Tag: disasterriskreduction

White Paper on the future of weather and climate forecasting

Industry News, Water sector
The advancement of our ability to predict the weather and climate has been the core aspiration of a global community of scientists and practitioners, in the almost 150 years of international cooperation in meteorology and related Earth system sciences.
The demand for weather and climate forecast information in support of critical decision-making has grown rapidly during the last decade and will increase even faster in the coming years. The generation and provision of these services has been revolutionized by supercomputers, satellite and remote sensing technology, smart mobile devices. A growing share in these innovations has come from the private sector. At the same time progress has been hampered by persisting holes in the basic observing system.
In a new White Paper on the Future of Weather and Climate Forecasting, 30 leading experts from the research, operations and education fields therefore analyse the challenges and opportunities and set directions and recommendations for the future.
White Paper on the future of weather and climate forecasting“Undoubtedly, the 2020s will bring significant changes to the weather, climate and water community: on the one hand through rapid advancement of science and technology, and on the other hand through a swiftly changing landscape of stakeholders with evolving capabilities and roles,” writes WMO Secretary-General Prof. Petteri Taalas.
“Such changes will affect the way weather and climate forecasts are produced and used,” he says.
While National Meteorological and Hydrological Services in all 193 WMO Members are still the public entities designated by governments to provide meteorological and related services, many other providers have entered the weather forecasting business in recent decades, including intergovernmental organizations like ECMWF, private sector companies and academic institutions.
This profound change into multi-stakeholder delivery of weather and climate services is driven by several factors such as: rapidly growing demand for such services from public and private sectors; the open data policy of many public agencies and the technological advancement and affordable solutions for service delivery; and the improved skill of the forecasts, which raises demand and user confidence. As a result, there is now a new era of weather and climate services with many new challenges and opportunities.
In June 2019, WMO launched the Open Consultative Platform (OCP), Partnership and Innovation for the Next Generation of Weather and Climate Intelligence, embracing a community-wide approach with participation of stakeholders from the public and private sectors, as well as academia and civil society. The new White Paper is an output of this consultative platform.
“The White Paper is based on the concept of a weather and climate innovation cycle which is determined to advance prediction services with the aim to improve public safety, quality of life, protect the environment, safeguard economic productivity. This applies across all domains, weather, climate, oceans, hydrology and the land surface, and time span of decisions from minutes and hours, through to weeks, months and even years ahead." Says Dr Gilbert Brunet, Chair of the WMO Scientific Advisory Panel and lead author and coordinator of the group of prominent scientists and experts worldwide who contributed to the White Paper.
"With appropriate investment in science and technology, and through better PPE, the weather and climate enterprise will meet the increasing stakeholder and customer demands for tailored and seamless weather and climate forecasts. Such improvements will provide significant value to all nations. This paper makes the case that in many ways the PPE will accelerate the desired bridging of the capacity gap in weather and climate service needed for developing countries," said Dr Brunet.
The White Paper traces the development of the weather enterprise and examines challenges and opportunities in the coming decade. It examines three overarching components of the innovation cycle: infrastructure, research and development, and operation.
Chapters include:
- Infrastructure for forecasting (observational and high-performance ecosystems; advances through public-private engagement)
- Science and technology driving advancement of numerical prediction (numerical Earth-system and weather-to-climate prediction; high-resolution global ensembles; quality and diversity of models; innovation through artificial intelligence and machine learning; leveraging through public–private engagement.
- Operational forecasting: from global to local and urban prediction (computational challenges and cloud technology; verification and quality assurance; further automation of post-processing systems and the evolving role of human forecasters; leveraging through public–private engagement).
- Acquiring value through weather and climate services (user perspective; forecasts for decision support; bridging between high-impact weather and climate services; education and training).
“The decade 2021–2030 will be the decisive period for realization of the 17 United Nations Sustainable Development Goals. Most of these goals have links with the changing environment – climate change, water resources and extreme events,” he said.
“The desired outcomes in all areas require enhanced resilience, which is also the main call of the WMO Vision 2030. The advances expected in weather forecasting and climate prediction during this decade will support those ambitious goals by enabling a next generation of weather and climate services that help people, businesses and governments to better mitigate risks, reduce losses, and materialize opportunities from the new intelligence of highly accurate and reliable forecasts and predictions,” says the concluding chapter of the White Paper.
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UNDRR ROAMC: Investment in education creates more resilient societies

Agency News, Industry News, University/Research/Academia sector
Investments in safe schools provide economic returns for society and also contribute to economic recovery, according to the latest evidence. They represent a clear way to finance risk reduction initiatives in the education sector and are a direct contribution to the creation of more resilient societies.
