RDPS-Vertical

1. Overview of RDPS v9.0.0

  • Implementation Date: June 11, 2024

  • Developed by: Meteorological Service of Canada (MSC), Canadian Meteorological Centre (CMC)

  • Part of: Innovation Cycle 4 (IC-4)

  • Primary Function: Provides high-resolution, short-term weather forecasts for Days 1 and 2.

  • Grid Resolution: 10 km horizontal resolution

  • Forecast Frequency: Four forecasts per day (00, 06, 12, 18 UTC)

  • Vertical Levels: 84 hybrid staggered vertical levels

  • Coverage: North America and adjacent oceans

Major Changes in RDPS v9.0.0:

  • Global Yin-Yang domain replacing limited-area model (LAM) strategy.

  • Assimilation Cycle: Now uses GDPS-9.0.0 background fields rather than a continuous assimilation cycle.

  • Improved Satellite Data Assimilation.

2. Data Assimilation & Objective Analysis

RDPS-9.0.0 incorporates real-time observations into its forecast system through a 4DEnVar (Four-Dimensional Ensemble-Variational) assimilation method.

2.1 Assimilation Strategy

  • Runs every 6 hours (00, 06, 12, 18 UTC).

  • Uses background fields from the 15 km GDPS-9.0.0.

  • Data Cutoff: Observations up to T+2 hours.

  • Analysis increments every hour for improved forecast accuracy.

2.2 Observational Data Sources

  1. Satellite Data

    • Microwave Sensors: AMSU-A, MHS, ATMS, SSMIS, MWHS-2

    • Infrared Sensors: IASI, CrIS (AIRS no longer used)

    • Geostationary Satellite Imagers

  2. Ground-Based Observations

    • Radiosondes (TEMP, PILOT)

    • Surface Weather Stations (SYNOP, METAR, BUOY, SHIP)

    • Aircraft-Based Weather Observations (AMDAR, TAMDAR)

    • GPS Radio Occultation (GPS-RO)

  3. Quality Control & Bias Corrections

    • Satellite radiance bias correction using GDPS-derived coefficients.

    • Dynamic bias correction applied to aircraft temperature data.

    • Inter-channel error correlation for all satellite data.

    • Cloud-affected (all-sky) radiance assimilation for AMSU-A, ATMS, and MHS.

3. Forecast Model Configuration

The RDPS-9.0.0 forecast model is based on the GEM (Global Environmental Multiscale) Model v5.2.

3.1 Model Setup

  • Numerical Grid: Global Yin-Yang domain at 10 km resolution

  • Time Integration: Iterative-implicit semi-Lagrangian scheme with a 300-second timestep.

  • Initialization Scheme: 4D Incremental Analysis Update (4D-IAU).

  • Prognostic Variables Include:

    • Temperature, Wind, Surface Pressure, Cloud Condensate, Ozone.

    • Humidity, Precipitation Rate, Precipitation Type, Turbulence (TKE).

3.2 Physical Parameterizations

  • Deep Convection: Kain & Fritsch Scheme (for storm forecasting).

  • Shallow Convection: Bechtold mass-flux scheme.

  • Cloud Microphysics: Two-moment microphysics model.

  • Boundary Layer Mixing: Turbulent Kinetic Energy (TKE) model.

3.3 Land Surface Modeling

  • Soil moisture and temperature forecasts provided by the ISBA (Interactions between Soil, Biosphere, and Atmosphere) model.

  • Snow depth, sea ice fraction, and soil temperature initialized from GDPS.

  • Sea Surface Temperature (SST) and sea-ice thickness sourced from GIOPS (Global Ice Ocean Prediction System).

4. Computational Performance

  • Runs on 3,600 computing cores, completing analysis in ~20 minutes.

  • Nested-grid approach for improved forecast precision.

  • High-performance computing optimizations reduce model runtime.

5. Applications and Use Cases

RDPS-9.0.0 plays a crucial role in weather prediction for North America, supporting various industries and governmental operations:

  • Extreme Weather Forecasting: Hurricanes, snowstorms, heatwaves, thunderstorms.

  • Aviation & Transportation: High-resolution wind, visibility, and turbulence forecasts.

  • Energy Sector: Predicts power grid demand under extreme weather.

  • Agriculture & Food Security: Drought risk assessment, frost prediction, irrigation management.

  • Disaster Management: Early warnings for severe weather impacts.

6. Summary & Importance

The Regional Deterministic Prediction System (RDPS) v9.0.0 is an advanced short-term forecasting model that enhances Canada’s ability to predict and respond to extreme weather events. With improved data assimilation, increased resolution, and more accurate physics parameterizations, it delivers critical guidance for meteorologists, disaster response teams, and energy planners.

7. References & Further Reading

  • RDPS v9.0.0 Technical Note: Link

  • RDPS Changelog: View Here

  • Meteorological Service of Canada (MSC) Data Access: Access Here

Conclusion

RDPS v9.0.0 marks a significant advancement in regional weather prediction by utilizing high-resolution modeling, ensemble data assimilation, and cutting-edge observational inputs. It serves as a powerful tool for climate adaptation, disaster preparedness, and sustainable resource management, ensuring more reliable and precise weather forecasts for Canada and North America.

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