REPS
1. Overview of REPS v5.0
Implemented on: June 11, 2024
Developed by: Canadian Meteorological Centre (CMC)
Forecast Type: Short-range (72-hour) probabilistic forecasts
Grid Resolution: ~10 km (rotated latitude-longitude LAM grid)
Vertical Levels: Hybrid vertical coordinate system with 84 staggered levels
Forecast Frequency: 4 times daily (00, 06, 12, 18 UTC)
Coverage: North America and adjacent oceans
Number of Ensemble Members: 21 total (1 control + 20 perturbed members)
Key Updates in Version 5.0:
Assimilation switch: Now uses GDPS analysis instead of RDPS.
Upgraded perturbation scheme: More refined SPP (Stochastic Perturbation of Physics Parameters).
Extended uncertainty representation: More diverse initial condition perturbations.
New surface initialization method: Directly obtained from GDPS surface analysis.
2. Data Assimilation & Initial Conditions
2.1 Assimilation Strategy
Unlike other deterministic models, REPS does not use direct data assimilation. Instead, it leverages GDPS and GEPS for its initial conditions.
Upper-Air Initial Conditions:
Derived from GDPS analysis (15 km resolution) interpolated onto the REPS 10 km grid.
Uses GEPS ensemble perturbations, modified via Homogeneous Isentropic Perturbations (HIP).
Recentered around GDPS mean analysis to maintain physical consistency.
Surface Initial Conditions:
Directly obtained from the GDPS surface analysis.
Not perturbed to ensure forecast consistency.
2.2 Perturbation Techniques
HIP (Homogeneous Isentropic Perturbations):
Introduced in the boundary layer (variance 0.8) and upper atmosphere (variance 0.5).
Ensures realistic spread in the ensemble initialization.
SPP (Stochastic Perturbation of Physics Parameters):
Adjusts subgrid-scale physical processes randomly.
Uses a higher-resolution grid (384 × 192 points vs. previous 16 × 8 points) for better representation of model uncertainty.
Boundary Condition Perturbations:
Provided by GEPS (perturbed every 1 hour).
Helps capture forecast divergence in high-impact weather scenarios.
SKEB (Stochastic Kinetic Energy Backscatter):
Not active in REPS 5.0 (removed due to performance limitations).
3. Forecast Model Configuration
3.1 Model & Grid Setup
Core Model: GEM (Global Environmental Multiscale) v5.2.0
Grid Setup:
Rotated latitude-longitude grid (1108 × 1082 points)
Uniform 10 km horizontal resolution
Time Integration:
Iterative-implicit semi-Lagrangian scheme (300-second timestep)
Lid Nesting:
Model top at ~17 hPa with hybrid SLEVE coordinate.
3.2 Prognostic Variables
Core Atmospheric Fields:
Wind (E-W & N-S components), temperature, surface pressure, specific humidity.
Cloud & Precipitation Fields:
Cloud condensate, rain, snow, total ice mixing ratio, turbulence (TKE).
Derived Forecast Fields:
Mean Sea Level Pressure (MSLP), precipitation rate, boundary-layer height.
3.3 Key Model Enhancements
Improved Stochastic Parameterization (SPP)
Adjusts turbulence, cloud microphysics, and convection rates.
Better Land-Surface Coupling
Snow depth and soil temperature now evolve dynamically.
More Realistic Convective Triggers
Updated Kain-Fritsch deep convection scheme.
4. Physics Parameterizations
4.1 Atmospheric Physics
Deep Convection: Kain & Fritsch scheme
Shallow Convection: Bechtold mass-flux scheme
Microphysics: Sundqvist scheme for cloud and precipitation processes
Boundary Layer Turbulence: Turbulent Kinetic Energy (TKE) model
Radiation Model: Li-Barker correlated k-distribution scheme
4.2 Land-Surface & Ocean Processes
Surface Model: ISBA (Interactions between Soil, Biosphere, and Atmosphere)
Sea Ice Model: Uses dynamic sea ice thickness & fraction
SST Initialization: Updated from GIOPS ocean analysis
Subgrid Orography Effects: McFarlane & Lott-Miller parameterization
5. Ensemble Forecast Interpretation & Uncertainty Quantification
5.1 Spread & Skill Representation
Ensemble Mean: Averaged forecast from all 21 members.
Probability Forecasts:
Probability of exceeding a temperature/precipitation threshold.
Extreme Member Analysis:
Identifies upper and lower bounds of uncertainty.
5.2 Key Forecast Applications
Severe Weather Prediction:
Hurricane tracks, snowstorms, heatwaves, thunderstorms.
Aviation & Transportation:
Wind, turbulence, and visibility forecasts.
Energy & Infrastructure:
Load balancing for power grids during extreme weather.
Disaster Management:
Probability-based flood, wildfire, and ice storm risk assessments.
6. Summary & Key Advancements in REPS v5.0
Higher horizontal resolution (~10 km) for better regional forecasts.
More advanced ensemble initialization techniques (HIP & SPP).
Improved deep and shallow convection schemes.
Enhanced coupling with land, ocean, and ice models.
More efficient uncertainty quantification for high-impact weather events.
7. References & Further Reading
REPS v5.0 Technical Note:
REPS Changelog:
MSC Open Data Portal:
8. Conclusion
The Regional Ensemble Prediction System (REPS) v5.0 is a state-of-the-art short-range probabilistic forecasting system, enhancing uncertainty representation and regional weather prediction accuracy. Its high-resolution forecasts, advanced physics, and improved ensemble perturbation schemes make it an essential tool for meteorologists, disaster planners, and climate resilience initiatives.
Last updated
Was this helpful?