The suspension of classes for more than a year, due to the pandemic, has not been duly dimensioned.  Until now. Education may well be one of the most affected sectors by the COVID-19 crisis. According to different analyses, students affected by school closures will obtain 3% less income during their professional lives, which will mean an approximate GDP loss of 1.5% over the remainder of the century. The pandemic will also increase school desertion and will have a profound effect on learning processes for an entire generation, without taking into account systemic effects from school closures, such as increased malnutrition, mental health effects, and other vulnerabilities.
These are devastating figures that demonstrate the need for schools and their safety to be a fundamental part of national budgetary preparations. 3 out of 5 students who did not go to school last year live in Latin America and the Caribbean.  This was emphasized during the Virtual Caribbean Safe School Initiative Pre-Ministerial Forum, held between the 15th to the 26th of last March, which was oriented towards the promotion of safety in Caribbean schools, and which is the regional mechanism for putting into practice a relationship between education and resilience.
The sixth session of the Pre-forum: School safety investment as a Key Element of Economic Recovery showed the importance of integrating into recuperation processes all the lessons learnt during this crisis.
“We should invest in gathering and use of information for observation and mapping of precise interventions, while at the same time modernizing our technological infrastructure, not only to be able to face disasters, but also in regards to contemporary realities,” stated Fayval Willams, Minister of Education, Youth, and Information of Jamaica.
According to João Pedro Azevedo, World Bank economist, the educational system must prepare its teachers to confront lower learning levels and higher inequality levels. That is to say, to prepare them for the consequences of the pandemic. “Vulnerable sectors have been those most affected by the closures during the pandemic since they have no access to the necessary technology,” added Cynthia Hobbs, an education specialist from the Interamerican Development Bank.
Andrew A. Fahie, Prime Minister of the British Virgin Islands, stated that reconstruction of the school system after the pandemic must consider technology. “Inaction cannot be an action,” he stated.
FUNDING PRIORITY
Kamal Ahmed, an international disaster risk finance consultant for the United Nations Office for Disaster Risk Reduction (UNDRR), elaborated further on the importance of investing in all aspects of school safety. “A school structure that collapses or closes interrupts nutritional programs, for example, which are a key element in social programs of many countries, and which at times are the only access to nutrition for many vulnerable children. In the case of the pandemic, if the child stays at home, and the father or mother must also stay, it reduces participation of that home in the labour market and therefore, their income,” stated Ahmed. “Investment in education produces amazing results, but also a lack of investment leaves surprising consequences.”
According to Ahmed, governments should develop a comprehensive evaluation of schools, identifying strengths and capacities, in addition to creating a matrix with safe and resilient school strategies, fragile and marginal school programs, and most vulnerable school projects. A plan must be created to compensate for learning losses.
From the financial point of view, added Ahmed, investment must be made in such a way as to reduce economic, social, environmental, physical, and lack of governance vulnerabilities. The Ministry of Education must be the priority in national budget preparation, with projections not only for costs but also for emergency funds.
Raúl Salazar, chief of UNDRR - Regional Office for the Americas and the Caribbean, stated that “loss of education increases gaps and inequality in the school system, and therefore social vulnerabilities. The disappearance of a large sector of the school population from the educational system will create significant effects on all social systems, including the economic systems.”    This clearly underlines the dimensions of systemic risk by its characteristics and requires us to confront them with a holistic and comprehensive vision.
Fahie, Prime Minister of the British Virgin Islands, specified that 20% of the 7% tax collection is applied to financial services for the improvement of schools structure. In this case, risk reduction forms a permanent part of state expenditures.
The Sendai Framework for Disaster Risk Reduction (2015-2030) is clear on this subject: “disaster risk reduction should be strengthened by providing adequate resources through various funding mechanisms, including increased, timely, stable and predictable contributions to the United Nations Trust Fund for Disaster Reduction and by enhancing the role of the Trust Fund in relation to the implementation of the present Framework”.
The world initiative for Safe Schools was accepted by the States during the signing of the Sendai Framework, which has been in effect for six years as of the 18th of March.
“In order to go forward, we must do it together, in a comprehensive way, with inter-institutional and inter-sectorial effort that would employ the disaster management abilities of various sectors which will put in motion well developed plans and strategies, financed and coherent with other large agencies, such as the Sustainable Development Objectives, and the Paris Agreement,” stated Mami Mizutori, the Special Representative of the Secretary General for Disaster Risk Reduction, during the opening day of the Pre-Ministerial Forum.
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Using AI to better understand natural hazards and disasters

Industry News
As the realities of climate change take hold across the planet, the risks of natural hazards and disasters are becoming ever more familiar. Meteorologists, aiming to protect increasingly populous countries and communities, are tapping into artificial intelligence (AI) to get them the edge in early detection and disaster relief.
Al shows great potential to support data collection and monitoring, the reconstruction and forecasting of extreme events, and effective and accessible communication before and during a disaster.
