Illustrative: an ensemble of trajectories, its quantile fan and the outcome as it is observed

Risk Academy, led by the Nexus Consortium · Course catalogue · Hazards, Disasters and Resilience

Disaster Risk Reduction and Early Warning

Early warning and anticipatory action

Early warning and anticipatory action, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

Early warning and anticipatory action: foundations and practice

This area covers people-centred early warning systems across their four pillars: risk knowledge, monitoring and forecasting, dissemination and communication, and preparedness to respond.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
In authoring
Prerequisites
An L1 course in the area, or an entry review

Why now

Disaster mortality is nearly six times lower where systems are more comprehensive; gaps in risk knowledge and island states show where investment is most needed. World Meteorological Organization, Early warning systems reach new heights, but critical gaps jeopardise global progress, 12 November 2025

Learning outcomes

  1. Define impact-based forecast and the other key terms of the discipline and use them correctly in a written case
  2. Explain the four pillars of early warning and impact-based forecasting with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against warning messages and the Common Alerting Protocol, stating evidence, date and limits

Modules

  1. The four pillars of early warning
    • The four pillars of early warning
    • Impact-based forecasting
    • Warning messages and the Common Alerting Protocol
  2. Forecast verification
    • Forecast verification
    • Trigger design and thresholds
    • Anticipatory action protocols
  3. Pre-arranged finance for early action
    • Pre-arranged finance for early action
    • Last-mile dissemination
  4. Lab: the discipline's method
    • A forecast's verification gives its hit rate and false alarm rate; a user's cost of acting against the loss avoided gives a cost-loss ratio; together they give the relative economic value of acting on the forecast, which is positive only for some users and some thresholds, and that is what a trigger design must find.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-C7 Early warning, triggers and anticipatory action
  • NRS-I2 Risk communication and public-safe dashboards
  • NRS-M2 Method fabric: Hazard science

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C8 Evidence, uncertainty and provenance
  • NRS-C9 Risk models, methods and validation

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

Open now in this discipline

References

  1. World Meteorological Organization, Early warning systems reach new heights, but critical gaps jeopardise global progress, 12 November 2025
  2. Richardson, Skill and relative economic value of the ECMWF ensemble prediction system, 2000
  3. Coughlan de Perez et al., Forecast-based financing: an approach for catalyzing humanitarian action based on extreme weather and climate forecasts, 2015
  4. Murphy, What is a good forecast? An essay on the nature of goodness in weather forecasting, 1993
02Live map

Hazards now in Türkiye

Flood water, active fires, heavy rain and smoke, beside where people live, over Türkiye.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Hazards now

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Hazards now, framed on Türkiye.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological SurveySources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • MODIS and VIIRS atmosphere products, NASA LANCE and LAADS DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • Live: NASA EONET Events are indicative, not authority warnings.
  • Live: U.S. Geological Survey Events are indicative, not authority warnings.
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
  • Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
  • Optional layers (20 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
  • Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
  • Aerosol optical depth, daily: VIIRS NOAA-20 Deep Blue AOD, NASA LANCE · 13 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

Frontier course · L3 advanced, programme courses and standards

Early warning and anticipatory action: frontier methods

The methods, data, tools and studies that changed early warning and anticipatory action between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
Early warning and anticipatory action: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Disaster mortality is nearly six times lower where systems are more comprehensive; gaps in risk knowledge and island states show where investment is most needed. World Meteorological Organization, Early warning systems reach new heights, but critical gaps jeopardise global progress, 12 November 2025

Learning outcomes

  1. Explain what changed in early warning and anticipatory action since 2024 and cite the studies, data or rules behind each change
  2. Reproduce multi-hazard early warning coverage reaches 119 countries on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. Multi-hazard early warning coverage reaches 119 countries
    • The UNDRR-WMO Global Status of Multi-Hazard Early Warning Systems 2025 found 119 countries (60%) report a system, up 113% in ten years, but only 43% of small island developing states, and under one-third report risk knowledge capabilities.
    • Disaster mortality is nearly six times lower where systems are more comprehensive; gaps in risk knowledge and island states show where investment is most needed.
    • World Meteorological Organization, 12 November 2025
  2. ECMWF made its machine-learning ensemble forecast operational
    • ECMWF's AIFS ENS became operational, producing 51-member ensemble forecasts alongside the physics-based system.
    • Hybrid physical and machine-learning ensembles change how forecast uncertainty is produced and must be validated.
    • European Centre for Medium-Range Weather Forecasts, 1 July 2025
  3. Pre-agreed CERF funds released before Cyclone Jude in Mozambique
    • When Tropical Cyclone Jude met trigger thresholds in Mozambique's anticipatory action framework on 8 March 2025, 6 million dollars of pre-agreed CERF funding was released to reach over 290,000 people before landfall.
    • A worked example of forecast triggers linked to pre-arranged finance and government-led protocols, the core mechanics of anticipatory action.
    • Anticipation Hub, 11 March 2025
  4. ECMWF puts a machine-learning forecast model into operations
    • ECMWF made its Artificial Intelligence Forecasting System operational alongside its physics-based model, reporting gains of up to 20% for tropical cyclone tracks on some measures and about 1,000 times lower energy use per forecast.
    • Machine-learning forecasts are entering warning chains; forecasters and trigger designers need to verify them locally rather than assume uniform skill.
    • European Centre for Medium-Range Weather Forecasts, 25 February 2025
  5. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist
  6. Technology that helps and the risks it carries
    • Drones, Earth observation, machine learning forecasting and cell broadcast warnings.
    • Telecom and positioning failure, and AI-generated misinformation in a crisis.

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-C7 Early warning, triggers and anticipatory action
  • NRS-I2 Risk communication and public-safe dashboards
  • NRS-M2 Method fabric: Hazard science

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C8 Evidence, uncertainty and provenance
  • NRS-C9 Risk models, methods and validation

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 6 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

Open now in this discipline

References

  1. World Meteorological Organization, Early warning systems reach new heights, but critical gaps jeopardise global progress, 12 November 2025
  2. European Centre for Medium-Range Weather Forecasts, ECMWF's ensemble AI forecasts become operational, 1 July 2025
  3. Anticipation Hub, OCHA and UN CERF support anticipatory action ahead of Tropical Cyclone Jude in Mozambique, 11 March 2025
  4. European Centre for Medium-Range Weather Forecasts, ECMWF's AI forecasts become operational, 25 February 2025
  5. Richardson, Skill and relative economic value of the ECMWF ensemble prediction system, 2000
  6. Coughlan de Perez et al., Forecast-based financing: an approach for catalyzing humanitarian action based on extreme weather and climate forecasts, 2015
  7. Murphy, What is a good forecast? An essay on the nature of goodness in weather forecasting, 1993
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the course?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.

Key terms

Term

Impact-based forecast

A forecast that states what the weather or hazard is expected to do to people and assets, not only what it will be.

Term

Trigger

A pre-agreed threshold on a forecast or observation that starts a defined action.

Term

Lead time

The time between a warning and the onset of the hazard.

Term

False alarm ratio

The share of warnings that were not followed by the warned event.

ReportsContribute

Contribute evidence from this page.

Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

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