The Global Centre for Risk and Innovation (GCRI)
Risk Academy, a Nexortium programmeRisk Science and MethodsDepartment of Quantitative Risk Analysis
Forecasts as distributions, judged by proper scoring rules
A risk number is a forecast, and a forecast is only as good as its verification.
risksacademy.comrisk-sciencediscipline
Twin figures for the central Apennines: L'Aquila, Amatrice and Norcia, counted from its layers at their stated dates.
For Probabilistic forecasting and verification: the faculty's twin of L'Aquila on the planet map, the globe and in 3D. Pick a generator, switch layers, run the instruments; every layer prints its provider and date.
Drag to move, right drag or Shift with a drag to turn and tilt, Ctrl with the wheel to zoom. Keyboard: arrows move, Shift with arrows turn and tilt, + and - zoom, 0 resets, V steps through the views, Escape clears.
Flat plan of the same data: relief, the three reference epicentres with magnitude and depth, and the numbers below; the sampler's figure shows one precomputed field with its parameters. The 3D twin opens on devices with WebGL once its data and library have been checked.
The map loads from this site as it nears the view (MapLibre 6.9.1, checked by hash).
The discipline teaches probabilistic and ensemble forecasts, the proper scoring rules that judge them (the Brier score, the continuous ranked probability score and the logarithmic score), calibration and sharpness read from reliability diagrams, hit and false alarm rates for warnings and triggers, and forecast value through the cost-loss model. Machine-learning forecasts are part of the subject now: ECMWF has run its AIFS Single model in operations since February 2025 and its AIFS ensemble since July 2025, and both are verified with the same scores as the physics-based system. A graduate can score a forecast set, read its reliability and say whether acting on it pays.
This area covers how risk is forecast as a calibrated probability distribution rather than a single value, and how forecasts are judged against what happened with proper scoring rules. It is taught in the department of Quantitative Risk Analysis, Faculty of Risk Science and Methods, through courses still to be authored.
9 subtopics, taught across its courses and labs.
Recent developments in probabilistic forecasting and verification, each with its source; sources checked on 16 October 2026. Summaries are short readings of the source; follow the link for the full account.
ECMWF's AIFS ENS became operational, producing 51-member ensemble forecasts alongside the physics-based system. It outperforms physics-based models on many measures, runs over ten times faster with far less energy, but still relies on physics-based data assimilation.
Why it matters. Hybrid physical and machine-learning ensembles change how forecast uncertainty is produced and must be validated.
Source: European Centre for Medium-Range Weather Forecasts, ECMWF's ensemble AI forecasts become operational, 1 July 2025.
News and research as they are published, per discipline, with the faculty's other tools.
The newest research, preprints, news and humanitarian reports for Probabilistic forecasting and verification, each with its source and date, load here. Open the research and news hub
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Earn two discipline achievements in the Department of Quantitative Risk Analysis. In preparation.
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