Brian Golding, Sally Potter, Elizabeth Ebert, and David Hoffmann
In an era of climate change, extremes relative to the historical record are expected to occur more frequently. In 2021, several weather disasters occurred in which conditions surpassed recorded extremes. These included floods and associated impacts in the USA, Europe, China and Indonesia, heat waves and wildfires in southern Europe and northwestern North America, and winter weather in Spain and the southern USA. Analysis of the performance of warning systems in these disasters by the WWRP HIWeather project shows that in most, but not all, cases there was adequate forewarning of the nature and magnitude of the event, but that lack of preparedness and/or communication failures led to loss of life in particular vulnerable groups. Using information gathered for the HIWeather value chain database, I will present an overview of key aspects of each event – the weather and its impact, the forecasts, the warnings, and the responses. In the flood cases, a common feature was that limitations in the spatial resolution of the forecasts limited the ability of hydrological prediction systems to translate the rainfall forecast into a realistic flood forecast. In the case of the winter weather and heat waves, a lack of preparedness at both official levels and in the at-risk population led to failures of response. A comparative analysis of warning performance shows that communication failures were often distributed along the warning chain. Drawing on material from our recently published book, Towards the Perfect Weather Warning, I will draw some conclusions about critical components that are necessary for a successful warning system.
How to cite:
Golding, B., Potter, S., Ebert, E., and Hoffmann, D.: Preparing for the unprecedented, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-284, https://doi.org/10.5194/ems2022-284, 2022.
Jan Verkade, Laurène Bouaziz, Klaas-Jan van Heeringen, and Albrecht Weerts
Last July, the Ardennes/Eifel regions and the wider Moselle, Meuse and Rhine basins were hit by severe flooding. Large precipitation depths were forecast as of the weekend prior to the flooding, some 5 days in advance. Post the event, however, it turned out that precipitation forecasts were significantly lower than the observed precipitation depths. This caused various hydrological forecasts to initially underestimate river flow. That, in turn, had all sorts of fall-out, including a significant additional level of incredulity on the part of flood responders, who were already taken by surprise due to the unusual timing of the flood.
The analyses show the extent to which various quantitative precipitation forecast (QPF) products (including DWD-ICON and ECMWF deterministic and ensemble products) coincide and deviate from the ‘observational’ quantitative precipitation estimates (QPE), both in terms of quantity, timing and location. The analysis shows that the true uncertainty in the weather forecasts was significantly larger than what the ensemble forecasts suggested.
The analyses also show that the various observational and reanalysis products (including ERA5, E-OBS, HYRAS, RADOLAN and those originating from various operational hydrological forecasting systems) greatly vary in their estimate of actual/true/observed precipitation amounts.
The analysis is conducted both on the level of the original grids as well as on the level of various Meuse and Rhine tributaries including the Vesdre and Ahr river basins. Also, the effect of the different QPE and QPF estimates on the simulated and forecasted discharge is shown.
The present case study provides anecdotal evidence of the performance of QPF originating from numerical weather prediction products in severe flood events. Additional emphasis will be given to the propagation of these uncertainties to river flow forecasts produced by the Dutch national fluvial forecasting service, where the lead author was acting as flood duty officer in the week preceding the floods.
How to cite:
Verkade, J., Bouaziz, L., van Heeringen, K.-J., and Weerts, A.: QPF and QPE performance during the July 2021 flood event, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-44, https://doi.org/10.5194/ems2022-44, 2022.
