ECCC HRDPS forecast
| Spatial domain | Canada |
| Spatial resolution | 2.5 km |
| Time domain | Forecasts initialized 2026-07-09 00:00:00 UTC to Present |
| Time resolution | Forecasts initialized every 6 hours |
| Forecast domain | Forecast lead time 0-48 hours ahead |
| Forecast resolution | Hourly |
STAC (browse) · validation report
The High Resolution Deterministic Prediction System (HRDPS) is the 2.5 km limited-area weather forecast model operated by Environment and Climate Change Canada (ECCC), Canada's national meteorological service. Its continental domain covers Canada and the northern United States at a resolution that captures fine-scale weather features.
This dataset is an archive of past and present HRDPS continental forecasts. Forecasts are identified by four daily initialization times (init_time) denoting the start of the model run and step forward in time along the lead_time dimension, hourly out to 48 hours.
Examples
|
Quickstart (Github) |
|
Quickstart (Colab) |
import dynamical_catalog # dynamical-catalog>=0.8.0
ds = dynamical_catalog.open("eccc-hrdps-forecast", chunks=None)
ds["temperature_2m"].sel(init_time="2026-08-01T00", lead_time="12h")import icechunk
import pystac
import xarray as xr
catalog = pystac.Catalog.from_file("https://stac.dynamical.org/catalog.json")
collection = catalog.get_child("eccc-hrdps-forecast")
asset = collection.assets["icechunk-https"]
repo = icechunk.Repository.open(icechunk.http_storage(asset.href))
session = repo.readonly_session("main")
ds = xr.open_zarr(session.store, chunks=None)
ds["temperature_2m"].sel(init_time="2026-08-01T00", lead_time="12h")Dimensions
| min | max | units | |
|---|---|---|---|
| init_time | 2026-07-09T00:00:00Z | Present | seconds since 1970-01-01 |
| lead_time | 0 | 172800 | seconds |
| x | -14.821 | 42.306 | degrees |
| y | -12.303 | 16.7 | degrees |
Variables
Access a variable by its name (the bold identifier, e.g. ds["categorical_precipitation_type_surface"]).
Dimensions: init_time × lead_time × y × x
| variable | units |
|---|---|
categorical_precipitation_type_surface
Precipitation type (ptype)1=Rain; 2=Rain/snow; 3=Freezing rain; 4=Ice pellets; 5=Snow; 6=None; 7=Drizzle; 8=Freezing drizzle; 9=Freezing rain/ice pellets. NaN at lead time 0.
|
1 |
convective_available_potential_energy_surface
Convective available potential energy (cape)Negative values are source markers rather than energies and cover roughly half the domain. Mask values < -0.1.
|
J kg-1 |
dew_point_temperature_2m
2 metre dewpoint temperature (2d)
|
degree_Celsius |
downward_long_wave_radiation_flux_surface
Surface downward long-wave radiation flux (sdlwrf)Average flux since the previous forecast step.
|
W m-2 |
downward_short_wave_radiation_flux_surface
Surface downward short-wave radiation flux (sdswrf)Average flux since the previous forecast step.
|
W m-2 |
precipitation_surface
Precipitation rate (prate)Average precipitation rate since the previous forecast step. Units equivalent to mm/s.
|
kg m-2 s-1 |
pressure_reduced_to_mean_sea_level
Pressure reduced to MSL (prmsl)
|
Pa |
pressure_surface
Surface pressure (sp)
|
Pa |
snow_water_equivalent_surface
Snow depth water equivalent (sd)
|
m |
specific_humidity_2m
Specific humidity (q)
|
1 |
temperature_2m
2 metre temperature (2t)
|
degree_Celsius |
total_cloud_cover_atmosphere
Total cloud cover (tcc)NaN at lead time 0.
|
percent |
wind_direction_10m
10 metre wind direction (10wdir)Direction the wind blows from, clockwise from true north rather than from the rotated grid's north.
|
degree |
wind_direction_80m
80 metre wind direction (80wdir)Direction the wind blows from, clockwise from true north rather than from the rotated grid's north.
|
degree |
wind_gust_10m
Wind speed (gust) (gust)Peak wind speed at this time step, including sub-grid turbulence. Not a maximum since the previous forecast step.
|
m s-1 |
wind_speed_10m
10 metre wind speed (10si)
|
m s-1 |
wind_speed_80m
80 metre wind speed (80si)
|
m s-1 |
Details
License
Dataset licensed under CC BY 4.0 and ECCC Data Servers End-use Licence.
Attribution and citation
ECCC HRDPS data processed by dynamical.org from Environment and Climate Change Canada, used under the ECCC Data Servers End-use Licence version 2.1 (https://eccc-msc.github.io/open-data/licence/readme_en/).
Or ECCC HRDPS from dynamical.org.
Source
The source grib files this archive is constructed from are provided by ECCC MSC Open Data and the dynamical.org ECCC HRDPS grib archive on Source Cooperative.
Storage
Icechunk storage generously provided by AWS Open Data. Storage for the dynamical.org ECCC HRDPS grib archive is generously provided by Source Cooperative, a Radiant Earth initiative.
Chunks & shards
This dataset is stored in Zarr format, which splits each variable into a grid of chunks — the smallest unit read from storage. Chunks are grouped into larger shards (the objects actually written to storage), which keeps the object count manageable for long-archive datasets. When possible, aligning your reads with this dataset's chunk grid can significantly improve data access speed.
The element count and coordinate span of this dataset:
| dimension | chunk | shard |
|---|---|---|
| init_time | 1 (6 hours) | 1 (6 hours) |
| lead_time | 49 (49 hours) | 49 (49 hours) |
| y | 258 (~5.805°) | 1290 (29.025°) |
| x | 254 (~5.715°) | 2540 (57.15°) |
| uncompressed | 12.2 MiB | 612.5 MiB |
Validation report
Review the validation report to understand variable availability, missing data, known quirks, fill values, and approximate spatial, temporal, and value distributions.
Compression
The data values in this dataset have been rounded in their binary floating point representation to improve compression. See Klöwer et al. 2021 for more information on this approach. The exact number of rounded bits can be found in our reformatting code.