Appendix D: Data Engineering
1. Purpose and Scope
1.1 Goal and High-Level Objectives
The primary goal is to build a data infrastructure and pipeline that can:
Continuously ingest multi-scale meteorological, hydrological, and climate data,
Transform and integrate those data into model-ready features,
Leverage AI/ML to predict heatwaves (on various lead times) and quantify downstream impacts on health, infrastructure, and resources.
1.2 Motivation
Increasing Frequency/Severity of Heatwaves: Climate change continues to amplify the intensity and duration of heat events, making accurate forecasting crucial.
Urban Resilience: Dense urban environments (e.g., Toronto) suffer amplified temperature spikes (urban heat islands), stressing energy grids, water supplies, and public health systems.
Multi-Domain Requirements: True resilience requires merging meteorological data with water, energy, and public-health indicators to anticipate cascading failures.
1.3 Overall Technical Vision
End-to-End Pipeline: Data ingestion → storage → transformation/feature engineering → model training/inference → decision support.
Hybrid HPC/Cloud environment for large-scale data handling and real-time inferencing.
Multi-Timescale approach: short-term (nowcasting to 3-day leads), medium-term (5–10 days), and long-term (seasonal, multi-decadal) scenario planning.
2. Core Data Sources for Heatwave Prediction
2.1 Numerical Weather Prediction (NWP) Forecasts
2.1.1 High Resolution Deterministic Prediction System (HRDPS)
Spatial Resolution: ~2.5 km. This fine resolution is paramount for capturing microclimate variations, urban heat island effects, and localized convection.
Temporal Resolution: Updated hourly or every few hours, typically providing short-range forecasts (e.g., up to 48 hours).
Strengths:
Resolves small-scale features: sea breezes, city-level thermal anomalies, topographic influences.
Ideal for urban or complex terrains where a coarse model might overlook critical temperature peaks.
Constraints:
Higher computational cost and large data volume (~2.5 km grids produce significant data).
May have a shorter forecast horizon (e.g., 36–48 hours) due to computational intensity.
2.1.2 Regional Deterministic Prediction System (RDPS)
Spatial Resolution: ~10 km covering North America.
Temporal Resolution: Often produces 6-hourly or hourly output, updated 2–4 times/day, with up to 84-hour lead time.
Use Cases:
A broader “regional” vantage to capture mesoscale phenomena (regional heat domes).
Provides boundary conditions or additional layers (e.g., vertical profiles) to downscale for the HRDPS domain.
2.1.3 Global Deterministic Prediction System (GDPS)
Spatial Resolution: ~15–40 km globally, bridging planetary-scale circulations to local predictions.
Temporal Updates: Typically 6 or 12 hours, with up to 10-day or more forecast horizons.
Value for Heatwaves:
Large-scale wave patterns (Rossby waves, high-pressure ridges) that instigate heatwaves.
Boundary conditions for nested models (RDPS, HRDPS).
2.1.4 Ensemble Prediction Systems (GEPS, REPS, NAEFS)
Ensemble Members: Usually 20+ to sample initial condition/physics uncertainties.
Spatial Resolution:
GEPS ~20–40 km (global),
REPS ~10 km (regional).
Temporal Scope:
GEPS: up to 16 days, with some extended runs (weekly to 32 days).
REPS: shorter range (up to 3 days) but higher resolution.
Advantages:
Probabilistic forecasting for heatwave thresholds (e.g., P(Tmax > 35°C) or P(Heat Index > 40°C).
Risk-based decision-making: fosters robust contingency planning (health services, energy load management).
2.2 Land-Surface and Hydrological Products
2.2.1 CaLDAS-NSRPS (Canadian Land Data Assimilation System)
Purpose: Assimilates satellite-based remote sensing (soil moisture, snow cover) and ground station data to produce consistent land-surface states every ~3 hours.
Key Variables:
Soil Moisture/Temperature at multiple layers,
Latent & Sensible Heat Fluxes,
Snow Water Equivalent (SWE),
Surface Radiative Temperature.
