744–764, https://doi.org/10.1002/QJ.3245, 2018.
Lehner, B. and Grill, G.: Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems, Hydrol. Process., 27, 2171–2186, https://doi.org/10.1002/HYP.9740, 2013.
MacLeod, D., Kilavi, M., Mwangi, E., Ambani, M., Osunga, M., Robbins, J., Graham, R., Rowhani, P., and Todd, M. C.: Are Kenya Meteorological Department heavy rainfall advisories useful for forecast-based early action and early preparedness for flooding?, Nat. Hazards Earth Syst. Sci., 21, 261–277, https://doi.org/10.5194/nhess-21-261-2021, 2021.
McCabe, A., Swinbank, R., Tennant, W., and Lock, A.: Representing model uncertainty in the Met Office convection-permitting ensemble prediction system and its impact on fog forecasting, Q. J. Roy. Meteor. Soc., 142, 2897–2910, https://doi.org/10.1002/qj.2876, 2016.
McEwen, L., Stokes, A., Crowley, K., and Roberts, C.: Using role-play for expert science communication with professional stakeholders in flood risk management, J. Geogr. Higher Educ., 38, 277–300, https://doi.org/10.1080/03098265.2014.911827, 2014.
Merz, B., Kuhlicke, C., Kunz, M., Pittore, M., Babeyko, A., Bresch, D. N., Domeisen, D. I. V., Feser, F., Koszalka, I., Kreibich, H., Pantillon, F., Parolai, S., Pinto, J. G., Punge, H. J., Rivalta, E., Schröter, K., Strehlow, K., Weisse, R., and Wurpts, A.: Impact Forecasting to Support Emergency Management of Natural Hazards, Rev. Geophys., 58, e2020RG000704, https://doi.org/10.1029/2020RG000704, 2020.
Met Office: Met Office Rain Radar Data from the NIMROD System, NCAS British Atmospheric Data Centre [data set], http://catalogue.ceda.ac.uk/uuid/82adec1f896af6169112d09cc1174499 (last access: 18 April 2024), 2003.
Milan, M., Macpherson, B., Tubbs, R., Dow, G., Inverarity, G., Mittermaier, M., Halloran, G., Kelly, G., Li, D., Maycock, A., Payne, T., Piccolo, C., Stewart, L., and Wlasak, M.: Hourly 4D-Var in the Met Office UKV operational forecast model, Q. J. Roy. Meteor. Soc., 146, 1281–1301, https://doi.org/10.1002/QJ.3737, 2020.
Mittermaier, M. P.: Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts, Nat. Hazards Earth Syst. Sci., 8, 445–460, https://doi.org/10.5194/nhess-8-445-2008, 2008.
Moore, R. J., Cole, S. J., Dunn, S., Ghimire, S., Golding, B. W., Pierce, C. E., Roberts, N. M., and Speight, L.: Surface water flood forecasting for urban communities: CREW report CRW2012_03, https://www.crew.ac.uk/sites/www.crew.ac.uk/files/sites/default/files/publication/CREW Surface water flood forecasting for urban communities_full report.pdf (last access: 18 April 2024), 2015.
Neal, R. A., Boyle, P., Grahame, N., Mylne, K., and Sharpe, M.: Ensemble based first guess support towards a risk-based severe weather warning service, Meteorol. Appl., 21, 563–577, https://doi.org/10.1002/met.1377, 2014.
North, R., Trueman, M., Mittermaier, M., and Rodwell, M. J.: An assessment of the SEEPS and SEDI metrics for the verification of 6 h forecast precipitation accumulations, Meteorol. Appl., 20, 164–175, https://doi.org/10.1002/met.1405, 2013.
Ochoa-Rodríguez, S., Wang, L. P., Thraves, L., Johnston, A., and Onof, C.: Surface water flood warnings in England: overview, assessment and recommendations based on survey responses and workshops, J. Flood Risk Manag., 11, S211–S221, https://doi.org/10.1111/jfr3.12195, 2018.
Pilling, C.: New developments at the Flood Forecasting Centre: operations and flood risk guidance, Urban Water III, 1, 237–248, https://doi.org/10.2495/uw160211, 2016.
