HRDPS-North

1. Overview of HRDPS-North v2.1.0

  • Implementation Date: June 11, 2024

  • Forecast System Type: Limited-area numerical weather prediction (NWP)

  • Model Framework: Global Environmental Multiscale (GEM) model v5.2.0

  • Resolution:

    • Horizontal: ~3.0 km grid spacing (2250 × 1850 grid points)

    • Vertical: 62 levels (hybrid staggered grid with SLEVE coordinate)

  • Forecast Frequency: Twice daily (00 UTC & 12 UTC)

  • Forecast Duration: 48 hours

  • Domain Coverage:

    • Alaska

    • Canadian Arctic

    • European and Russian Arctic

  • Primary Users: Meteorologists, Arctic researchers, defense agencies, environmental monitoring agencies.

2. Forecast Model Configuration

The HRDPS-North v2.1.0 operates on a high-resolution computational grid to simulate Arctic weather patterns in greater detail.

2.1 Model Specifications

  • Numerical Model: GEM v5.2.0

  • Numerical Technique:

    • Finite-difference method on an Arakawa C-grid (horizontal) and Arakawa A-grid (vertical).

  • Grid Resolution:

    • Latitude-Longitude Grid: 0.02925° (~3.0 km)

    • Regional Arctic domain.

  • Vertical Levels:

    • 62 staggered hybrid levels with a SLEVE coordinate for better handling of mountainous terrain.

    • Diagnostic levels at 10m and 1.5m for near-surface wind and humidity.

  • Time Integration:

    • Implicit semi-Lagrangian 3D solver.

    • 2-time level scheme with a 60-second timestep.

  • Boundary Conditions:

    • Refreshed hourly from the 10-km GDPS.

2.2 Prognostic Variables

  • Atmospheric Fields: Wind (E-W & N-S components), temperature, surface pressure, specific humidity.

  • Cloud and Precipitation Fields: Cloud condensate, rain, snow, total ice mixing ratio, rime volume fraction.

  • Derived Variables: Mean sea level pressure (MSLP), precipitation rate, boundary-layer height, relative humidity.

  • Geophysical Variables:

    • Surface and deep soil temperature/moisture.

    • Snow depth, snow density, snow albedo.

    • Sea ice cover, sea ice thickness, sea surface temperature.

3. Data Assimilation & Initial Conditions

3.1 Initial & Boundary Conditions

  • Lateral Boundary Conditions:

    • Provided by the 10-km GDPS (Global Deterministic Prediction System).

    • Updated every hour.

  • Initial Hydrometeor Fields:

    • "Recycled" from the previous HRDPS-North 12-hour forecast.

  • Land Surface & Atmospheric Initialization:

    • Based on the latest GDPS output.

  • Sea Ice & Ocean Conditions:

    • Obtained from the 6-hour forecast of the RIOPS (Regional Ice Ocean Prediction System v2.4.0).

3.2 Data Sources

The HRDPS-North system ingests real-time observations from various Arctic data sources, including:

  1. Satellite Data

    • Microwave radiance (AMSU-A, ATMS, SSMIS).

    • Infrared data (IASI, CrIS).

    • Geostationary weather satellites.

  2. Ground-Based Observations

    • Radiosondes, METAR, SYNOP.

    • Weather buoys and ship reports.

  3. Sea Ice Data

    • Assimilated from remote sensing and observational models.

4. Physics & Parameterizations

The HRDPS-North system includes advanced physical parameterizations to improve Arctic weather forecasting.

4.1 Atmospheric & Land Surface Processes

  • Convection Schemes:

    • Deep convection: Kain-Fritsch scheme.

    • Shallow convection: Kuo Transient scheme.

  • Microphysics:

    • P3 Bulk Microphysics Model for precipitation and cloud physics.

  • Boundary Layer Mixing:

    • Turbulent Kinetic Energy (TKE) Model.

    • Includes statistical representation of subgrid-scale clouds.

  • Radiation Model:

    • Li-Barker correlated k-distribution radiative transfer scheme (updated every 15 minutes).

  • Surface Model:

    • ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface scheme.

    • Accounts for land, sea ice, glacier, and ocean conditions.

4.2 Ocean & Sea Ice Components

  • Sea Ice Thickness & Coverage:

    • Assimilated from 6-hour RIOPS forecasts.

  • Sea Surface Temperature (SST):

    • Obtained from RIOPS model.

  • Surface Roughness Over Water:

    • Uses Charnock formulation for momentum.

    • Deacu formulation for Z0T temperature scaling.

5. Computational Performance

  • HRDPS-North runs on high-performance computing clusters.

  • Each forecast cycle completes in ~20 minutes.

  • Parallelized processing ensures rapid model updates.

6. Applications & Use Cases

The HRDPS-North model provides critical weather intelligence for high-latitude regions where traditional global models lack sufficient resolution.

6.1 Primary Applications

  • Extreme Weather Forecasting: Arctic storms, snow squalls, ice storms.

  • Maritime & Ice Navigation: Supporting shipping routes in the Arctic.

  • Defense & Security: Used by Canada’s Department of National Defense (DND).

  • Indigenous & Remote Community Support: Improved forecasting for climate-sensitive regions.

  • Wildlife & Ecosystem Monitoring: Assessing climate impacts on Arctic habitats.

7. Summary & Importance

The HRDPS-North v2.1.0 represents a major advancement in Arctic and high-latitude weather prediction. With its high-resolution 3 km grid, advanced physics, and real-time data assimilation, it provides unmatched forecasting capabilities for northern Canada, Alaska, and the Arctic.

Key Benefits:

✔ Better prediction of Arctic storms and severe weather. ✔ Improved sea ice forecasts for navigation and climate research. ✔ Enhanced operational planning for defense, transportation, and remote communities.

8. References & Further Reading

Conclusion

The HRDPS-North v2.1.0 is a critical innovation in Arctic weather forecasting, supporting climate resilience, maritime navigation, and national security. Its high-resolution, data-driven approach ensures more reliable and precise forecasts for Canada and the Arctic.

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