The National Oceanic and Atmospheric Administration’s (NOAA), National Weather Service (NWS), and others have analyzed the real and potential impacts of wind farms on weather radars. The analysis has revealed that the chief impact of wind turbines comes from the rotating blades, which produce a radar detectable signal very similar to that produced by weather. Currently, the NWS’ radar-algorithm for removing clutter (e.g. buildings, towers) is dependent upon an object having zero, or near zero, velocity and thus cannot remove an operating turbine’s signal. There is no known signal-processing algorithm that can remove wind turbine clutter while completely preserving the weather signal. This wind turbine-induced clutter contaminates the radar’s base data (reflectivity, velocity, spectrum width) and internal algorithms, which in-turn can impact alerts and derived products (e.g. estimated precipitation). As a result, critical radar data over wind farm areas could be lost and distract forecasters as they conduct severe weather warning operations. NWS-funded studies to develop an algorithm that can automatically identify wind turbine corrupted signal data is in its initial stages and does not currently provide a mitigation option. Until a radar-based solution is developed, one available mitigation option is limited operational curtailment of turbines during severe weather events (e.g., tornadoes, severe thunderstorms).
The purpose of curtailing turbine operations is to allow weather forecasters to view radar data uncontaminated by wind turbine clutter. During operational curtailment, the wind farm operator would feather the turbine blades bringing them to a stop, or near stop, for short time periods (on the order of 15 to 60 minutes). This allows the radar to filter out any signals returned from the wind farm. Rotation in a feathered condition can occur, but is very slow (up to 4 rpm) compared to operational speeds. Turbine clutter will be significantly reduced or completely eliminated in most circumstances, hence data contamination and algorithm errors are greatly reduced. Example scenarios for the implementation of operation curtailment are presented at Appendix A.