Porson, A. N., Carr, J. M., Hagelin, S., Darvell, R., North, R., Walters, D., Mylne, K. R., Mittermaier, M. P., Willington, S., and Macpherson, B.: Recent upgrades to the Met Office convective-scale ensemble: An hourly time-lagged 5-day ensemble, Q. J. Roy. Meteor. Soc., 146, 3245–3265, https://doi.org/10.1002/qj.3844, 2020.
Ramos, M. H., van Andel, S. J., and Pappenberger, F.: Do probabilistic forecasts lead to better decisions?, Hydrol. Earth Syst. Sci., 17, 2219–2232, https://doi.org/10.5194/hess-17-2219-2013, 2013.
Raynaud, L., Pechin, I., Arbogast, P., Rottner, L., and Destouches, M.: Object-based verification metrics applied to the evaluation and weighting of convective-scale precipitation forecasts, Q. J. Roy. Meteor. Soc., 145, 1992–2008, https://doi.org/10.1002/QJ.3540, 2019.
Roberts, N., Ayliffe, B., Evans, G., Moseley, S., Rust, F., Sandford, C., Trzeciak, T., Abernethy, P., Beard, L., Crosswaite, N., Fitzpatrick, B., Flowerdew, J., Gale, T., Holly, L., Hopkinson, A., Hurst, K., Jackson, S., Jones, C., Mylne, K., Sampson, C., Sharpe, M., Wright, B., Backhouse, S., Baker, M., Brierley, D., Booton, A., Bysouth, C., Coulson, R., Coultas, S., Crocker, R., Harbord, R., Howard, K., Hughes, T., Mittermaier, M., Petch, J., Pillinger, T., Smart, V., Smith, E., and Worsfold, M.: IMPROVER: the new probabilistic post processing system at the UK Met Office, B. Am. Meteorol. Soc., 1–31, https://doi.org/10.1175/bams-d-21-0273.1, 2023.
Roebber, P. J.: Visualizing multiple measures of forecast quality, Weather Forecast., 24, 601–608, https://doi.org/10.1175/2008WAF2222159.1, 2009.
Saint-Martin, C., Fouchier, C., Javelle, P., Douvinet, J., and Vinet, F.: Assessing the exposure to floods to estimate the risk of flood-related damage in French Mediterranean basins, 3rd Eur. Conf. Flood Risk Manag. FLOODRisk 2016, E3SE3S Web Conf., 7, 04013, https://doi.org/10.1051/e3sconf/20160704013, 2016.
Schwartz, C. S. and Sobash, R. A.: Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations, Mon. Weather Rev., 145, 3397–3418, https://doi.org/10.1175/MWR-D-16-0400.1, 2017.
Sharpe, M. A.: A flexible approach to the objective verification of warnings, Meteorol. Appl., 23, 65–75, https://doi.org/10.1002/met.1530, 2016.
Sharpe, M. A., Bysouth, C. E., and Stretton, R. L.: How well do Met Office post-processed site-specific probabilistic forecasts predict relative-extreme events?, Meteorol. Appl., 25, 23–32, https://doi.org/10.1002/met.1665, 2018.
Smith, L., Liang, Q., James, P., and Lin, W.: Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework, J. Flood Risk Manag., 10, 370–380, https://doi.org/10.1111/JFR3.12154, 2017.
Speight, L., Cole, S. J., Moore, R. J., Pierce, C., Wright, B., Golding, B., Cranston, M., Tavendale, A., Dhondia, J., and Ghimire, S.: Developing surface water flood forecasting capabilities in Scotland: an operational pilot for the 2014 Commonwealth Games in Glasgow, J. Flood Risk Manag., 11, S884–S901, https://doi.org/10.1111/jfr3.12281, 2018.
Speight, L. J., Cranston, M. D., White, C. J., and Kelly, L.: Operational and emerging capabilities for surface water flood forecasting, Wiley Interdiscip. Rev. Water, 8, 1–24, https://doi.org/10.1002/wat2.1517, 2021.
