Automated impact-based warnings for energy system stress: Integrating multi-hazard detection, ensemble probabilistics, and causal analysis into the forecast and warning value chain

Engineering notes

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Chinese explanation / 中文解读

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Original abstract

Impact-based warning systems increasingly aim to translate meteorological forecasts into sector-specific risk information. However, a critical gap persists for the energy sector: the weather situations that cause the most severe grid stress — compound renewable generation failures, supply-demand mismatches, and cascading events — often do not exceed conventional meteorological warning thresholds. A Dunkelflaute or a storm-to-icing cascade may be meteorologically unremarkable yet operationally devastating. This contribution presents an automated, end-to-end framework that extends the forecast and warning value chain to energy system impacts, demonstrating how multi-hazard detection, ensemble-based probabilistic forecasting, and causal modeling can generate first-guess impact warnings for a sector not yet systematically served by warning services.The framework implements automated hazard detection for (currently) five energy-relevant weather hazards — wind speed ramping, storm gusts, precipitation and icing, heatwaves, and cold spells — using configurable threshold-based methods with multi-level severity classification. For the critical forecast-to-warning step, an ensemble-based wind power ramping detection system processes ECMWF IFS 51-member ensemble forecasts through timing-based clustering that preserves extreme events typically destroyed by spatial averaging. This yields five (adjustable to more) probabilistic scenarios with severity classifications from −4 to +4, explicitly accounting for modern turbine storm control regimes (25–35 m/s). A complementary solar ramping system uses a three-component approach decoupling atmospheric convective threat from photovoltaic vulnerability, eliminating nighttime false alarms. A compound event module identifies co-occurring and cascading hazard sequences that amplify grid stress beyond what any single-hazard warning would indicate.Weather pattern classification provides the seamless bridge between synoptic-scale NWP guidance and local impact probability, enabling regime-conditioned automated first-guess warnings. We explore the application of the Cause-Trigger framework (Hlaváčková-Schindler et al., 2025) to distinguish atmospheric processes that cause energy system hazards from those that merely trigger them — with direct implications for achievable warning lead times. The framework combines physics-informed machine learning with transparent threshold-based detection, addressing the session's question of whether data-driven model output can meaningfully contribute to warning systems.Input data span from ERA5 and ARA high-resolution reanalysis through operational IFS and AIFS forecasts to Destination Earth Digital Twins, enabling seamless analysis across climate, seasonal, and operational timescales. We present results for the Austrian Alpine domain, where complex terrain amplifies forecast uncertainty, and show how reanalysis-derived hazard climatologies serve as the reference baseline for evaluating forecast-based impact warnings — closing the loop from observing through forecasting to warning and response within the energy sector.

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