1. Impact Of Surface State Analysis On Estimates Of Long Term Variability Of A Wind Resource Dr. Jim McCaa jmccaa@3tiergroup.com

2. 3TIER Group Established 1999 Offices in North and Latin America Focused on the weather driven renewable energy sector (wind-hydro-solar) Forecasting for over: –2,000 MW wind energy (18 projects) –2,000 MW hydro (6 projects)

3. Month Ahead Forecasts & Resource Assessment Requires a full understanding of project output and climate variability

4. Long term forecasting issues Does wind have dependable capacity on seasonal/monthly time scales? What is the probability of several above/below average years in a row? Is production linked to predictable climate indices? Can probabilistic forecasts contribute to dependable capacity?

5. Global Weather Archive 1948-present High Resolution Terrain, Soil and Vegetation Data Hourly spatial meteorological data Hourly 3-dimensional meteorological data Multi year hourly time series Numerical Weather Simulation Model On-Site Observations Dynamics Statistics INPUTS METHOD PRODUCTS Spatial Maps of Wind Resource Accurate Variability Estimate & Month Ahead Forecast Capability Accurate Dependable Capacity Estimates Resource Related Risk Analysis: R-cubed Time-evolving Moisture Availability

6. Strengths and Weaknesses of Modeled Record Extension Demonstrated skill at downscaling large scale flows and generating internal thermally-driven circulations Models generally underpredict natural variability Strong dependence on lower and lateral boundary conditions

7. Limitations of reanalysis dataset Useful for capturing large-scale flow in the upper atmosphere Not suitable for use at a single point Can not represent small-scale/thermally driven flow Too coarse for proper surface initialization

8. Case study: Northern California Long-term met tower near Altamont Flow dominated by thermal circulation driven by heating in the San Joaquin Valley Pathological case for reanalysis forcing

9. Reanalysis points in model domain

10. Altamont, California Verification

11. Interannual Variability by Season Summer (JJA) Mean Winds Winter (DJF) Mean Winds

12. Introduction of better surface initialization Re-initialize surface moisture every 3 days from an 1/8 degree hydrology model Hydrology simulation provided by Ed Maurer of the University of Washington Hydrology model was driven by surface observations from 1950 to 2000 Only addresses one part of the surface initialization problem

13. VIC hydrology model The Variability Infiltration Capacity (VIC) model is a macroscale hydrologic model that solves full water and energy balances, originally developed by Xu Liang at the University of Washington.

14. Old and new moisture availability (red is 0.1, blue is 0.5) Reanalysis 6/1/1993 VIC 6/1/1993

15. Improved Altamont Verification

16. Improved Interannual Variability Summer (JJA) Mean Winds Winter (DJF) Mean Winds

17. 10 Years of above average wind 30 Years of below average or average wind Large variability in capacity factor from month to month 75% 17%

18. PDO Negative PDO Positive

19. El Nino Conditions: Annual Capacity Factor decreases from 43% to 39% Winter Wind Every month forecast below average Summer Wind Near Average

20. Summary NCEP/NCAR reanalysis can be used (when appropriately downscaled!) to reconstruct synoptically-driven flow Mesoscale model representation of thermally- driven circulations is reasonable, but may show insufficient interannual variability Improvements to mesoscale model surface initialization translate to better reconstructed winds

21. 3TIER Environmental Forecast Group www.3tiergroup.com info@3tiergroup.com (206) 325-1573

22. Seven Mile Hill Verification A similar story…