Twenty-one runners died of hypothermia during the 100 km Ultramarathon Mountain race in Baiyin, Gansu Province on 22 May 2021. The hypothermia was caused by a combination of low temperatures, precipitation, and high winds associated with a typical large-scale cold front passing by the race site that morning. Based on historical hourly records of 13 meteorological surface stations over the past six years, temperature (3.0°C) and apparent temperature (−5.1°C) at 1200 LST as well as gust wind speed (11.2 m s−1) at 1100 LST on the day of the tragedy were found to be within the top or bottom 5thpercentile for the month of May. The precipitation was only moderate at this time, but when temperature lower than 3.0°C, gust wind speed greater than 11.2 m s−1, and precipitation greater than 0.1 mm for any adjacent three hours were combined together, 1200 LST 22 May fell within the top 0.1% of cases. The European Centre for Medium-range Weather Forecasting model produced reasonably good forecasts of the low temperature and high wind one day and seven days before the event, respectfully. Based on this study, lessons that can be learned from this tragedy are summarized from an academic perspective: Hazard and impact forecasts of high-impact weather events should be developed to increase the value of weather forecasts. Probability forecasts should be issued by government weather agencies and communicated well to the public. And more importantly, knowledge of how to evaluate the impact of weather should be delivered to the public in the future.How to increase the value of weather forecasts to provide better service to users and to prevent weather-related disasters is a great challenge. From observation to decision making, there are several “mountains and valleys” to overcome. Collaborations among scientists in different areas, governments, social media, and citizens are needed for successful hazard prevention.
How to cite:
Zhang, Q. and Ng, C.-P.: Lessons Learned from the Tragedy during the 100 km Ultramarathon Race in Baiyin, Gansu Province on 22 May 2021, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-66, https://doi.org/10.5194/ems2022-66, 2022.
On 20th July 2021, an extreme rainfall event occurred at Zhengzhou city in China with maximum hourly rainfall of 201.9 mm and 24h accumulated rainfall of 624.1 mm at Zhengzhou weather station. This storm rainfall event caused huge damages of about 40.9 billion Yuan (RMB) property loss and 380 death. Using the Value Chain concept, the functions of observation, weather forecast, hazard forecast, impact forecast, warning and decision during this event were analyzed. The gaps between the “islands” in the Value Chain were emphasized. There are some limits in the observation system to monitor the event and few observations were passed into the next step of weather forecast. The gap between observation and weather forecast was obvious. The skills of global and regional numerical model forecasts were verified. The forecast skills for the extreme rainfall is low, and it is a challenge to forecast the location and amount of the extreme rainfall for the numerical model. How to improve the forecast skills from minutes to days for these extreme weather systems is still a big problem. The ability of hazard and impact forecast were low, and then lead to some delayed or mis decisions. It is indicated that cooperation between mult-discipline is required to improve the ability of hazard and impact forecast. Some high-level hazard warnings were issued during the event. It is helpful for the public and government making decisions and avoiding the damage, but it needs to be improved in some respect. This case is an example to demonstrate the importance of the idea of value chain in hazard mitigation.
How to cite:
Liang, X., Wang, Y., and Zhang, Q.: Analysis of the Value Chain in the ‘7.20’ZhengZhou extreme rainfall event, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-601, https://doi.org/10.5194/ems2022-601, 2022.
All over the World large and medium-sized cities formulate ambitions and implement plans to create smart, sustainable, and climate resilient urban environments. These plans are needed to manage risks in urban areas where hazardous events can cause a series of interlocking systems to fail. To support these plans Integrated Urban Services (IUS) are being developed. IUS is driven both by the growing capabilities of NHMSs to develop and provide such services and by the growing needs of urban decision makers to adequately anticipate these hazards and their impacts on cities and to improve short-term and long-term resilience. To implement IUS cities must invest in diverse observation and modelling options to monitor and evaluate their response to changes in the climate (including extreme events) and urban landscape.
Notwithstanding the large potential to contribute to sustainability goals of cities the realization of broad scoped truly integrated urban services could be faster than its current pace of development. In this context it is expected that the ability to better demonstrate the value potential of IUS and the extra benefits of further developing and coordinating different services would help to precipitate uptake and expansion of IUS. To this end, the WMO Study Group on Integrated Urban Services has produced several guidelines to help NHMSs to create IUS in cooperation city authorities. This Study Group also received expert support to better describe the benefit potential, mechanisms behind benefit generation, and ways to assess the net benefits of IUS. The approach hinges on the value chain concept.
