9.8    Joint UW-3TIER Project on Data Assimilation for Renewable Energy Forecasting

Grimit, Eric P., 3TIER, Inc., Philip Regulski, Clifford F. Mass, and Gregory J. Hakim, University of Washington

In 2011, 3TIER and the University of Washington entered an arrangement for the joint evaluation an operational version of its 64-member ensemble Kalman filter (EnKF) data assimilation system, which uses DART software in combination with version 3.0.1 of the WRF model.  The initial aim of the project was to evaluate the impact that this could have on reducing wind power forecast errors compared to existing operational NWP models generated by public sources and at 3TIER.  A synoptically active period over the Pacific Northwest (April 10-30, 2011), with generally moderate to strong westerly and northwesterly flow, was chosen for a retrospective forecast experiment due to the presence of significant timing and intensity errors in the existing operational NWP models.  The 21-day period was re-simulated using 3-hourly data assimilation cycles with UWÕs regional observation data sets.  3TIER then executed its wind power forecast system based on the ensemble mean forecast at three representative wind facilities in the region.

Timing and intensity errors associated with fronts and other major weather features that are present in control WRF runs with no data assimilation (cold-start) are improved more often than not.  Overall, a reduction of about 0.5 m/s in mean absolute wind speed forecast errors is achieved at two sites.  A noticeable improvement in wind power ramp event detection, as defined by 3x3 contingency table scores, is observed at all three facilities.  Using the RUC model as a benchmark forecast that employs data assimilation, the verification scores achieved by the UW-3TIER system are far superior.