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From Meteorological Forecasts to Impact-Based Warnings: Challenges and Interdisciplinary Synergies (organized by young researchers and dedicated to young researchers)
Further Sessions will be:
Session I: Tuesday, 28 January 2025, from 3:00 pm - 4:00 pm, Lecture Hall S003
Session II: Wednesday, 29 January 2025, from 2:00 pm - 3:00 pm, Lecture Hall S003
Session Abstract
Issuing warnings, be they hazard-based or impact-oriented, requires a data processing pipeline to generate a reliable warning product which can be distributed to the end user. Each step along the chain, including both manual and automatic, offers its own challenges to refine the data.
Traditionally, hazard-based warnings are derived from initially complex, gridded weather forecast data and have to be simplified for easy understanding by the public. Yet, hazard warnings do not provide specific information regarding their consequences, for example, physical damage to infrastructure, disruption of societal activities, or economic losses. Under the umbrella initiative Early Warnings for All (EW4A), the World Meteorological Organization advocates for advancing early warning systems, increasingly tailoring them to the needs of specific users, focusing on impacts, and informing actions to mitigate damage. Developing accurate and useful impact-based forecasts is challenged by limited data and information, lack of standardized technical protocols, issues sharing impact data and little knowledge of the needs of various user groups.
Verification is crucial to ensure the quality of any warning system. A dense network of measurements. Yet even if this is given, as simplifications are made to issue a pleasing product to the user, verifying warnings poses several challenges. Impact warnings are even more challenging to verify, and guidelines are needed.
This session aims to unite scientists, natural catastrophe modellers, weather forecasters, tool developers, stakeholders, and policy professionals and discuss advancements and challenges related to the warning chain. We welcome inputs on the identification of extreme weather and impacts, the generation of hazard or impact warnings and forecasts, their verification, visualization, uncertainty, and user needs. The session features expert presentations and a panel discussion to allow the community to collaborate on developing storylines, marking a significant step forward in weather and impact modelling.
Presentations
DeepWaive: Probabilistic 2D Flood Forecasts using a Generalized Hybrid Model
Julian Hofmann, Adrian Holt
FloodWaive Predictive Intelligence GmbH, Germany
The increasing risks due to hydro-meteorological events necessitate innovative and flexible forecasting tools for effective management of flood risks. FloodWaive addresses this challenge with DeepWaive, a groundbreaking generalized Deep Learning (DL) model for probabilistic 2D flood forecasting. Our approach overcomes the limitations of traditional hydrodynamic models, offering rapid, accurate, and scalable impact-based flood forecasting across diverse geographical domains.
DeepWaive integrates in-house developed DL architectures with hydrodynamic 2D models, enabling ad-hoc simulations of pluvial and fluvial events of varying intensities and durations over extensive areas. Unlike conventional AI models that require retraining for each new domain, DeepWaive generalizes across different topographies and regional characteristics, eliminating the need for domain-specific retraining and enhancing scalability.
With a speed-up factor of up to 10^6, DeepWaive can translate precipitation or discharge values into spatial hydraulic flooding processes within seconds. This capability facilitates the processing of several ensemble rainfall forecasts into impact and probability-based forecasts and warnings. The model's applications extend to dynamic risk analyses, real-time evaluation of flood protection measures, and dam break simulations.
While still in development, DeepWaive represents a significant leap in flood forecasting technology. Our goal is to offer a universally deployable and comprehensible, real-time flood prediction tool, empowering crisis and flood risk management to make informed decisions quickly, potentially saving lives and reducing economic losses.
User-centred Evaluation of Cold Wave Forecasts for Disaster Risk Reduction in Lesotho
1ECMWF, United Kingdom; 2510 The Netherlands Red Cross, NL; 3The Lesotho Red Cross Society, Lesotho
In the real world, people must decide how to respond to hazardous weather or climate events. Forecasts are crucial for timely decision-making, yet relevant services may be lacking or not tailored to user needs and often fail to prompt action. The I-CISK project (Innovating Climate Services through Integrating Scientific and Local Knowledge) aims to develop a new generation of climate services that directly address users' requirements.
Evaluating the performance of these services is key to their success and guides their development. Understanding the strengths and weaknesses of a service helps users decide whether to incorporate it into their operational activities. However, standard statistical verification is often complex and not easily applicable in real-world, action-driven scenarios. A 'user-centred' evaluation approach considers the specific context in which forecast data is used, making the results more relevant and understandable.
We present a flexible process for user-centred evaluation and demonstrate its application to cold wave forecasting for disaster risk reduction in Lesotho, one of the I-CISK' Living Laboratories'. Lesotho's mountainous terrain makes its population vulnerable to cold waves, which have caused fatalities, livestock losses, and severe transport disruptions. We evaluate ECMWF temperature and snow forecasts within the Lesotho Red Cross Society's draft Early Action Protocol framework for cold wave disaster risk reduction.
Flood Forecasting, Preparedness and Early Warning case studies from South East Asia
Sujana Dhar
flood forecasting specialist , India
The importance of early warning for flood disaster preparedness cannot be less stressed on. Techniques of visualization and communication have evolved from desktop application of dash boards to mobile friendly application alerts. The dovetailing of real time data and 72-hour early warning forecasted data from the Meteorological Department has helped save lives and conduct safe evacuation of livestock and persons with disabilities, from hospitals and jails, amongst others.R2R framework has also stressed on the importance of early warning systems for preparedness and response to disasters.
This paper will include case studies from India demonstrating the preparedness and responsiveness to flood disasters. Their early warning system architecture and alert protocols will be presented.
The use of real time data and integration of numerically forecasted data towards creating an early warning system for South East Asia, and various parts of India will be showcased in this study including the use of hydrological models. The endeavour of the author is to imbibe the sharing of best practices, as well as providing room for discussion of methodological problems in risk modeling and visualization, including resilience analytics and improvements towards disaster management.