The presentation will show how the value chain can explain and analyse the diversity in organizational and service structure of IUS. The presentation will also illustrate how the value chain can be used to quantify value generation and how that approach can also be used to review what segments in current service practice seem to offer the prospect for the most notable improvements. Assessed service cases are as such hypothetical yet based on real world information and on indications from the literature on key parameters.
How to cite:
Perrels, A. and Millls, G.: Identifying and enhancing socioeconomic value of Integrated Urban Services, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-673, https://doi.org/10.5194/ems2022-673, 2022.
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David Hoffmann, Beth Ebert, Carla Mooney, Brian Golding, and Sally Potter
The weather information value chain provides a framework for characterising the production, communication, and use of information by all stakeholders in an end-to-end warning system covering weather and hazard monitoring, modelling and forecasting, risk assessment, communication and preparedness activities. Warning services are typically developed and provided through a multitude of complex and malleable value chains (networks), often established through co-design, co-creation and co-provision.
A 4-year international project under the WMO World Weather Research Programme is using value chain approaches to describe and evaluate warning systems for high impact weather by integrating physical and social science. It aims to create a framework with guidance and tools for using value chain approaches, and to develop a database of high impact weather warning case studies for scientists and practitioners to review, analyse and learn from previous experience using value chain approaches.
Since its start in November 2020, the project has made significant progress to achieve its aims through contributions from a continuously growing, interdisciplinary project team. It has developed an interim database template for high-impact weather event case study collection and analysis and used it to describe and learn from several high impact weather events that happened in 2021. It has also created a glossary for a common terminology between social and physical scientists and outlined a conceptual high-level value chain framework.
Here we focus on the interim database template that provides a tool for scientists and practitioners involved in researching, designing and evaluating weather-related warning systems to review previous experience of high impact weather events and assess their efficacy. The interim database leverages and extends existing databases, such as EM-DAT, ECMWF Severe Event Catalogue, SHELDUS, DesInventar, etc., collecting rich information on the many components of the warning value chain. It will enable in-depth case studies and cross-cutting analysis of end-to-end warning value chains, from simple to complex, to understand effective practices, and support the value cycle of review and learning from past events to identify improvements that would enhance future warnings.
We encourage the research and operational community to participate in this project by contributing case studies of high impact events and collaborating in their analysis.
Corresponding/presenting author: David Hoffmann, Bureau of Meteorology, Melbourne, Australia; david.hoffmann@bom.gov.au
How to cite:
Hoffmann, D., Ebert, B., Mooney, C., Golding, B., and Potter, S.: WMO HIWeather/SERA Value Chain Project: Using Value Chain Approaches to Evaluate End-to-End Warning Systems, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-357, https://doi.org/10.5194/ems2022-357, 2022.
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Elizabeth Ebert, Carla Mooney, David Hoffmann, and Trina Read
National weather services often review their forecasts and warnings following high impact weather events to analyse performance and identify areas for improvement. These reviews can be internal or external with agency partners and the community.
A major flood event in eastern Australia in March 2021 led to the evacuation of 18,000 people, 1000 flood rescues and an estimated economic damage bill of $2.9billion and signicant social disruption.
As part of its post-event review the Bureau of Meteorology brought contributors across the whole value chain together to analyse the effectiveness of the service provided during this flood. The intention was to connect pre-season engagement, weather observations, modelling, hazard prediction, forecasts and warnings, communication, response, impact and recovery with the aim to better understand:
the end-to-end value chain linking weather and warnings to stakeholder decisions and benefits,
strengths and weaknesses of the various value chain elements and their linkages as they played out in the March 2021 flood event,
roles and expectations of different groups within the value chain for exchange of data, information and engagement, and
developments underway or planned, and improvements needed.
The WWRP Warning Value Chain project interim database template was used to guide information collection. Methods included review of quantitative data, document analysis and pre-workshop interviews. The template systematically draws out description and analysis of the different elements of the warning value chain, the information flows along the chain, its overall success and challenges in creating benefit.
