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