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Wind Resource Data: Wind Integration National Dataset (WIND) Toolkit

Model

Wind resource data for North America was produced using the Weather Research and Forecasting Model (WRF). The WRF model was initialized with the European Centre for Medium Range Weather Forecasts Interim Reanalysis (ERA-Interm) data set with an initial grid spacing of 54 km. Three internal nested domains were used to refine the spatial resolution to 18, 6, and finally 2 km. The WRF model was run for years 2007 to 2014. While outputs were extracted from WRF at 5 minute time-steps, due to storage limitations instantaneous hourly time-step are provided for all variables while full 5 min resolution data is provided for wind speed and wind direction only.

The following variables were extracted from the WRF model data:

  • Wind Speed at 10, 40, 60, 80, 100, 120, 140, 160, 200 m
  • Wind Direction at 10, 40, 60, 80, 100, 120, 140, 160, 200 m
  • Temperature at 2, 10, 40, 60, 80, 100, 120, 140, 160, 200 m
  • Pressure at 0, 100, 200 m
  • Surface Precipitation Rate
  • Surface Relative Humidity
  • Inverse Monin Obukhov Length

Countries

North America

Wind resource for North America was produce using three distinct WRF domains shown below. The CONUS domain for 2007-2013 was run by 3Tier while 2014 as well as all years of the Canada and Mexico domains were run under NARIS. The data is provided in three sets of files:

  • CONUS: Extracted exclusively from the CONUS domain
  • Canada: Combined data from the Canada and CONUS domains
  • Mexico: Combined data from the Mexico and CONUS domains

Asia

Wind resource was also produced fro the following countries and years:

  • Bangladesh: 2014-2017
  • Central Asia: 2015
  • India: 2014
  • Kazakhstan: 2015
  • Philippines: 2017
  • Vietnam: 2016-2018

Directory structure

Wind resource data is made available as a series of hourly .h5 files corresponding to each country and year. Below is an example of the directory structure for the CONUS domains:

  • s3://nrel-pds-wtk/conus -> root directory for the conus domain
    • /v1.0.0 -> version 1 of the data corresponding to years 2007-2013, run by 3Tier
      • /wtk_conus_${year}.h5 -> Hourly data for all variables for the given year
      • /${year}/wind_${hub_height}.h5 -> Five minute wind resource data for the given year and hub height
    • /v1.1.0 -> version 1.1 of the data corresponding to 2014, run under NARIS with an updated version of WRF and new Boundary Layer Physics (PBL scheme)

The WIND Toolkit data is also available via HSDS at /nrel/wtk/${country}.

For examples on setting up and using HSDS please see our examples repository

Data Format

The data is provided in high density data file (.h5) separated by year. The variables mentioned above are provided in 2 dimensional time-series arrays with dimensions (time x location). The temporal axis is defined by the time_index dataset, while the positional axis is defined by the meta dataset. For storage efficiency each variable has been scaled and stored as an integer. The scale-factor is provided in the scale-factor attribute. The units for the variable data is also provided as an attribute (units).

Python Examples

Example scripts to extract wind resource data using python are provided below:

The easiest way to access and extract data from the Resource eXtraction tool rex

from rex import WindX

wtk_file = '/nrel/wtk/conus/wtk_conus_2010.h5'
with WindX(wtk_file, hsds=True) as f:
    meta = f.meta
    time_index = f.time_index
    wspd_100m = f['windspeed_100m']

Note: WindX will automatically interpolate to the desired hub-height:

from rex import WindX

wtk_file = '/nrel/wtk/conus/wtk_conus_2010.h5'
with WindX(wtk_file, hsds=True) as f:
    print(f.datasets)  # not 90m is not a valid dataset
    wspd_90m = f['windspeed_90m']

rex also allows easy extraction of the nearest site to a desired (lat, lon) location:

from rex import WindX

wtk_file = '/nrel/wtk/conus/wtk_conus_2010.h5'
nwtc = (39.913561, -105.222422)
with WindX(wtk_file, hsds=True) as f:
    nwtc_wspd = f.get_lat_lon_df('windspeed_100m', nwtc)

or to extract all sites in a given region:

from rex import WindX

wtk_file = '/nrel/wtk/conus/wtk_conus_2010.h5'
state='Colorado'
with WindX(wtk_file, hsds=True) as f:
    co_wspd = f.get_region_df('windspeed_100m', state, region_col='state')

Lastly, rex can be used to extract all variables needed to run SAM at a given location:

from rex import WindX

wtk_file = '/nrel/wtk/conus/wtk_conus_2010.h5'
nwtc = (39.913561, -105.222422)
with WindX(wtk_file, hsds=True) as f:
    nwtc_sam_vars = f.get_SAM_df(nwtc)

If you would rather access the WIND Toolkit data directly using h5pyd:

# Extract the average 100m wind speed
import h5pyd
import pandas as pd

# Open .h5 file
with h5pyd.File('/nrel/wtk/conus/wtk_conus_2010.h5', mode='r') as f:
    # Extract meta data and convert from records array to DataFrame
    meta = pd.DataFrame(f['meta'][...])
    # 100m windspeed dataset
    wspd = f['windspeed_100m']
    # Extract scale factor
    scale_factor = wspd.attrs['scale_factor']
    # Extract, average, and unscale windspeed
    mean_wspd_100m = wspd[...].mean(axis=0) / scale_factor

# Add mean windspeed to meta data
meta['Average 100m Wind Speed'] = mean_wspd_100m
# Extract time-series data for a single site
import h5pyd
import pandas as pd

# Open .h5 file
with h5pyd.File('/nrel/wtk/conus/wtk_conus_2010.h5', mode='r') as f:
    # Extract time_index and convert to datetime
    # NOTE: time_index is saved as byte-strings and must be decoded
    time_index = pd.to_datetime(f['time_index'][...].astype(str))
    # Initialize DataFrame to store time-series data
    time_series = pd.DataFrame(index=time_index)
    # Extract 100m wind speed, wind direction, temperature, and pressure
    for var in ['windspeed_100m', 'winddirection_100m',
    			'temperature_100m', 'pressure_100m']:
    	# Get dataset
    	ds = f[var]
    	# Extract scale factor
    	scale_factor = ds.attrs['scale_factor']
    	# Extract site 100 and add to DataFrame
    	time_series[var] = ds[:, 100] / scale_factor

References

For more information about the WIND Toolkit please see the website. Users of the WIND Toolkit should use the following citations: