Impact Of Surface State Analysis On Estimates Of Long Term Variability Of A Wind Resource

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

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 Winter (DJF) Mean Winds Summer (JJA) 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 Reanalysis 6/1/1993 VIC 6/1/1993 (red is 0.1, blue is 0.5)

15. Improved Altamont Verification

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

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

18. PDO Positive PDO Negative

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

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 VerificationA similar story…