john w zack aws truepower, llc 185 jordan rd troy, ny 12180
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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©2013 AWS Truepower, LLC
ALBANY • BARCELONA • BANGALORE
463 NEW KARNER ROAD | ALBANY, NY 12205awstruepower.com | [email protected]
A CUSTOMIZED RAPID UPDATE MULTI-MODEL FORECAST SYSTEM FOR RENEWABLE ENERGY
AND LOAD FORECASTING APPLICATIONS IN SOUTHERN CALIFORNIA
SoCal Atmospheric Modeling MeetingJune 3, 2013
Monrovia, CA
JOHN W ZACKAWS TRUEPOWER, LLC
185 JORDAN RDTROY, NY 12180
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©2013 AWS Truepower, LLC©2011 AWS Truepower, LLC
Overview
• Current Modeling System• Output Products and Applications• Near-term Plans for Modeling System Upgrades
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The Modeling System
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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
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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
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SoCal Modeling SystemJune 2013
MASS6-72
WRF6-72
MASS6-72
MASS12-72
WRF6-72
ARPS2-12
MASS2-12
NAM GFSGEM RR
PIM-C0.25-3
EnKF
GEOSP0.25-3
MOS MOS MOS MOS MOS MOS MOS
Optimized Ensemble Algorithm
Composite Forecast Products
MOS
HRRR
MOS MOS MOS MOS
MOS
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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
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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)
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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
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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
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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
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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 2 3
45 6
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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
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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):
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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.
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1330HST
1400HST
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.
Pyramidal Image Matcher MethodFirst Phase: Estimate Motion Vectors of Clouds
Step 1: Compute 8-km averaged images.
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Pyramidal Image Matcher MethodSecond Phase: Estimate Future Locations of Clouds
• 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.
Observed 60 minute forecast
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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
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Geospatial Statistical Model Example:Time-lagged Spatial Correlations
Clear Sky Factor Estimated from Satellite Brightness Data
Forecast Site Forecast Site
60-Minute Time-lagged Correlation 150-Minute Time-lagged Correlation
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Output Products and Applications
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Products to Support Load Forecasting
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Individual Model Output: Key Application Variables Hourly Regional 2-m Temperature
ImagesAnd
Animations:0-72 hrs
MASS-NAM
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Ensemble Composites: Key Application Variables Hourly Regional 2-m Temperature
Images and Animations:0-72 hrs
Ensemble Mean Ensemble Standard Deviation
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Ensemble Member Point Data:Day-ahead 2-m Temperature
Ensemble member temperature forecasts for CQT for today(from yesterday afternoon’s runs)
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Individual Model Output: Support Variables Boundary Layer Height
WRF-NAM WRF-GFS
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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
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Individual Model Output: Support Variables Marine Layer – Marine RH in the PBL
WRF-NAM WRF-GFS
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Individual Model Output: Support Variables Cloud Variables – Global Solar Irradiance
WRF-NAM WRF-GFS
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MOS-derived Support Variables: LA Basin MSLP Table
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MOS-derived Support Variables: LA Basin MSLP Difference Table
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Products to Support Renewable Energy Production Forecasting
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Individual Models: 50-m WindsZoomed Images and Animations of the Passes
WRF-NAM WRF-NAM
Tehachapi Pass San Gorgonio Pass
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Wind Power Forecasts: TabularAggregated wind power
forecasts (kW)@ SCE substations
Ensemble Composite Wind Forecasts @ SCE met tower sites
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Near-term Plans for Upgrades:
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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
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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
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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
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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)