The post-event review workshop had two parts. The first used short presentations from subject matter experts along all parts of the chain to describe how the event unfolded, who did what and when, and the performance of the various elements. The second part used small group collaborative processes to explore what went well and what could have been better. The groups were purposefully formed to bring together contributors along the value chain who would not normally directly interact. The processes included activities and technologies (collaboration boards, shared spreadsheets, and survey) that accommodated diverse communication styles to enable deeper discussion, ideation, and prioritisation for improvement.
The value chain approach facilitated identification of the key stakeholders and their capabilities and brought focus to the important linkages between them. The evaluation of the effectiveness of information exchange along the warning chain identified a range of improvements which have potential to improve services and reduce economic and social harms. This approach engaged a broader and more diverse range of players in the post-event review and brought additional insights in the observation, modelling and post-processing parts of the value chain.
How to cite:
Ebert, E., Mooney, C., Hoffmann, D., and Read, T.: Using Value Chain Tools to Learn from a Major Flood Event in Australia, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-102, https://doi.org/10.5194/ems2022-102, 2022.
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At the Swiss Federal Office of Meteorology and Climatology MeteoSwiss, the main goal of our warning system is to support the affected population and organizations in the best possible way to take the necessary measures to reduce the impact of extreme weather events. How can we measure whether our warnings achieve this goal and create societal value? A classical way of assessing the success of a warning system is to calculate the hit rate and false alarm rate. While this is very important, it is just a first step in measuring the value of the warning. Even the most perfect warning is not a guarantee for a reduction of the impact of the warned extreme event. A next step would be to assess how many individuals of the target group were reached and whether they were satisfied with the warnings and understood the information. However, the number of people reached and their average satisfaction alone still do not indicate how successful our warnings are. What we ultimately would like to know is whether our warnings lead to improved risk assessment, behavioural change and finally, to reduced costs and damages through extreme weather.
This contribution presents a concept for a standardized population survey that aims to provide quantifiable measures on the social impact of the warning. By drawing from methods of impact assessment in the non-profit sector, we differentiate between output, outcome and impact of our warnings and derive indicators for each of these levels. Data for these indicators will be collected through representative population surveys in the affected regions a few days after an extreme weather event occurred. During a pilot phase to be launched in fall of this year, we will assess the potential of these indicators and of different data collection methods (representative online survey vs. representative telephone interviews vs. online survey with users of our channels). Although data will not be available yet at the time of the conference, the presentation aims to present our approach and discuss opportunities as well as challenges and limits of measuring the value of our warnings by using event-based surveys.
How to cite:
Popovic, N. and Willemse, S.: Measuring the social impact of weather warnings with event-based surveys, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-585, https://doi.org/10.5194/ems2022-585, 2022.
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In Australia, the impact of tropical cyclones, extratropical storms, and extensive inland flooding have shown the vulnerability of low-lying areas in floodplains and along the coast to flooding. Particularly when flooding from rainfall, rivers, and the sea converge, it has caused significant damage to infrastructure and loss of life. To help mitigate the impacts, emergency services benefit from accurate flood inundation forecasts, real-time inundation analysis, and post-event flood maps to support decision-making before, during, and after events. This requires a sophisticated early warning system capable of integrating numerous real-time data, fast, large-scale compound modeling tools and flexible dissemination protocols to reach the various end-users.
THE NEED FOR A DATA AND MODELING FRAMEWORK
The challenges for developing an effective early warning system are found in the efficient integration of large meteorological and hydrological data sets, specialized modules to process the data, and open interfaces to allow easy integration of new and existing modeling and dissemination capabilities. We have therefore developed a cloud-based national Australian flood inundation forecasting system with the Delft-FEWS framework at its core. This system, currently in the Proof of Concept (PoC) phase, handles large amounts of forecast data efficiently, orchestrates massive computations and disseminates the real time information in various ways to the end users.
