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Wind

Wind parameters available through the POWER API and DAV include wind speeds and direction at various heights above the surface. The user community choice determines the exact height availability.

Mean Wind Speed and Wind Direction Parameters

The wind speed and wind direction parameters are to be used to determine the mean wind flow of a region rather than local area due to effects of buildings and local topography.

Corrected Wind Speed

In addition to the wind speed parameters available at set heights above surface, the POWER API allows the user to request a wind speed at a user defined height (between 10 and 300 m) for a particular surface type.

Equation: Corrected Wind Speed (Adjusted For Elevation)

\begin{align}\ WSC_{hgt} = WS_{50m} \times \left(\frac{hgt}{50. }\right) ^{\alpha} \end{align}
\begin{align} Where&: \\ &WSC_{hgt} : \text{ Corrected wind speed at user supplied height and surface type} \\ &hgt : \text{ User supplied height above surface in meters} \\ &WS_{50m} : \text{ Wind speed at 50 meters } \\ &\alpha: \text{ Surface roughness for user supplied surface type } \\ \end{align}
surface Definition surface Definition
vegtype_1 35-m broadleaf-evergreen trees (70% coverage) vegtype_7 0.6-m perennial ground cover (100%)
vegtype_2 20-m broadleaf-deciduous trees (75% coverage) vegtype_8 0.5-m broadleaf shrubs (variable %) & ground cover
vegtype_3 20-m broadleaf and needleleaf trees (75% coverage) vegtype_9 0.5-m broadleaf shrubs (10%) with bare soil
vegtype_4 17-m needleleaf-evergreen trees (75% coverage) vegtype_10 Tundra: 0.6-m trees/shrubs (variable %) & ground cover
vegtype_5 14-m needleleaf-deciduous trees (50% coverage) vegtype_11 Rough bare soil
vegtype_6 Savanna:18-m broadleaf trees (30%) & ground cover vegtype_12 Crop: 20-m broadleaf-deciduous trees (10%) & wheat
vegtype_20 Rough glacial snow/ice sea ice Smooth sea ice
openwater Open water airport ice Airport: flat ice/snow
airportgrass Airport: flat rough grass

Wind Speed Validation

In this section, MERRA-2 10-meter wind speed is compared to observations reported to the National Center for Environmental Information (NCEI – formerly National Climatic Data Center). Selected surface sites from the NCEI Integrated Surface Database (ISD) files are used for the hourly MERRA-2 comparisons. Global "Summary of the Day" (GSOD) files are used for the comparison to the daily mean MERRA-2 temperature.

Hourly

Hourly Wind Speed

Density plot of the 2-D histograms comparing MERRA-2 hourly 10-meter wind speed with station observations from the selected NCEI ISD files for the years 2001 - 2019. Darker reds indicate a higher number of matched pairs within the 2-D histogram.

Hourly Temperature

Daily Average

Daily Average 10-meter Wind Speed

Density plot of the 2-D histograms comparing MERRA-2 daily wind speed with station observations from the NCEI GSOD files for 1981 – 2020. Darker reds indicate a higher number of matched pairs within the 2-D histogram.

Wind Speed

Wind Direction

Wind Direction at a given height in degrees as the meteorological convention. The meteorological convention gives the direction from which the wind originates and wind direction is measured in degrees clockwise from due north.

The computation of wind direction from MERRA-2 u and v components is based upon the information from NCAR/UCAR/EOL's quick reference on wind direction. Verification of the equations is accomplished with the usage of MERRA-2 10 meter winds from July 11, 2015; shown in this table:

Latitude Longitude u (ms-1) v (ms-1) direction
38.0°N 80.0°W 1.14718 0.00922 270.46°
37.0°N 79.0°W 1.53487 -0.89496 300.25°
37.0°N 76.5°W -1.53666 0.21723 81.95°
35.0°N 75.5°W 1.85421 2.19976 220.13°
39.0°N 77.5°W -0.09501 1.50678 3.61°

These locations are shown as the red circles on the streamline map here.

Image of MERRA-2 streamline winds

Hourly Wind Direction Validation

Hourly 10-meter Wind Direction Contingency Chart

Diagram of the hourly wind direction contingency comparison (shown as percentage) between MERRA-2 10-meter wind direction and the selected NCEI ISD files for the years 2001 - 2019. Hourly wind directions for both are categorized into octads, then 2-D histograms relate the directions to each other. Wind Direction

Click to Expand - Python Code 
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def WD(data, Level):
    '''
    Wind Direction

    - data: Xarray Dataset with the variables units defined (required)
    - Level: the elevation to compute the wind direction (required)
    '''

    U_Values = Convert(data["U{}M".format(Level)]).To("m/s")
    V_Values = Convert(data["V{}M".format(Level)]).To("m/s")

    WD_Values = np.arctan2(-U_Values, -V_Values) * (180 / np.pi)
    WD_Values = np.where(WD_Values >= 0, WD_Values, WD_Values + 360)

    WD_Values = xr.DataArray(WD_Values, dims=('time', 'latitude', 'longitude',))

    WD_Values.name = "WD{}M".format(Level)
    WD_Values.attrs['units'] = "Degrees"

    return WD_Values

Reference

Chandler, William S., C.H. Whitlock, P.W. Stackhouse, Jr., 2005: Determining Wind Resources as a Function of Surface Roughness and Height from NASA Global Assimilation Analysis. Proceedings of the International Solar Energy Society 2005 Solar World Congress, August 6-12, Orlando, Florida