Integration:
Enhances land surface initial conditions in NWP models, crucial for surface fluxes that drive temperature feedback loops.
2.2.2 HRDLPS (High Resolution Deterministic Land Surface Prediction System)
Resolution: Similar to HRDPS (~2.5 km).
Focus: Forecasting land-surface variables (e.g., soil moisture, surface fluxes) over medium-range periods.
Relevance:
Soil moisture deficits can exacerbate heatwave severity (less evaporative cooling).
Predicting future dryness and land temperature feedback.
2.2.3 Water Cycle Prediction System (WCPS)
Coverage: Great Lakes/St. Lawrence + expansions.
Variables: Atmosphere-surface-hydrology coupling—runoff, river discharge, precipitation, evaporation.
Heatwave Link:
Drought conditions or stressed reservoirs during prolonged high temps.
Integrated view of water availability (irrigation, drinking water) during heat events.
2.3 Precipitation Analyses
2.3.1 RDPA (Regional Deterministic Precipitation Analysis)
Spatial: ~10 km, North American domain.
Temporal: 6-hourly, 24-hour accumulations, updated multiple times daily.
Confidence Index: The analysis indicates how much the final precipitation estimate leans on observations vs. model trial fields.
2.3.2 HRDPA (High Resolution Deterministic Precipitation Analysis)
Spatial: ~2.5 km for finer granularity.
Temporal: 6-hourly/24-hourly accumulations.
Use:
Evaluate real-time precipitation (convective storms, frontal rainfall), affecting surface cooling and local humidity.
2.3.3 HREPA (High Resolution Ensemble Precipitation Analysis)
Ensemble-based: Provides a spread or probabilistic precipitation outlook.
Utility: Understand uncertain rain events that might break a heatwave or provide partial relief.
2.4 Observational Data
2.4.1 Weather Radar Imagery
Resolution: ~1 km, updated every 5–10 minutes.
Parameters: Reflectivity (precip intensity), radial velocity, dual-polarization metrics (hail detection).
Application: Near-real-time convective monitoring. Quick-hitting storms can temporarily reduce local temps or add humidity.
2.4.2 Lightning Density
Variables: Flash location, frequency, type (cloud-to-ground vs. intra-cloud).
Temporal: Sub-hourly data.
Value: Storm identification, potential triggers for forced convection within hot air masses.
2.4.3 Satellite Observations
Spatial: Typically 1 km or coarser, some geostationary sensors at 2 km or better in IR channels.
Temporal: 15–60 minute geostationary cycles, daily for polar-orbiting (e.g., MODIS).
Key Metrics:
Land Surface Temperature (LST),
Vegetation Indices (NDVI, EVI),
Cloud Cover fraction.
Heatwave Relevance:
LST identifies hotspots (urban vs. rural).
Vegetation stress can exacerbate local heating (low evapotranspiration).
2.4.4 In Situ Observations
Coverage: Weather stations (urban, rural, airports).
Frequency: Hourly to 10-minute data.
Variables: Temperature, humidity, wind, precipitation, pressure.
Importance: Ground-truth calibration and real-time verification of forecasts and remote sensing data.
2.4.5 Hydrometric Observations
Parameters: Water levels, flows, discharge rates for rivers/reservoirs.
Frequency: Hourly or daily, depending on station automation.
Heatwave Impact: Helps detect drought conditions, water resource stress, or flooding when heat triggers convective storms.
2.4.6 Vertical Atmospheric Profiles
Measurements: Balloon radiosondes measuring T, RH, wind, pressure at multiple altitudes.
Temporal: Typically 00Z and 12Z (twice daily), special launches in severe weather events.
Derived Indices: CAPE, CIN, LCL, etc.
Use: Understanding stability and potential for thunderstorm “breaking” of heat domes.
2.5 Air Quality and Health-Related Data
2.5.1 RAQDPS (Regional Deterministic Air Quality Prediction System)
Variables: O₃, PM₂.₅, NO₂, SO₂, CO, etc.
Spatial: 2.5–10 km (varies by product).
Temporal: Hourly or 6-hourly forecasts.