THE NEED FOR FAST COMPOUND FLOOD MODELS
Since flooding can occur by different drivers (marine, pluvial, riverine), all these processes need to be included dynamically to model compound flooding events with enough detail to produce accurate and relevant flood maps for the emergency authorities. Therefore, the reduced-physics solver SFINCS (Leijnse et al. 2021) was used, combining all relevant processes to model compound flooding events, with strongly reduced computation times to model large scales. We demonstrate in this work that we have developed 13 SFINCS models along the coastline of the states of New South Wales, Queensland and Northern Territories covering a total of 7000+ kilometers. Additionally, corresponding gridded hydrological WFLOW models have been set up that provide upstream river boundary conditions for the SFINCS models. The models were validated against historical events with good results before using them for simulating the 2022 event in real-time.
CONCLUSION
During the extensive floods in February 2022, the PoC demonstrated that a data and modeling framework solution can be built that is scalable, interoperable, fit-for-purpose, and adaptable to the real-world challenges of emergency management. It showcases the technologies to be used in development, and how the software solution can be adopted and applied by its intended users. The products of the system can easily and freely be shared with others and the method of creating those products is transparent.
How to cite:
lemans, M.: Developing a real-time data and modeling framework for operational flood inundation forecasting for Australian coastlines, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-82, https://doi.org/10.5194/ems2022-82, 2022.
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Matteo Ponzano, Laure Raynaud, Marta Sanchez Cidoncha, Gilles Gawinowski, Manuel Fernando Soler Arnedo, Aniel Jardines, Juan Simarro, Danlin Zheng, Yan Xu, Elodie Bastie, Berangère Arnould, Florenci Rey, Ahmed Khassiba, Aurelie Peuaud, and Marie Carré
Flight delays are one of the major concerns in air traffic management. The impact of flight delays represents financial and time losses and may derive in loss of reputation of the air traffic business. On average weather accounts for roughly one-third of ATFM (Air Traffic Flow Management) delays (25% for en-route and ~50% for the airports). Examining only the top ten days with highest delays due to weather regulations from the first half of 2018, strong convective activity throughout Europe was the principal cause, with estimated cost due to airport and en-route delays reaching almost €130 million (roughly 10% of the weather delay in 2018 concentrated in only 10 days). Given these large cost figures, even minor improvement in prediction and performance of ATFM operations during significant convective weather events will yield to substantial yearly savings for the ATM (Air Traffic Management) system. Designing an efficient value chain for ATFM, that propagates weather forecasts into a series of tools to select mitigation measures at local and network levels in a collaborative ATFM operations paradigm, requires a multidisciplinary approach to gather the different stakeholders. Such an approach has been developed in the SESAR ISOBAR project, whose aim is to integrate enhanced convective weather forecasts for predicting imbalances between air traffic capacity and demand (requests to fly by airspace users, mainly airlines) and to select appropriate mitigation measures. The value chain developed in the framework of ISOBAR leverages the power of Artificial Intelligence (AI) in the different stages. AI engine is trained using a dataset of selected convective events in summer 2019, which includes forecasts from high resolution ensemble prediction systems (IFS, γ-SREPS and AROME-EPS), declared capacity in air traffic flow and initial air traffic demand. The value chain produces a solution for tactical (day 0) and pre-tactical (day -1) ATFM operations. A validation exercise was organised at EUROCONTROL Innovation Hub in March 2022 with the collaboration of ATC (Air Traffic Control) operational staff and Air Traffic Controllers from Spain, France and Europe air traffic network management. AI Engine was run for some high convective situations over Europe, which were characterised by high delays due to weather regulations. Offline simulations highlighted the added value of the solutions assessed by ATC experts. The predicted air traffic delay has been drastically cut by up 75%.
How to cite:
Ponzano, M., Raynaud, L., Sanchez Cidoncha, M., Gawinowski, G., Soler Arnedo, M. F., Jardines, A., Simarro, J., Zheng, D., Xu, Y., Bastie, E., Arnould, B., Rey, F., Khassiba, A., Peuaud, A., and Carré, M.: ISOBAR SESAR - Artificial intelligence solutions to meteo-based air traffic imbalances for network operations planning, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-122, https://doi.org/10.5194/ems2022-122, 2022.
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