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John W Zack AWS Truepower, LLC 185 Jordan Rd Troy, NY 12180

SoCal Atmospheric Modeling Meeting June 3, 2013 Monrovia, CA. A Customized Rapid Update Multi-Model Forecast System for Renewable Energy and Load Forecasting Applications in Southern California . John W Zack AWS Truepower, LLC 185 Jordan Rd Troy, NY 12180. Overview.

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John W Zack AWS Truepower, LLC 185 Jordan Rd Troy, NY 12180

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  1. SoCal Atmospheric Modeling Meeting June 3, 2013 Monrovia, CA A Customized Rapid Update Multi-Model Forecast System for Renewable Energy and Load Forecasting Applications in Southern California John W Zack AWS Truepower, LLC 185 Jordan Rd Troy, NY 12180

  2. Overview • Current Modeling System • Output Products and Applications • Near-term Plans for Modeling System Upgrades

  3. The Modeling System

  4. 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. 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. SoCal Modeling SystemJune 2013 GEM NAM GFS EnKF RR HRRR MASS 6-72 MASS 12-72 WRF 6-72 MASS 6-72 WRF 6-72 ARPS 2-12 MASS 2-12 PIM-C 0.25-3 GEOSP 0.25-3 MOS MOS MOS MOS MOS MOS MOS MOS MOS MOS MOS MOS MOS Optimized Ensemble Algorithm Composite Forecast Products

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

  13. 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. 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. 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. Pyramidal Image Matcher Method FirstPhase: Estimate Motion Vectors of Clouds Full 1 km Resolution Image 8 km Averaged Image Step 2: Compute Motion Vectors at 8 km resolution. Step 3: Use motion vectors to estimate1400 HST 1 km from 1330 image. Step 4: Average estimated 1 km image to 4 km. Step 5: Estimate correction to motion vectors using 1330 HST observed 4 km and estimated 1400 HST observed 4 km images. Step 6: Repeat steps 2-4 at 2 km and 1 km scales. 1330 HST 1400 HST Step 1: Compute 8-km averaged images.

  17. Use motion vectors computed in firstphaseto 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. Pyramidal Image Matcher Method SecondPhase: Estimate Future Locations of Clouds Observed 60 minute forecast

  18. 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. Geospatial Statistical Model Example:Time-lagged Spatial CorrelationsClear Sky Factor Estimated from Satellite Brightness Data 60-Minute Time-lagged Correlation 150-Minute Time-lagged Correlation Forecast Site Forecast Site

  20. Output Products and Applications

  21. Products to Support Load Forecasting

  22. Individual Model Output: Key Application Variables Hourly Regional 2-m Temperature MASS-NAM Images And Animations: 0-72 hrs

  23. Ensemble Composites: Key Application Variables Hourly Regional 2-m Temperature Ensemble Standard Deviation Ensemble Mean Images and Animations:0-72 hrs

  24. Ensemble Member Point Data:Day-ahead 2-m Temperature Ensemble member temperature forecasts for CQT for today (from yesterday afternoon’s runs)

  25. Individual Model Output: Support Variables Boundary Layer Height WRF-NAM WRF-GFS

  26. Individual Model Output: Support Variables Marine Layer Height WRF-NAM WRF-GFS Definition of marine layer based on max RH in PBL and vertical RH gradient

  27. Individual Model Output: Support Variables Marine Layer – Marine RH in the PBL WRF-NAM WRF-GFS

  28. Individual Model Output: Support Variables Cloud Variables – Global Solar Irradiance WRF-GFS WRF-NAM

  29. MOS-derived Support Variables: LA Basin MSLP Table

  30. MOS-derived Support Variables: LA Basin MSLP Difference Table

  31. Products to Support Renewable Energy Production Forecasting

  32. Individual Models: 50-m WindsZoomed Images and Animations of the Passes Tehachapi Pass San Gorgonio Pass WRF-NAM WRF-NAM

  33. Wind Power Forecasts: Tabular Aggregated wind power forecasts (kW) @ SCE substations Ensemble Composite Wind Forecasts @ SCE met tower sites

  34. Near-term Plans for Upgrades:

  35. 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. 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. 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. 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)

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