Relevance: Heatwaves often correlate with elevated ozone and PM₂.₅, increasing health risks.
2.5.2 AQHI Observations & Forecasts
Air Quality Health Index: A composite measure from pollutant concentrations.
Temporal: Hourly real-time + short lead forecasts.
Use: Enhanced alerts when both temperature and AQHI exceed safe thresholds.
2.5.3 Hospital Admissions / Public Health Data
Variables: Heat-related illness ER visits, hospital occupancy, mortality rates.
Temporal: Daily aggregated or near-real-time (varies by health authority).
Integration:
Train ML models linking temperature/AQ to hospital burden.
Inform real-time resource allocation (ambulance, cooling centers).
2.6 Climate and Historical Data
2.6.1 AHCCD (Adjusted and Homogenized Canadian Climate Data)
Scope: Station-based daily data, corrected for inhomogeneities (instrument changes, relocations).
Temporal Span: Multi-decadal, often over 50+ years.
Variables: Daily max/min temperature, precipitation, sometimes wind or pressure.
Model Use: Baseline for historical extremes, calibrating frequency and intensity of past heatwaves.
2.6.2 CANGRD (Canadian Gridded Data)
Resolution: ~50 km, daily or monthly anomalies from a climate normal baseline (e.g., 1961–1990).
Application: Broader context on temperature/precip anomalies, historical dryness or warming trends.
2.6.3 CMIP5/CMIP6 + Downscaled (e.g., CanDCS-U6)
Spatial: 50+ km for raw GCM output, 10–25 km (or finer) for statistically downscaled products.
Temporal: Monthly or daily for future scenario runs (RCP/SSP-based).
Utility:
Project future expansions of heatwave frequency, intensity, duration.
Long-term planning for infrastructure resilience.
2.6.4 Daily Climate Records (Long-Term Extremes)
Coverage: ~750 urban locations with robust daily extreme data.
Relevance: Analyze top 1% temperature days, align with health impacts, compare with current forecasts to refine threshold-based alerts.
3. Key Resolutions and Data “Velocity” Summary
Below is an expanded table aligning dataset resolution, velocity, and use cases:
Data Source
Spatial Res.
Temporal Res.
Velocity
Heatwave-Specific Use
HRDPS
~2.5 km
Hourly outputs (up to 48h)
High (hourly model runs)
Local-scale forecasting, microclimate, UHI
RDPS
~10 km
6-hourly or hourly (up to 84h)
Medium (2–4 runs/day)
Mesoscale patterns, bounding region
GDPS
15–40 km
6–12 hourly (10-day horizon)
Medium (2 runs/day)
Global context, large-scale ridge detection
GEPS/REPS
10–40 km (ensemble)
6–12 hourly cycles, multi-day horizon
Medium (2 runs/day)
Probabilistic extremes, P(Temperature > threshold)
Radar (Imagery)
~1 km
5–10 min updates
Very High (live feeds)
Nowcasting of convective storms impacting local temps
Satellite (GEO)
~1–2 km
15–60 min updates
High
Land surface temperature, cloud cover, vegetation health
In Situ Stations
Point-based
Hourly or sub-hourly
Medium
Ground-truth calibration, local anomaly detection
Hydrometric
Station/basin
Daily/hourly
Medium
Drought/flood synergy with heat events
RAQDPS (Air Quality)
~2.5–10 km
Hourly/6-hourly forecasts
Medium
Heat + pollution synergy, short-range health risk analysis
AHCCD / CANGRD
5–50 km (various)
Daily/Monthly historical
Low (archival)
Baseline trend analysis, climate extremes
CMIP5/6 & Downscaled
10–50+ km (downscaled)
Daily/Monthly for future periods
Low (archival/scenario)
Long-term planning, scenario-based heatwave intensification
Health Data (admissions, etc.)
Region / aggregated
Daily or sub-daily
Variable
Linking heat indices to real-world health outcomes
4. Derived Indices and Transformations
4.1 Heat Stress Metrics
Heat Index (HI)
Formula combining T in Fahrenheit and RH to yield “feels-like” temperature.
