1. ©2013 AWS Truepower, LLC ALBANY BARCELONA BANGALORE 463 NEW KARNER ROAD | ALBANY, NY 12205 awstruepower.com | info@awstruepower.com A CUSTOMIZED RAPID UPDATE MULTI-MODEL FORECAST SYSTEM FOR RENEWABLE ENERGY AND LOAD FORECASTING APPLICATIONS IN SOUTHERN CALIFORNIA SoCal Atmospheric Modeling Meeting June 3, 2013 Monrovia, CA JOHN W ZACK AWS TRUEPOWER, LLC 185 JORDAN RD TROY, NY 12180

2. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Overview Current Modeling System Output Products and Applications Near-term Plans for Modeling System Upgrades

3. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC The Modeling System

4. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Prediction Tools at AWST Numerical Weather Prediction (NWP) -Weather Research and Forecasting (WRF) Model -Advanced Regional Prediction System (ARPS) -Mesoscale Atmospheric Simulation System (MASS) -WRF- Data Assimilation Research Testbed (WRF-DART) Non-NWP -Wide range of statistical tools applied to: Model Output Statistics (MOS) Geospatial statistical models Weather-dependent application models Example: Wind power plant output model -Feature detection and tracking

5. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Current SoCal Modeling System Overview Numerical Weather Prediction (NWP) -Continental-scale EnKF medium res ensemble -SoCal downscaled NCEP/EC models -SoCal rapid update models Advanced Model Output Statistics (MOS) -Dynamic screening multiple linear regression -Other methods in development and testing Non-NWP models -Cloud vector model based on satellite images being implemented for solar forecasting -Geospatial statistical models being implemented

6. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC SoCal Modeling System June 2013 MASS 6-72 MASS 6-72 WRF 6-72 WRF 6-72 MASS 6-72 MASS 6-72 MASS 12-72 MASS 12-72 WRF 6-72 WRF 6-72 ARPS 2-12 ARPS 2-12 MASS 2-12 MASS 2-12 NAM GFS GEM RR PIM-C 0.25-3 PIM-C 0.25-3 EnK F GEOSP 0.25-3 GEOSP 0.25-3 MOS Optimized Ensemble Algorithm Composite Forecast Products MOS HRRR MOS

7. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Ensemble Kalman Filter (EnKF) Ensemble Objectives -Provide potential alternative to NCEP/EC larger scale models for higher-res model initial and boundary conditions -Provide flow-dependent spatial error covariances for high-res data assimilation -Provide indication of forecast sensitivity patterns Current Use Experimental configuration – not used in forecast production

8. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC EnKF Configuration 24 WRF members -51 km outer grid -17 km inner grid 84-hr forecast every 12 hours 12-hour data assimilation cycles via DART Limited data assimilation on inner grid at present -Radiosonde -Satellite-derived winds -ASOS -Mesonet & buoy GFS outer grid BCs (perturbed)

9. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Statistical NWP Forecast Sensitivity Based on a statistical analysis of an ensemble of “perturbed” NWP forecasts Needs an ensemble of statistically significant size Maps the relationship of a change in the forecast at the target site to changes in initial condition variables at the the time of forecast initialization Case-specific Forecast Site

10. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Downscaled NWP Objective: -Add higher frequency features to larger scale NCEP/EC forecasts due to surface properties and non- linear interaction of atmospheric features Approach -Nested grid with 5 km inner resolution -72 hour forecast -6 hour update for NCEP models -12-hour update for EC GEM model -3 MASS model runs -2 WRF model runs

11. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Rapid Update NWP Objective: -Improve 0-12 hour forecasts by frequently assimilating local and regional data with high resolution NWP model in rapid update mode Approach: -2-hour update cycle -5-km resolution -MASS with 4-hr pre-forecast observation nudging cycle (4DDA) -ARPS with 3DVAR data assimilation

12. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Rapid Update Local Data Assimilation Inferred moisture from satellite visible and infrared imagery Winds and temperature from SCE met sensor network in the Passes ASOS, Mesonet & buoy Winds and temperature from AQMD profilers/RASS network Temperature, water vapor and cloud water from SCE radiometer @ LAX VAD winds and reflectivity from NWS 88D radars 1 1 2 2 3 3 4 4 5 5 6 6

13. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Model Output Statistics (MOS) Objective -Reduce the magnitude of systematic errors in NWP forecasts for specific variables of interest Approach -Screening multiple linear regression -Dynamic 30-day rolling training sample -Advanced statistical approaches under development

14. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Non-NWP Models: Atmospheric Feature Vector Model Objective: -Short-term (0-3 hrs) forecasts of weather features on time scales for which it is difficult to obtain value from the NWP approach Approach -Pyramidal Image Matcher (PIM) Possible applications -Cloud vector (Currently operating for Hawaii and being implemented for SoCal) -Radar reflectivity vector (Under development) -Other feature vectors (Under development):

15. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Pyramidal Image Matcher Attributes Development history -Originally developed for stereographic video processing. -Adapted by Zinner et. al. (2008) for satellite image processing. Multi-scale approach enables the PIM to capture the motion and development/dissipation of clouds at a wide range of scales of motion. Estimates coarse cloud motion vector field a larger scales using visible satellite images averaged to coarse resolution. Refines cloud motion vector field at successively finer scales until the full resolution image is reached. Estimates future images by propagating current image forward in time using the motion vector field.

16. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC 1330 HST 1400 HST Full 1 km Resolution Image 8 km Averaged Image Pyramidal Image Matcher Method

17. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Pyramidal Image Matcher Method Use motion vectors computed in first phase to estimate future cloud locations Wind forecasting: Use feature identification techniques to identify potential ramp-causing cloud features (such as outflow from rain showers) and predict their arrival at wind farms. Solar Forecasting: Apply PIM to solar irradiance derived from visible satellite images to predict future solar irradiance. Observed60 minute forecast

18. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Non-NWP Models: Geospatial Statistical Models Objective: -Very short-term (0-2 hrs) forecasts of weather variables of interest on time scales for which it is difficult to obtain value from the NWP approach Approach -Identify and use time-lagged statistical relationships -May have simple linear components and complex non-linear components

19. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Geospatial Statistical Model Example: Time-lagged Spatial Correlations Clear Sky Factor Estimated from Satellite Brightness Data Forecast Site 60-Minute Time-lagged Correlation150-Minute Time-lagged Correlation

20. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Output Products and Applications

21. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Products to Support Load Forecasting

22. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Individual Model Output: Key Application Variables Hourly Regional 2-m Temperature Images And Animations: 0-72 hrs MASS-NAM

23. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Ensemble Composites: Key Application Variables Hourly Regional 2-m Temperature Images and Animations:0-72 hrs Ensemble Mean Ensemble Standard Deviation

24. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Ensemble Member Point Data: Day-ahead 2-m Temperature Ensemble member temperature forecasts for CQT for today (from yesterday afternoon’s runs)

25. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Individual Model Output: Support Variables Boundary Layer Height WRF-NAMWRF-GFS

26. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Individual Model Output: Support Variables Marine Layer Height WRF-NAMWRF-GFS Definition of marine layer based on max RH in PBL and vertical RH gradient

27. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Individual Model Output: Support Variables Marine Layer – Marine RH in the PBL WRF-NAM WRF-GFS

28. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Individual Model Output: Support Variables Cloud Variables – Global Solar Irradiance WRF-NAM WRF-GFS

29. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC MOS-derived Support Variables: LA Basin MSLP Table

30. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC MOS-derived Support Variables: LA Basin MSLP Difference Table

31. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Products to Support Renewable Energy Production Forecasting

32. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Individual Models: 50-m Winds Zoomed Images and Animations of the Passes WRF-NAM Tehachapi PassSan Gorgonio Pass

33. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Wind Power Forecasts: Tabular Aggregated wind power forecasts (kW) @ SCE substations Ensemble Composite Wind Forecasts @ SCE met tower sites

34. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Near-term Plans for Upgrades:

35. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Background Upgrades Model updates as they become available Data assimilation system upgrades as they become available Assimilation of additional data as it becomes available in real-time

36. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Advanced Rapid Update Data Assimilation Objective: Improve impact of assimilated data on forecast performance Approach Implement GSI with 2-hr update WRF run Use flow-dependent spatial model error covariance estimates -From EnKF run? NCEP ensemble? Time-lagged AWST forecast ensemble? -Use to derive flow-dependent nudging coefficients (i.e. weights for nudging terms) -Hybrid 3DVAR

37. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Advanced MOS: Decision Tree Regression Objective: More effectively reduce the magnitude of systematic model errors in NWP forecasts of specific variables of interest especially for infrequent of extreme events Approach -Employ decision tree methods in place of screening multiple linear regression More potential to identify and correct non-linear error patterns Demonstrated to be among the best statistical prediction techniques for a variety of applications -Use larger training samples where possible Advanced non-linear approaches tend to exploit larger samples more effectively

38. ©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC Advanced MOS: Analog Ensemble Objective: More effectively reduce the magnitude of systematic model errors in NWP forecasts of specific variables of interest especially for infrequent of extreme events Approach -Employ analog ensemble concept Compare current NWP forecast to all NWP forecasts in an historical archive with respect to a set of “matching parameters” Identify the the N closest forecasts matches Compile an N-member ensemble of the observed outcomes for the N best matches Use the outcomes to generate a deterministic (e.g. ensemble mean) or probabilistic (e.g. ensemble distribution) forecast -Effectively customizes to MOS to each forecast scenario -Preliminary result suggest it may perform much better than typical MOS approaches for infrequent or extreme events (with an appropriate sample)