MedEWSa - Hazard forecasting, uncertainty and hazard indicators

This MedEWSa slide deck explains how forecasts of physical variables become signals of hazard. Comparing them with thresholds, either statistical return periods or values set by users, identifies potentially hazardous conditions, as shown with EFAS flood forecasts from CEMS. It discusses climatology and anomalies, and how predictability depends on lead time, variable and spatial scale. It shows how different timescales support readiness, preparedness and action. It explains why forecasts are never perfect and how ensemble forecasts express uncertainty as probabilities. It also covers ECMWF's extreme-weather indices, the Extreme Forecast Index (EFI) and Shift of Tails (SOT), and the factors that affect confidence in a forecast. Finally, it introduces hazard indicators that combine several variables into one decision-ready signal, such as the Fire Weather Index (FWI), the Universal Thermal Climate Index for heat stress (UTCI) and the Standardised Precipitation-Evapotranspiration Index for drought (SPEI). Its central message is that the best forecast is the forecast that is reliable enough, early enough and detailed enough to support the action required.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-26
DOI
https://doi.org/10.5281/zenodo.23667614
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
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article

MedEWSa - Hazard forecasting, uncertainty and hazard indicators

Nikolaos Mastrantonas
Zenodo (CERN European Organization for Nuclear Research)
Meteorological Phenomena and Simulations
article

MedEWSa - Hazard forecasting, uncertainty and hazard indicators

Nikolaos Mastrantonas
article en

Abstract

This MedEWSa slide deck explains how forecasts of physical variables become signals of hazard. Comparing them with thresholds, either statistical return periods or values set by users, identifies potentially hazardous conditions, as shown with EFAS flood forecasts from CEMS. It discusses climatology and anomalies, and how predictability depends on lead time, variable and spatial scale. It shows how different timescales support readiness, preparedness and action. It explains why forecasts are never perfect and how ensemble forecasts express uncertainty as probabilities. It also covers ECMWF's extreme-weather indices, the Extreme Forecast Index (EFI) and Shift of Tails (SOT), and the factors that affect confidence in a forecast. Finally, it introduces hazard indicators that combine several variables into one decision-ready signal, such as the Fire Weather Index (FWI), the Universal Thermal Climate Index for heat stress (UTCI) and the Standardised Precipitation-Evapotranspiration Index for drought (SPEI). Its central message is that the best forecast is the forecast that is reliable enough, early enough and detailed enough to support the action required.

Zenodo (CERN European Organization for Nuclear Research)
European Centre for Medium-Range Weather Forecasts (GB)
Openalex Percentile: Top 21%
Meteorological Phenomena and Simulations
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