Implementation: Convert model outputs in °C to °F, apply Rothfusz regression, convert back to °C.
Utility: More intuitive for public communication.
Wet-Bulb Globe Temperature (WBGT)
Accounts for temperature, humidity, wind, and solar radiation.
Often requires black globe temperature or direct solar radiation estimates.
Vital for workforce safety thresholds (e.g., OSHA guidelines).
4.2 Drought and Moisture Indices
Standardized Precipitation Evapotranspiration Index (SPEI)
Compares precipitation with potential evapotranspiration over various timescales (1–12 months).
Captures dryness trends that compound heatwave severity.
Soil Moisture Anomalies
From CaLDAS/HRDLPS, gauge dryness or saturation.
Low soil moisture → reduced evaporative cooling → higher local temperatures.
4.3 Atmospheric Stability
CAPE (Convective Available Potential Energy) & CIN (Convective Inhibition)
Derived from vertical profiles (RDPS, HRDPS, or radiosonde).
High CAPE can lead to storm-induced temperature breaks; high CIN can prolong stagnation.
4.4 Urban Heat Island (UHI) Metric
Combine satellite LST anomalies (nighttime vs. rural baseline), land-use classification, building density.
Spatial weighting for city core vs. suburbs.
4.5 Air Quality & Pollution Index
Weighted combination of PM₂.₅, O₃, and possibly NO₂ with temperature/humidity thresholds for a combined “Heat-Pollution Stress” indicator.
5. Integration in AI/ML Pipelines
5.1 Data Lake & ETL Workflows
Ingestion Mechanisms
Batch: Download NWP GRIB2/NetCDF, daily climate from MSC Datamart.
Real-time: Subscribe to AMQP feeds (Datamart or local HPC servers) for near-instant model output updates.
IoT / Station Data: Possibly via REST APIs or direct aggregator (e.g., SCADA for energy, city data portals for water usage).
Storage Solutions
Cloud Object Storage (S3, Azure Blob) with partitioning by date/dataset.
HPC Parallel File System (e.g., Lustre) for high-throughput processing.
Ensure robust metadata: Include model run time, forecast lead time, resolution, versioning.
Processing & Transformation
Parallelized frameworks (Spark, Dask, or HPC job schedulers) to handle large volumes.
Regridding: Tools like CDO, NCO, xESMF for consistent spatial matching across NWP and observational data.
Temporal Alignment: Interpolation/resampling for sub-hourly sync if necessary.
Data Quality & Validation
Automatic anomaly detection for missing or spurious values (e.g., T=999 °C).
Cross-check with in situ data or radar at overlapping timestamps.
Logging & alerting on ingestion failures or suspicious dataset volumes.
5.2 Feature Engineering & Model Development
Feature Construction
Temporal windows: Rolling means (e.g., past 24h average temp, lag features T(t-24), T(t-48))
Spatial context: Summaries of neighboring grid points, or CNN-based approach for full 2D fields.
Cross-domain: Combine energy usage spikes with temperature, or water usage with dryness indices to highlight resource stress.
ML/DL Architectures
CNN for grid-based inputs (radar or NWP fields).
RNN/LSTM/GRU or Temporal Convolution for time-series sequences.
Transformers for capturing longer temporal contexts (multi-day leading to multi-week).
Hybrid (CNN + LSTM) for spatiotemporal synergy.
Probabilistic & Ensemble Methods
Train models on each ensemble member or use ensemble summary stats (mean, spread, percentile).
Output distribution of possible outcomes, e.g., heatwave probability distribution over time.
Evaluation & Validation
Metrics:
RMSE, MAE for temperature predictions,
Brier score / CRPS for probabilistic outputs,
Classification metrics (Precision, Recall, F1) for “Heatwave day / not heatwave day”.
Cross-validation: Rolling-origin for time-series, or specialized spatiotemporal splits.
5.3 Real-Time Inference and MLOps
Deployment Model
Containerization (Docker) + Orchestration (Kubernetes) for scale-out.
GPU/TPU instances if deep learning inference is heavy.
Model Serving
REST/gRPC Endpoints for external dashboards, municipal alert systems, or enterprise integration.
Load Balancers for high concurrency during extreme events.
Monitoring & Retraining
Prometheus/Grafana to track pipeline performance, inference latency, and drift in error metrics.
Scheduled Retraining: e.g., weekly or after major heat events to incorporate the newest data.
Drift Detection: If actual conditions deviate significantly from predictions, trigger re-analysis or model re-calibration.
Governance & Rollbacks
Version each model release in an MLflow-like repository.
If real-time performance degrades, revert to a stable baseline model while investigating issues.
6. Practical Considerations and Best Practices
6.1 Balancing Latency, Accuracy, and Resolution
High-resolution data (2.5 km, sub-hourly) → large volumes, potential HPC bottlenecks.
Caching or downsampling for broader overviews—only use the highest resolution for final local-scale predictions.
Progressive Forecast layering: Start with coarser NWP to fill gaps, refine with HRDPS in near range.
6.2 Data Gaps, Reliability, and Redundancy
Maintain backup data feeds (HPFX servers or alternative HPC endpoints).
Implement robust QA: automated checks for outlier corrections, station offline warnings, or missing radar scans.
6.3 Integration with External Systems
Energy & Water Utilities: Possibly private APIs or SCADA data requiring special security protocols (VPN, token-based auth).
Public Health: Must anonymize or aggregate patient data to comply with privacy laws.
6.4 Long-Term Storage & Analysis
Archive historical forecasts and reanalysis in partitioned cloud storage for retrospective research, model replays, or method development.
Metadata: Implement clear naming conventions (dataset_runID_forecastHour_gridRes.nc).
6.5 Ethical and Privacy Considerations
Sensitive data (health records) must be aggregated or anonymized.
Transparent model results: disclaimers about uncertainties. Provide probability-based ranges, not just a single deterministic value.
6.6 Future Integrations
IoT sensor expansions: Hyperlocal street-level temperature sensors for microclimate mapping.
Quantum/Cloud Hybrid solutions: Evaluate advanced HPC or quantum annealing methods for complex scenario optimization.
Additional Hazards: Extend to multi-hazard synergy (smoke from wildfires, concurrent floods, or air quality crises).
7. Concluding Remarks
7.1 Key Takeaways
Multi-Dataset Integration: Effective heatwave prediction demands combining fine-scale NWP (HRDPS) with ensemble uncertainty (GEPS/REPS), real-time radar, land-surface data (CaLDAS), and climate context (AHCCD, CMIP6).
Complex Data Pipelines: A robust ETL/feature engineering layer is essential to unify different resolutions, frequencies, and data types.
Advanced AI/ML: CNNs, LSTMs, Transformers, and ensemble models can capture the spatiotemporal complexity of heat events, especially when derived indices (HI, WBGT, SPEI) are included.
Continuous, Real-Time Insights: High-velocity ingestion (radar, sub-hourly models) combined with HPC and containerized deployments ensures timely forecasting, enabling immediate alerts.
Scalability & Adaptability: The approach scales from city-level heat islands to provincial or national coverage, while retaining the capacity for future expansions (multi-hazard, multi-domain).
7.2 Benefits for Stakeholders
Municipal Authorities: Quick detection of heat hotspots for opening cooling centers, adjusting city services.
Energy/Water Utilities: Predictive load management, reservoir usage planning, and grid resilience strategies.
Healthcare: Early surge capacity planning for heat-related ER admissions.
General Public: More accurate, timely heat alerts that incorporate local intensities and air quality factors.
7.3 Roadmap for Implementation
Phase 1: Data pipeline setup & pilot city (e.g., Toronto) with real-time ingestion of HRDPS + radar + in situ stations.
Phase 2: Incorporate ensemble forecasts for probabilistic heatwave warnings, add land-surface/hydrological data.
Phase 3: Expand to nationwide coverage, refine HPC scale, ingest quantum cloud pilots if feasible.
Phase 4: Integrate advanced health data & socio-economic indicators (supply chain, workforce exposure), leading to a holistic risk/resilience platform.
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