DOI: 10.5281/zenodo.15910942
title: EnergAIze: Ensemble generator results for selected use cases and deterministic input data

description: Multi-Parameter Spatio-Temporal Gaussian-Neighbourhood Ensemble Generation for Renewable Energy Applications

Description

This dataset contains ensemble forecasts generated using a novel multi-parameter spatio-temporal gaussian-neighbourhood method specifically designed for renewable energy meteorological applications. The method produces physically consistent, spatially coherent ensemble members that preserve cross-variable relationships critical for wind, solar, and hydropower energy assessments.

Key Features

Multi-Scale Perturbation Framework: The ensemble generation employs a sophisticated three-scale perturbation approach:



Synoptic Scale (150 km): Captures large-scale air mass uncertainty and synoptic pattern variations

Mesoscale (50 km): Represents intermediate-scale processes including convective organization and orographic effects

Local Scale (15 km): Addresses fine-scale variability related to surface heterogeneity and boundary layer processes


Physics-Informed Constraints: All ensemble members maintain meteorological realism through:



Thermodynamic consistency (Clausius-Clapeyron constraints)

Mass conservation for wind fields

Energy balance relationships between radiation, clouds, and temperature

Geostrophic balance preservation


Renewable Energy Variables: Each ensemble member includes derived variables optimized for energy applications:



Wind power density at 10m and 100m heights

Hub-height wind speed extrapolation using stability-dependent power laws

Clear sky index and photovoltaic capacity factor estimation

Basin-averaged precipitation for hydropower assessment


Technical Implementation

Mathematical Framework:

X^(i)(s,t) = X(s,t) + ε^(i)(s,t)
ε^(i)(s,t) = Σ_k α_k × G_σk × η_k^(i)(s,t)


Where:



X^(i)(s,t): Ensemble member i at location s, time t

α_k: Scale-dependent amplitude weights

G_σk: Gaussian spatial correlation filter

η_k^(i)(s,t): Independent random fields at scale k


Temporal Correlation: Implemented through first-order autoregressive process with scale-dependent persistence (synoptic: 12h, mesoscale: 6h, local: 2h)

Data Sources

The ensemble generation method is compatible with multiple input data sources:



ARCO-ERA5: Primary reanalysis dataset with 0.25° spatial resolution

WRF Model Output: High-resolution numerical weather prediction data


Variables Included

Core Meteorological Variables:



2-meter temperature (K)

10-meter wind components (u, v) (m/s)

100-meter wind components (u, v) (m/s)

Total precipitation (mm)

Surface pressure (Pa)

Surface solar radiation downwards (W/m²)

Mean sea level pressure (Pa)


Quality Control

All ensemble members undergo comprehensive quality control:



Physical bounds checking for all variables

Cross-variable consistency validation

Spatial and temporal continuity assessment

Energy balance verification


Applications

This ensemble dataset is particularly valuable for:



Wind Energy: Turbine siting, power forecasting, ramp event analysis

Solar Energy: PV system planning, intermittency assessment, grid integration

Hydropower: Basin-scale precipitation uncertainty, runoff modeling

Energy System Operations: Portfolio risk assessment, demand forecasting

Climate Risk Assessment: Extreme event probability, long-term resource planning


Methodology Reference

The methodology is based on the multi-parameter spatio-temporal gaussian-neighbourhood approach developed within the EnergAIze project, combining classical ensemble generation techniques with modern physics-informed constraints specifically tailored for renewable energy applications.

Technical Specifications

Spatial Resolution: Typically 0.25° (approximately 25 km) for demonstration cases Temporal Resolution: Hourly time steps Ensemble Size: 20-50 members (configurable) Domain Coverage: Central European focus with global applicability Format: NetCDF-4 with CF-compliant metadata

Computational Performance



Generation Speed: 10-25 ensemble members per minute for typical domains

Memory Requirements: <2GB for 3-day case studies

Scalability: Linear scaling with ensemble size and domain area

Reproducibility: Deterministic results with fixed random seeds


Validation Results

The ensemble system demonstrates:



Statistical Consistency: Flat rank histograms across all variables

Spread-Skill Relationships: Strong correlation (r > 0.7) between ensemble spread and forecast error

Probabilistic Skill: 15-25% improvement in Continuous Ranked Probability Score (CRPS) over traditional methods

Physical Realism: Maintenance of atmospheric dynamics and energy balance relationships


Citation

If you use this dataset in your research, please get in touch for citation.

Acknowledgments

This work was conducted as part of the EnergAIze project, focusing on artificial intelligence applications for renewable energy meteorology. The development was supported by [funding information].

Contact

For questions about the dataset or methodology, please contact: irene.schicker@geosphere.at

Version History



v1.0: Initial release with ARCO-ERA5 and WRF support

v1.1: Added enhanced wind calculations and visualization improvements

v1.2: Integrated machine learning extensions and pattern recognition capabilities