jimtseng/apac-nwp-forecast

APAC NWP Forecast — analysis-ready Zarr cube

Hourly numerical weather prediction (NWP) forecasts for the Asia-Pacific region (lat −44…46, lon 92…154 — Japan, Taiwan, Southeast Asia, Australia and surrounding seas), archived every run from multiple weather models. Variables are chosen for solar-energy applications (irradiance, temperature, cloud cover, wind, …).

Every run is stored in one growing, analysis-ready Zarr cube per model with axes init_time × lead_time × latitude × longitude — a record of past forecasts, useful for forecast-correction / model-blending ML (not reconstructable from reanalysis). New runs are appended daily; the run_init axis is pre-allocated to the end of 2027, so the cube grows in place. The full model grid is kept, including ocean cells.

Data is sourced from the Open-Meteo open-data distribution and exported with the open-source open-meteo toolchain.

Dataset family

Dataset	Format	Role
jimtseng/apac-nwp-forecast (this one)	Zarr cube (Mode A, sharded)	Analysis-ready Silver — use this
jimtseng/apac-nwp-forecast-raw	per-run parquet	Bronze — immutable per-run capture (from go-forward onward)
jimtseng/apac-nwp-forecast-zip	per-run .zarr.zip	Historical cold archive (the pre–Bronze original capture)

Coverage at a glance

Spatial extent — dwd_icon GHI over the whole domain (−44…46°N, 92…154°E):

Example jma_msm variables (~5 km, Japan domain) — many fields beyond GHI:

Cloud cover (%)	10 m wind speed (m/s)

Models

Model	Provider	Resolution	Runs/day	Horizon	Grid (incl. ocean)
jma_msm	JMA (Japan)	0.0625°×0.05° (~5 km)	8 (3-hourly)	78 h (00/12 UTC) / 39 h (other runs)	473×481, 22.4–47.6°N · 120–150°E
dwd_icon	DWD (Germany)	0.125° (~11 km)	4	180 h (7.5 days)	721×497, −44…46°N · 92…154°E

Each model is a separate cube: jma_msm_silver.zarr, dwd_icon_silver.zarr.

Notes:

• Hourly values beyond a model's native hourly range (ICON > 78 h) are interpolated by Open-Meteo from 3-hourly steps (solar-geometry-aware clearness-index for radiation, hermite otherwise). Daily totals stay reliable; sub-3-hourly cloud variability there is smoothed.
• Horizon is run-dependent (jma_msm: 00/12 UTC reach 78 h, other runs 39 h). Slots beyond a run's real horizon read back as the missing-value sentinel (NaN / masked).
• Ocean cells carry real model values. elevation is 0 over sea — use it (or location_id) to mask land/sea.

Structure

Each <model>_silver.zarr is a Zarr v3 store (Mode A):

• dims (run_init, lead, latitude, longitude) — lead = forecast lead time in hours after run_init (valid_time = run_init + lead). run_init grows as new runs are appended.
• coords run_init, lead, latitude, longitude, plus 2-D static elevation and location_id (latitude, longitude) (stored once).
• data variables — each weather variable is its own array in its native dtype (uint8 / uint16 / float32).
• sharding — one shard per run (run_init=1 × all-lead × whole-grid) containing small inner chunks, so point/series reads touch few bytes while file counts stay HF-friendly.
• slot_filled — (run_init,) int8 marker: 1 = this run has data, 0 = empty future slot.

Unwritten (empty / beyond-horizon) cells read back as the sentinel (NaN for float, masked for int).

Schema (variables)

Variable	Type	Description
shortwave_radiation_wattPerSquareMetre	uint16	GHI, backwards-averaged over the previous hour
direct_radiation_wattPerSquareMetre	uint16	Direct horizontal irradiance
diffuse_radiation_wattPerSquareMetre	uint16	Diffuse irradiance
direct_normal_irradiance_wattPerSquareMetre	uint16	DNI (unstable at sun elevation < 5°; filter before use)
temperature_2m_celsius	float32	2 m air temperature
relative_humidity_2m_percentage	uint8	2 m relative humidity
wind_speed_10m_metrePerSecond	float32	10 m wind speed
surface_pressure_hectopascal	float32	Surface pressure
precipitation_millimetre	float32	Hourly precipitation
cloud_cover_percentage (+ _low / _mid / _high)	uint8	Cloud cover layers
snow_depth_metre	float32	Snow depth (dwd_icon only)
snowfall_water_equivalent_millimetre	float32	Hourly snowfall, water equivalent (dwd_icon only)
elevation	float32	Grid-cell elevation (m); 0 over sea
location_id	int32	Grid point index within the model grid

Per-model variable set: dwd_icon has 15 weather variables; jma_msm has 13 (no snow — its upstream source provides none). The earliest dwd_icon runs (2026-03-19/20/21) predate the snow addition and also lack snow (those slots are the sentinel).

Integer columns reflect the source quantization (radiation step 1 W/m², cloud/humidity integer %); values are identical to the float representation, raw grid-cell values (no elevation downscaling).

Usage

import xarray as xr

# open the whole growing cube straight from HF (lazy; reads only the chunks you touch)
ds = xr.open_zarr(
    "hf://datasets/jimtseng/apac-nwp-forecast/dwd_icon_silver.zarr",
    consolidated=True,
)

# only the runs that actually have data (skip empty future slots)
ds = ds.isel(run_init=(ds["slot_filled"] == 1))

# a point time series: all runs × all leads at one grid point
ts = ds["shortwave_radiation_wattPerSquareMetre"].sel(
    latitude=25.0, longitude=121.5, method="nearest"
)

# "all forecasts valid at time T" is the diagonal run_init + lead == T

Needs a recent zarr>=3 and huggingface_hub. hf:// streaming reads shards on demand; for heavy use, download the cube (or specific variables) locally first.

License & attribution

Data: CC BY 4.0 — Weather data by Open-Meteo.com, based on open data by JMA (MSM) and DWD (ICON). Please retain this attribution when redistributing or displaying the data.