Global Validation of the REST2 Irradiance Model
Presented by William Gustafson at ICEM 2015 Boulder CO
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Introduction
Vaisala, through the acquisition of 3TIER, has been using a SUNY Perez based clear
sky model for 5 years. We are constantly looking for ways to improve our dataset, by
including other data sources, or incorporating new algorithms.
Our experiences providing global resource assessments and comparing to over a
hundred independent ground stations has led us to decide that our best path for
improvement is to update our clear sky model. An extensive literature review, and
previous collaboration with Dr. Gueymard, led us to identify the REST2 algorithm to
be the most promising. In fact NREL has identified REST2 as the best model.
Regional validations of the algorithm have been positive but no global validations
have previously been performed. Since we care about global results we decided to
make it.
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Vaisala's implementation of the Perez Satellite based Model
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We are following the basic methodology laid out by Richard Perez in his paper
“A new operational model for satellite-derived irradiances: description and validation”
modified with certain proprietary algorithms and various publicly available source data.
We use a 2 arc-minute base resolution, processing various broad-band visible data
from geosychronous weather satellites (currently GOES-13, GOES-15, Meteosat 7,
Meteosat 10, and AMTSAT02, with historical data for these regions going back to
1997-1999) to create cloud cover estimates (cloud indexes). Snow cover data
derived from National Ice Center dataset “IMS Daily Northern Hemisphere snow
and ice analysis at 4 km and 24 km resolution.” is also used in the cloud index
calculation.
These cloud indexes are calculated using a Vaisala proprietary algorithm.
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Vaisala's implementation of the Perez-Ineichen Clear Sky Model
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Clear Sky Irradiance is calculated from Linke values using Perez methodology,
using Ineichen's paper on “Conversion function between the Linke turbidity and
the atmospheric water vapor and aerosol content.” to calculate Linke values from
MODIS daily AOD and water vapor datasets.
Quantity Source Notes
Elevation
(m)
“Hole-filled seamless Shuttle
Radar Topography Mission
(SRTM) data V4”
Aggregated to 2 arc minute
AOD at 550
nm
level-3 MODIS Atmosphere
Daily Global, Terra and Aqua
satellites.
Spatial resolution: 1 degree
Temporal resolution: daily
Precipitable
Water (cm)
level-3 MODIS Atmosphere
Daily Global, Terra and Aqua
satellites.
Spatial resolution: 1 degree
Temporal resolution: daily
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Current Processing Methodology
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MODIS AOD and precipitable water data
and elevation are used to calculate Clear
Sky Irradiance.
Cloud indexes calculated from raw weather
satellite data and snow cover are used to
modulate Clear Sky GHI to calculate GHI
values.
DNI values are calculated from GHI using
Perez's modified DIRINT method. Diffuse is
calculated from GHI and DNI and the solar
zenith angle.
MODIS
Perez SUNY Method Proprietary Method
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REST2 model description
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Dr. Gueymard's REST2 is a parameterized version of the SMARTS
radiative transfer model. We use defaults for ozone, albedo, single scattering albedo, and asymmetry.
Quantity Source Notes
Alpha
(Angstrom’s
Exponent)
ECMWF-MACC
(Monitoring Atmospheric
Composition and
Climate)
Spatial resolution: ~0.7 degree
Temporal resolution: 3 hours
Derived from linear fit of 12
AOD values from 380-1020 nm
AOD at 550 ECMWF-MACC Spatial resolution: ~0.7 degree
Temporal resolution: 3 hours
Precipitable
Water (cm)
ECMWF-MACC Spatial resolution: ~0.7 degree
Temporal resolution: 3 hours
Surface
Pressure (Pa)
ECMWF-MACC Spatial resolution: ~.7 degree
Temporal resolution: 3 hours
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Modifications to Current Processing Stream Using REST2
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ECMWF-MACC inputs replace
MODIS inputs, and directly feed REST2.
Linke turbidity is not calculated.
REST2 calculation replaces Perez-
Ineichen Clear sky calculation
DNI and GHI are calculated
Independently, rather than DNI
using the DIRINT methodology
ECMWF MACC
REST2 Method Proprietary Method
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Sources of Observations Used in the Validation
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In the comparison 124 publicly available ground stations from:
Baseline Surface Radiance Network (BSRN)
National Solar Radiation Database (NSRDB)
World Radiation Data Center (WRDC)
Surface Radiation Network (SURFRAD)
Australian Bureau of Meteorology (BOM)
New Zealand National Institute of Water and Atmospheric Research (NIWA)
India Meteorological Department (IMD)
Chilean Energy Ministry
Basic Quality Control was applied to observations to remove measurements
that were obviously in error (ex: minimum and maximum value checks, etc).
Stations are independent of one another and independent of the modeled datasets.
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Observations vs Models Comparison
Both models were used to calculate hourly mean GHI values, and then filtered
for hours where our Cloud Index was less than 0.1. This is our proxy for clear
sky values.
Overall Mean Bias Error (MBE), Root Mean Square Error (RMSE) and Mean
Absolute Error (MAE) both absolute and as a percentage of Observed Mean
were calculated.
Our clients are typically most interested in low MBE to ensure that our resource
estimates will be accurate, RMSE tests that residuals aren’t too large and
looking at MAE ensures that we don’t have bias errors that are cancelling.
Period was limited to 2003-2012 as that is when ECMWF-MACC dataset we
used was valid.
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Absolute improvement
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Metric Rest2 better Same (0.1
tolerance)
Perez better
MBE 83 0 41
MBE Pct 80 4 40
RMS 106 0 18
RMS Pct 102 4 18
MAE 95 1 28
MAE Pct 90 9 25
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Perez Model Clear Sky GHI MBE
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REST2 Model Clear Sky GHI MBE
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Bias Comparison for North America
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Side-by-side comparison Current model on the left, REST2 on the right.
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Bias Comparison for Europe
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Side-by-side comparison Current model on the left, REST2 on the right.
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Perez Model Clear Sky GHI RMS pct
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REST2 Model Clear Sky GHI RMS pct
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Perez Model Clear Sky GHI MAE pct
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REST2 Model Clear Sky GHI MAE pct
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Aggregate Statistics (124 samples)
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Perez
Metric 25% Mean Median 75%
MBE (W/m2): -4.04 13.3 11.12 27.91
MBE Pct: -0.96 3.14 2.54 6.21
RMS (W/m2): 48.45 77.9 63.7 88.59
RMS Pct: 10.66 17.02 13.67 20.04
MAE (W/m2): 33.29 53.49 44.32 59.77
MAE Pct: 7.2 11.74 8.98 13.22
REST2
Metric 25% Mean Median 75%
MBE (W/m2): -9.36 4.34 0.93 14.21
MBE Pct: -1.96 1.22 0.23 3.28
RMS (W/m2): 47.1 73.05 57.68 82.05
RMS Pct: 9.98 16.1 12.48 18.29
MAE (W/m2): 30.51 49.95 39.53 54.21
MAE Pct: 6.62 11.03 7.79 12.62
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Next Steps We are proceeding carefully to decide how to use REST2 in our global irradiance
dataset.
Clear sky GHI validation work (presented) shows promise.
DNI and GHI regional modulation functions to be determined and applied.
Need to decide approach when ECMWF-MACC is not available.
Should test using ECMWF-MACC with existing processing stream.
Detailed validation will be conducted with additional public stations.
Ability to do bias corrections with onsite data needs to be tested
Expected to be used in global production capacity Fall 2015 for creating resource
assessment and energy analysis.
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Questions?