A rapid-updated wind analysis system based on mesoscale model, radar, and surface data for ramp-event wind energy forecasting

Predicting sudden and significant changes in wind power generation is a challenging problem. Such changes can be forced by large-scale weather features as well as local-scale weather events. The large-scale weather systems can be predicted by numerical weather prediction (NWP) models with reasonable accuracy. However, the current NWP models have limited ability in forecasting ramp-events caused by the sudden and significant wind changes corresponding to mesoscale features, such as passages of cold front or thunderstorm outflow.

Given the rapid evolving and small-scale nature of many ramp events, an accurate and rapid-updated analysis that combines high-resolution data can provide a good alternative to NWP forecasts for use in ramp-event wind energy prediction. Over several decades, NCAR has developed data assimilation, nowcasting, and forecasting systems targeted at the improvement of very short-term forecasts of high-impact weather events. In addition to WRF-based data assimilation systems, NCAR developed the unique high-resolution data assimilation system VDRAS for convective-scale nowcasting applications. VDRAS is designed for the assimilation of high-resolution observations from radar and surface networks using a four-dimensional variational (4D-Var) scheme of a cloud-resolving model. One unique feature of VDRAS is its rapid update cycle, typically 6-15 minutes. VDRAS has been implemented in real-time operational environments since 1998. The frequently updated analysis from VDRAS through the assimilation of high-density observations provides the current atmospheric state, which is crucial for nowcasting wind and initializing high-resolution numerical models. VDRAS is being demonstrated in wind farms in Colorado and New Mexico to assess its value and capability in the application of ramp-event wind energy prediction. In this presentation, we will describe and discuss preliminary results from the field demonstration and post-field analysis.