A versatile WRF and MM5-based weather analysis and forecasting system for supporting wind energy prediction

In collaboration with the Army Test and Evaluation Command (ATEC), NCAR has developed a Real-Time Four Dimensional Data Assimilation (RTFDDA) and forecasting system. The RTFDDA system is built around the Penn State/NCAR Mesoscale Model version 5 (MM5) and the Weather Research and Forecasting (WRF) model. RTFDDA is capable of continuously collecting and ingesting diverse synoptic and asynoptic weather observations from conventional and unconventional platforms, and provides continuous 4-D weather analyses for accurate nowcasts and short-term forecasts for mesoscale regions. Operational RTFDDA systems have been implemented at seven ATEC test ranges and tens of other DoD, public and private applications in the last seven years, providing rapidly updated, multi-scale weather analyses and forecasts with the fine-mesh domain having 0.5 - 3 km grid increments. The observational data ingested by the system includes WMO standard upper-air and surface reports, wind profilers, satellite cloud-drift winds, commercial aircraft reports, all available mesonet data, radar observations, and any special instruments that report temperature, winds and moistures. Furthermore, the system has been expanded to include the following new modeling and data assimilation capabilities that are highly valuable for wind energy applications: a) Ensemble RTFDDA, which is a multi-model, mesoscale data analysis and forecasting system that samples uncertainties in the major components of RTFDDA and predicts the uncertainties in the weather forecasts by performing an ensemble of RTFDDA analyses and forecasts; b) LES (Large Eddy Simulation) modeling, which is nested down from the RTFDDA mesoscale data assimilation and forecasts to LES models with grid sizes of ~100 m for wind farm regions using GIS 30-m resolution terrain; and c) HRLDAS (High-Resolution Land-Surface Data Assimilation System), which assimilates high-resolution satellite vegetation and soil data to generate high-resolution, accurate state of soil moisture and temperature. This modeling system has been applied for supporting wind energy prediction at wind farms operated by Xcel Energy. The modeling capabilities will be described and demonstrated through case studies at selected wind farms.