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Wind Integration National Dataset (WIND Toolkit) Gridded Data Cube

/nrel/wtk_us.h5

Data Layout

The data has three dimensions: latitudinal index, longitudinal index, and temporal index and is arranged in a uniform matrix:

The coordinates are thus defined:

  • t = number of hours since 12AM on the 1st of January, 2007 UTC. Up to hour 61368, which would be 7 years worth of data.
  • y = index of lambert conic coordinates.
  • x = index of lambert conic coordinates.

Note: All data are instantaneous in time.

At any point there exist 37 variables, or datasets:

Datasets (t,x,y)

  • DIF
  • DNI
  • GHI
  • inversemoninobukhovlength_2m
  • precipitationrate_0m
  • pressure_0m
  • pressure_100m
  • pressure_200m
  • relativehumidity_2m
  • temperature_100m
  • temperature_10m
  • temperature_120m
  • temperature_140m
  • temperature_160m
  • temperature_200m
  • temperature_2m
  • temperature_40m
  • temperature_60
  • temperature_80m
  • winddirection_100m
  • winddirection_10m
  • winddirection_120m
  • winddirection_140m
  • winddirection_160m
  • winddirection_200m
  • winddirection_40m
  • winddirection_60m
  • winddirection_80m
  • windspeed_100m
  • windspeed_10m
  • windspeed_120m
  • windspeed_140m
  • windspeed_160m
  • windspeed_200m
  • windspeed_40m
  • windspeed_60m
  • windspeed_80m

There are two special datasets for indexing and time slicing:

  • coordinates (y,x) - lat/lon coordinates for every point on the x/y grid (original projection is a modified Lambert Conic)
  • datetime (t) - YYYYMMDDHHMMSS datetimestamp for every time in the time dimension

Units

  • Pressure: Pa
  • Temperature: K
  • Direction: degree
  • Speed: m s-1
  • GHI: W m-2
  • inversemoninobukhovlength_2m: m-1

Data Access

Use the h5pyd.File function to open a connection to the server.

f = h5pyd.File("/nrel/wtk-us.h5", 'r')

Most datasets can be access with the following pattern:

f[dataset][t,y,x]

The indices support numpy-style indexing, including slices. For example:

f = h5pyd.File("/nrel/wtk-us.h5", 'r')
one_value = f["windspeed_100m"][42,42,42]
timeseries = f["windspeed_100m"][:,42,42]
map = f["windspeed_100m"][42,:,:]

Downsampling can also be accomplished easily by using a numpy-style skip parameter:

downsampled_map = f["windspeed_100m"][42,::16,::16] # every 16th point
downsampled_timeseries = f["windspeed_100m"][::24,42,42] # daily (every 24 hours)

Special datasets may not have three dimensions.

#retrieve the latitude and longitude of y=0, x=0.
coordinate = f["coordinates"][0,0]

#retrieve the datetime string for t=0.
datetime = f["datetime"][0]