Problem statement

Weather and flood forecasts have never been more accurate, yet accurate warnings still fail to prevent losses. The WMO Guidelines on Multi-hazard Impact-based Forecast and Warning Services (WMO-No. 1150) were written around exactly this paradox: services issue technically correct warnings (“80 mm of rain expected in the next 24 hours”) and communities still flood, because nobody translated the meteorology into consequences. A rainfall total is not a decision. What the weather will be is not what the weather will do.

Impact-based forecasting (IBF) closes that gap. It combines the hazard forecast you already produce with two layers your service rarely holds alone, exposure (who and what is in the path) and vulnerability (how susceptible they are), and turns the combination into warning levels that people can act on.

This module gives early-career forecasters a compact, hands-on introduction to IBF fundamentals: the risk vocabulary, the likelihood times impact severity warning matrix, the actors who co-develop it, and a layer-by-layer build of a minimal flood IBF that you assemble yourself in a Python notebook on a clearly labelled synthetic demonstration region.

Short description

Learn how impact-based forecasting turns a rainfall forecast into an actionable warning. You will work with the UNDRR and WMO risk vocabulary (hazard, exposure, vulnerability, impact, risk), read and apply an impact matrix, map the actor landscape around a warning, combine hazard, exposure, and vulnerability layers into Yellow, Amber, and Red warning zones in a notebook, and finish as the duty forecaster assigning warning levels in an escalating scenario.

Audience

Early-career forecasters and hydrometeorologists at National Meteorological and Hydrological Services who produce or interpret rainfall and flood forecasts and want to understand how impact-based warning services are built. Fundamentals level: no prior IBF experience is assumed.

Estimated effort

About 1 hour 45 minutes total (1.5 to 2 hour range). Self paced.

Prerequisites

• Basic familiarity with rainfall and flood forecasting vocabulary (forecast, threshold, probability of exceedance).
• Comfort reading maps of gridded fields.
• Python at run-a-notebook level (run cells, edit a few values). The notebook uses only numpy and matplotlib, and all data are generated inside it. Recommended platfroms: HydroShare or Google Colab.

See unit 1.3 for details and refreshers.

What you will be able to do by the end

1. Explain the difference between a phenomenon-based forecast and an impact-based forecast, with a concrete flood example.
2. Define hazard, exposure, vulnerability, impact, and risk in the UNDRR and WMO sense, and classify real scenario statements into these categories.
3. Read a likelihood times impact severity warning matrix and explain why its levels and thresholds are set nationally with stakeholders.
4. Combine hazard, exposure, and vulnerability layers through a transparent impact matrix into Yellow, Amber, and Red warning zones on a map.
5. Adjust a warning decision when a temporary exposure change (a large public festival) alters the impact picture, and say why this is forecaster judgment rather than a matrix rule.
6. Draft an impact-based warning message with the WMO-recommended content: what, where, when, likelihood, expected impacts, recommended actions.

Acknowledgments

Module developed for the World Meteorological Organization (WMO) capacity building activity under the Early Warnings for All Initiative. The project is funded jointly by the "Early Warning System for Floods - United States Department of States (EWS-F US DoS)" and the "Supporting Regional Cooperation to Strengthen Operational Multi-Hazard Forecasting and Early Warning Systems at National Level in the South-West Indian Ocean (CREWS South-West Indian Ocean)" projects.