A Customized Rapid Update Multi

Report
ALBANY • BARCELONA • BANGALORE
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
463 NEW KARNER ROAD | ALBANY, NY 12205
awstruepower.com | [email protected]
©2013 AWS Truepower, LLC
Overview
• Current Modeling System
• Output Products and Applications
• Near-term Plans for Modeling System Upgrades
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©2011
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
©2013 AWS Truepower, LLC
©2011
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
©2013 AWS Truepower, LLC
©2011
SoCal Modeling System
June 2013
GEM
NAM
EnK
F
GFS
RR
HRRR
MASS
12-72
MASS
6-72
WRF
6-72
WRF
6-72
MASS
6-72
ARPS
2-12
MASS
2-12
PIM-C
0.25-3
MOS
MOS
MOS
MOS
MOS
MOS
MOS
MOS
MOS
MOS
MOS
MOS
MOS
Optimized Ensemble
Algorithm
Composite Forecast Products
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©2011
GEOSP
0.25-3
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
-
Radiosonde
Satellite-derived winds
ASOS
Mesonet & buoy
• 84-hr forecast every
12 hours
• 12-hour data
assimilation cycles via
DART
• Limited data
assimilation on inner
grid at present
• 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
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©2011
Forecast
Site
Downscaled NWP
• Objective:
- Add higher frequency
features to larger scale
NCEP/EC forecasts due to
surface properties and nonlinear 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
1
2
Inferred moisture from satellite
visible and infrared imagery
3
Winds and temperature from
AQMD profilers/RASS network
VAD winds and reflectivity
from NWS 88D radars
Winds and temperature from SCE met
sensor network in the Passes
4
5
Temperature, water vapor and cloud
water from SCE radiometer @ LAX
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6
ASOS, Mesonet & buoy
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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©2011
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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Pyramidal Image Matcher Method
Full 1 km
Resolution Image
1330
HST
1400
HST
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8 km Averaged
Image
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.
Observed
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60 minute forecast
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
-
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May have simple linear components and complex non-linear
components
Geospatial Statistical Model Example:
Time-lagged Spatial Correlations
Clear Sky Factor Estimated from Satellite Brightness Data
60-Minute Time-lagged Correlation 150-Minute Time-lagged Correlation
Forecast Site
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Forecast Site
Output Products and Applications
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©2011
Products to Support
Load Forecasting
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Individual Model Output: Key Application Variables
Hourly Regional 2-m Temperature
MASS-NAM
Images
And
Animations:
0-72 hrs
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Ensemble Composites: Key Application Variables
Hourly Regional 2-m Temperature
Ensemble Mean
Ensemble Standard Deviation
Images and Animations:0-72 hrs
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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
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WRF-GFS
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
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WRF-GFS
Individual Model Output: Support Variables
Cloud Variables – Global Solar Irradiance
WRF-NAM
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WRF-GFS
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 Winds
Zoomed Images and Animations of the Passes
Tehachapi Pass
WRF-NAM
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San Gorgonio Pass
WRF-NAM
Wind Power Forecasts: Tabular
Aggregated wind power
forecasts (kW)
@ SCE substations
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Ensemble Composite Wind Forecasts @ SCE met tower sites
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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©2011
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
•
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Advanced non-linear approaches tend to exploit larger samples
more effectively
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)
©2013 AWS Truepower, LLC
©2011

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