This potential was in focus at a recent workshop feeding into the first meeting of the new Focus Group on AI for Natural Disaster Management. The group is open to all interested parties, supported by the International Telecommunication Union (ITU) together with the World Meteorological Organization (WMO) and UN Environment.
“AI can help us tackle disasters in development work as well as standardization work. With this new Focus Group, we will explore AI’s ability to analyze large datasets, refine datasets and accelerate disaster-management interventions,” said Chaesub Lee, Director of the ITU Telecommunication Standardization Bureau, in opening remarks to the workshop.
New solutions for data gaps
"High-quality data are the foundation for understanding natural hazards and underlying mechanisms providing ground truth, calibration data and building reliable AI-based algorithms," said Monique Kuglitsch, Innovation Manager at Fraunhofer Heinrich-Hertz-Institut and Chair of the new Focus Group.
In Switzerland, the WSL Institute for Snow and Avalanche Research uses seismic sensors in combination with a supervised machine-learning algorithm to detect the tremors that precede avalanches.
“You record lots of signals with seismic monitoring systems,” said WSL researcher Alec Van Hermijnen. “But avalanche signals have distinct characteristics that allow the algorithm to find them automatically. If you do this in continuous data, you end up with very accurate avalanche data."
Real-time data from weather stations throughout the Swiss Alps can be turned into a new snowpack stratigraphy simulation model to monitor danger levels and predict avalanches.
Modelling for better predictions
Comparatively rare events, like avalanches, offer limited training data for AI solutions. How models trained on historical data cope with climate change remains to be seen.
At the Pacific Northwest Seismic Network, Global Navigation Satellite System (GNSS) data is monitored in support of tsunami warnings. With traditional seismic systems proving inadequate in very large magnitude earthquakes, University of Washington research scientist Brendan Crowell wrote an algorithm, G-FAST (Geodetic First Approximation of Size and Timing), which estimates earthquake magnitudes within seconds of earthquakes’ time of origin.
In north-eastern Germany, deep learning of waveforms produces probabilistic forecasts and helps to warn residents in affected areas. The Transformer Earthquake Alerting Model supports well-informed decision-making, said PhD Researcher Jannes Münchmeyer at the GeoForschungsZentrum Potsdam.
Better data practices for a resilient future
How humans react in a disaster is also important to understand. Satellite images of Earth at night - called "night lights" – help to track the interactions between people and river resources. The dataset for Italy helps to manage water-related natural disasters, said Serena Ceola, Senior Assistant Professor at the University of Bologna.
Open data initiatives and public-private partnerships are also using AI in the hope of building a resilient future.
The ClimateNet repository promises a deep database for researchers, while the CLINT (Climate Intelligence) consortium in Europe aims to use machine learning to detect and respond to extreme events.
Some practitioners, however, are not validating their models with independent data, reinforcing perceptions of AI as a “black box”, says Carlos Gaitan, Co-founder and CTO of Benchmark Labs and a member of the American Meteorological Society Committee on AI Applications to Environmental Science. "For example, sometimes, you have only annual data for the points of observations, and that makes deep neural networks unfeasible."
A lack of quality-controlled data is another obstacle in environmental sciences that continue to rely on human input. Datasets come in different formats, and high-performing computers are not available to all, Gaitan added.
AI to power community-centred communications
Communications around disasters require high awareness of communities and their comprising connections.
"Too often when we are trying to understand the vulnerability and equity implications of our work, we are using data from the census of five or ten years ago,” said Steven Stichter, Director of the Resilient America Program at the US National Academies of Science (NAS). “That's not sufficient as we seek to tailor solutions and messages to communities."
A people-centered mechanism is at the core of the Sendai Framework for Disaster Risk Reduction, a framework providing countries with concrete actions that they can take to protect development gains from the risk of disaster.
If AI can identify community influencers, it can help to target appropriate messages to reduce vulnerability, Stichter said.
With wider internet access and improved data speeds, information can reach people faster, added Rakiya Babamaaji, Head of Natural Resources Management at Nigeria’s National Space Research and Development Agency and Vice Chair of the Africa Science and Technology Advisory Group on Disaster Risk Reduction (Af-STAG DRR).
AI can combine Earth observation data, street-level imagery, data drawn from connected devices, and volunteered geographical details. However, technology alone cannot solve problems, Babamaaji added. People need to work together, using technology creatively to tackle problems.
With clear guidance on best practices, AI will get better and better in terms of accessibility, interoperability, and reusability, said Jürg Luterbacher, Chief Scientist & Director of Science and Innovation at WMO. But any AI-based framework must also consider human and ecological vulnerabilities. "We have also to identify data biases, or train algorithms to interpret data within an ethical framework that considers minority and vulnerable populations," he added.
Image credit: ITU-Camptocamp.org via Wikimedia Commons
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