Spatial domain | Global |
Spatial resolution | 0.25 degrees (~20km) |
Time domain | 2015-01-15 00:00:00 UTC to 2024-07-01 00:00:00 UTC |
Time resolution | 1 hour |
⎘
This dataset is an "analysis" containing the model's best estimate of each value at each timestep. In other words, it does not contain a forecast dimension. GFS starts a new model run every 6 hours and dynamical.org has created this analysis by concatenating the first 6 hours of each forecast. Before 2021-02-27 GFS had a 3 hourly step at early forecast hours. In this reanalysis we have used linear interpolation in the time dimension to fill in the two timesteps between the three-hourly values prior to 2021-02-27.
This dataset is designed to be used in conjunction with the NOAA GFS forecast dataset.
min | max | units | |
---|---|---|---|
latitude | -90 | 90 | decimal degrees |
longitude | -180 | 179.75 | decimal degrees |
time | 2015-01-15 00:00:00 UTC | 2024-06-30 23:00:00 UTC | seconds since 1970-01-01 00:00:00 |
units | dimensions | |
---|---|---|
precipitation_surface | kg/(m^2 s) | time × latitude × longitude |
temperature_2m | C | time × latitude × longitude |
wind_u_10m | m/s | time × latitude × longitude |
wind_v_10m | m/s | time × latitude × longitude |
Open notebook in github | Open notebook in colab |
import xarray as xr
ds = xr.open_zarr("https://data.dynamical.org/noaa/gfs/analysis-hourly/[email protected]")
ds["temperature_2m"].sel(time="2024-06-01T00:00").mean().compute()
Storage for this dataset is generously provided by Source Cooperative, a Radiant Earth initiative.
The data values in this dataset have been rounded in their binary representation to improve compression. We round to retain 9 bits of the floating point number's mantissa (a 10 digit significand) which creates a maximum of 0.2% difference between the original and rounded value. See Klöwer et al. 2021 for more information.