Stephenson, D. B., Casati, B., Ferro, C. A. T., and Wilson, C. A.: The extreme dependency score: a non-vanishing measure for forecasts of rare events, Meteorol. Appl., 15, 41–50, https://doi.org/10.1002/met, 2008.
Stewart, E. J., Jones, D. A., Svensson, C., Morris, D. G., Dempsey, P., Dent, J. E., Collier, C. G., and Anderson, C. A.: Reservoir Safety – Long Return Period Rainfall, Technical Report (two volumes), Project FD2613 WS 194/2/39, DEFRA/EA FCERM R&D Programme, https://assets.publishing.service.gov.uk/media/602e43e2e90e0709e3127489/_long_return_report_1.pdf (last access: 18 April 2024), 2013.
Swinbank, R., Friederichs, P., and Wahl, S.: Forecasting high-impact weather using ensemble prediction systems, in: Dynamical Prediction of Large Scale High-Impact Weather Climate Events, edited by: Li, J., Swinbank, R., Grotjahn, R., and Volkert, H., 95–112, https://doi.org/10.1017/CBO9781107775541.008, 2016.
Tennant, W.: Improving initial condition perturbations for MOGREPS-UK, Q. J. Roy. Meteor. Soc., 2324–2336, 141, https://doi.org/10.1002/qj.2524, 2015.
Vesuviano, G.: The FEH22 rainfall depth-duration-frequency (DDF) model, 103 pp., Science centre technical report, https://fehwebdocs.hydrosolutions.co.uk/.attachments/The FEH22 rainfall depth-duration-frequency (DDF) model-caa11347-4ff7-4c89-b707-bf5bb1c05d79.pdf (last access: 18 April 2024), 2022.
Vesuviano, G., Stewart, E., Spencer, P., and Miller, J. D.: The effect of depth-duration-frequency model recalibration on rainfall return period estimates, J. Flood Risk Manag., 14, e12703, https://doi.org/10.1111/JFR3.12703, 2021.
Welles, E., Sorooshian, S., Carter, G., and Olsen, B.: Hydrologic verification: A call for action and collaboration, B. Am. Meteorol. Soc., 88, 503–511, https://doi.org/10.1175/BAMS-88-4-503, 2007.
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences: Fourth Edition, Elsevier B.V., Oxford, ISBN 978-0-12-815823-4, 2019.
Witherow, M. A., Sazara, C., Winter-Arboleda, I. M., Elbakary, M. I., Cetin, M., and Iftekharuddin, K. M.: Floodwater detection on roadways from crowdsourced images, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 7, 529–540, https://doi.org/10.1080/21681163.2018.1488223, 2018.
Wu, W., Emerton, R., Duan, Q., Wood, A. W., Wetterhall, F., and Robertson, D. E.: Ensemble flood forecasting: Current status and future opportunities, Wiley Interdiscip. Rev. Water, 7, e1432, https://doi.org/10.1002/WAT2.1432, 2020.
Yu, D. and Coulthard, T. J.: Evaluating the importance of catchment hydrological parameters for urban surface water flood modelling using a simple hydro-inundation model, J. Hydrol., 524, 385–400, https://doi.org/10.1016/J.JHYDROL.2015.02.040, 2015.
Zhang, Q., Li, L., Ebert, B., Golding, B., Johnston, D., Mills, B., Panchuk, S., Potter, S., Riemer, M., Sun, J., Taylor, A., Jones, S., Ruti, P., and Keller, J.: Increasing the value of weather-related warnings, Sci. Bull., 64, 647–649, https://doi.org/10.1016/J.SCIB.2019.04.003, 2019.
Short summary
This paper presents the development and verification of FOREWARNS, a novel method for regional-scale forecasting of surface water flooding. We detail outcomes from a workshop held with UK forecast users, who indicated they valued the forecasts and would use them to complement national guidance. We use results of objective forecast tests against flood observations over northern England to show that this confidence is justified and that FOREWARNS meets the needs of UK flood responders.
This paper presents the development and verification of FOREWARNS, a novel method for...
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