Comparing near surface measurements of wind speed and direction over the Indian Ocean from Lidar and Scatterometer, and results from predictive study using the wind shear power law and the surface roughness log law to model upper level winds from near surface measurements
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Comparing near surface measurements of wind speed and direction over the Indian Ocean from Lidar and Scatterometer, and results from predictive study using the wind shear power law and the surface roughness log law to model upper level winds from near surface measurements

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  • Description:
    This study compares the equivalent neutral wind estimates of two space based active microwave sensors called scatterometers, the ASCAT (Advanced SCATterometer) and OSCAT (Oceansat-2 scatterometer), to the average wind profiles as estimated from continuous conical scans of a Doppler lidar deployed on a research vessel for a 3 month period in the Indian Ocean. Statistical analysis of matched pairs between the OSCAT derived wind speeds and the lidar measured wind speeds show the OSCAT is positively biased by 0.5 m s⁻¹. While the comparison between the pairs of lidar and ASCAT winds show no bias. The effect of atmospheric stability in estimating winds from surface roughness as compared to the Doppler wind measurements was investigated using a calculated stability indicator, Richardson number. Using the OSCAT and lidar matched pairs, analyses shows a statistical significant positive scatterometer bias (p-value < 0.01) of 0.83 m s⁻¹ in wind speeds associated with unstable atmospheric conditions, or those with Richardson numbers greater than -0.4. The vertical profiles of wind speed from the lidar not only capture winds near the surface, but also at heights up to 2 km. Data at between 50 m and 200 m is increasingly relevant as wind turbines for energy generation climb to these heights to harness more constant non-turbulent wind flow. Methods to model or extrapolate surface wind to upper levels exist. A wind shear power law or a surface roughness log law is commonly used by the wind energy industry. Using knowledge of atmospheric and surface conditions to constrain the shear exponent of the power law and surface roughness length of the log law, near surface wind speed estimates were extrapolated using these two models and compared to the lidar measured estimates at wind turbine heights. Biases between the modeled and lidar estimated winds found at rotor plane heights are presented. Using the log law and a surface roughness parameter of 0.01 to predict wind speeds at various heights and in 3 atmospheric stability scenarios, biases between the model and measurements range from 2.26 - 0.16 m s⁻¹. [doi:10.7289/V5X63JZV (http://dx.doi.org/10.7289/V5X63JZV)]
  • Content Notes:
    Brandi J. McCarty, James H. Churnside "April 2016." "Funding for this research has come from NASA (NNH13ZDA001N-WEATHER)"--Page 2. Includes bibliographical references (pages 25-26).
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