Validating precision estimates in horizontal wind measurements from a Doppler lidar
- 1Pacific Northwest National Laboratory, Richland, WA 99352, USA
- 2National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Boulder, CO 80305, USA
- 3Cooperative Institute for Research in Environmental Sciences, Boulder, CO 80305, USA
- 4National Center for Atmospheric Research, Boulder, CO 80307, USA
- 5Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80309, USA
- 6National Renewable Energy Laboratory, Golden, CO 80401, USA
Abstract. Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.
All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.