Vaisala created a global validation of the REST2 clear sky model used for determining solar irradiance for use in solar PV development. This presentation details the process used and the results.

1. Global Validation of REST2
Incorporated into an Operational
DNI and GHI Irradiance Model
Authored by: William T. Gustafson, Dr. Louise V. Leahy, and Gwendalyn
Bender
Presented by: Gwendalyn Bender
9/28/161

2. Page © Vaisala
Vaisala is Your Weather Expert!
§ We have been helping industries
manage the impact of weather for
nearly 80 years
§ Our weather analysis and
consulting services are based on
proven science
§ We help you understand the true
impact of weather on your
business, allowing you to improve
efficiency and profitability
§ Acquired 3TIER Inc in 2013
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3. Page © Vaisala
Solar Experience
§ 3TIER Services developed the world’s first
high resolution global solar irradiance
dataset and advanced techniques for bias
correcting solar models to ground stations
starting in 2009
§ Delivered over 500 resource assessment
projects on all 6 continents
§ Produced financial grade energy
assessments for more than 50
photovoltaic projects in countries including
the United States, Brazil, India and others
§ Supported over $5.5 billion dollars worth
of project financing for some of the largest
PV and CSP plants in the world
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4. Page © Vaisala
Observations
Equipment Design
Uncertainty
Environment
Derates and Losses
Net Energy
Solar Energy Assessment Process
Satellite Data

5. Page © Vaisala
Agenda
§ Introduction
§ Investigating new clear sky algorithms and turbidity datasets in the
search for more accurate irradiance estimates
§ Methodology
§ Clear sky algorithms tested, Turbidity data inputs, Cloud Index
modulation, Ground stations used
§ Results
§ Comparison of two models at 158+ stations globally
§ Conclusions
§ No one model wins 100% of the time but improvements were made
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6. Page © Vaisala
Introduction
§ Why investigate new clear sky models?
§ Satellite based irradiance estimates are widely accepted as the
most accurate at long-term predictions of resource data for solar
project development.
§ As with all models these estimates carry a bias compared to local
high quality ground observations.
§ Decreasing the model’s uncertainty has a direct effect on
improved project financing and better long-term outcomes
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7. Page © Vaisala
Methodology - Components
IMS Daily
Northern
Hemisphere
Snow and Ice
Analysis
Shuttle Radar
Topography
Mission
(SRTM)
Perez SUNY 2002
or REST2 v9.0
MODIS or MERRA2
or ECMWF MACC
Proprietary
3TIER Services
cloud algorithm
5 Geostationary
Satellites

8. Page © Vaisala
Methodology – Clear Sky Index
Perez SUNY
§ We are following the basic methodology laid
out by Dr Perez in the 2002 paper modified
with certain proprietary algorithms and
various publicly available source data.
§ Clear Sky Irradiance is calculated from Linke
values using Perez’s methodology. The Linke
values are calculated using methodology from
Ineichen's 2002 paper with data MODIS daily
Aerosol Optical Depth (AOD) and water vapor
datasets.
§ 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.
REST2
§ The REST2 model is a parameterized version
of Dr. Gueymard's SMARTS radiative transfer
model. We are using a version of the code
which uses the inputs for AOD, perceptible
water, etc from MERRA2.
§ Defaults are currently used for ozone, albedo,
single scattering albedo and asymmetry
parameter. Testing was done to set the
defaults.
§ Cloud indexes calculated from raw weather
satellite data and snow cover are used to
modulate Clear Sky GHI to calculate GHI
values. In the REST2 model a second function
is used to also calculate DNI from the cloud
index and the clear sky DNI value. Diffuse is
then calculated from the GHI, DNI values and
solar zenith angle.
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9. Page © Vaisala
Methodology - Turbidity
Perez SUNY
QuantitiesSourceNotes
AOD at 550 nmMODISSpatial Res: 1.0
degree
Temporal Res: daily
Precipitable
Water (cm)
MODISSpatial Res: 1.0
degree
Temporal Res:
monthly
REST2
QuantitiesSourceNotes
Alpha (Angstrom
Exponent),
MERRA2Spatial Res: 0.5-0.625 degree
Temporal Res: 1 hours
AOD at 550 nmMERRA2Spatial Res: 0.5-0.625 degree
Temporal Res: 1 hours
Precipitable
Water (cm)
MERRA2Spatial Res: 0.5-0.625 degree
Temporal Res: 1 hours
Surface
Pressure (pa)
MERRA2Spatial Res: 0.5-0.625 degree
Temporal Res: 1 hours
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10. Page © Vaisala
Methodology – Cloud Index
§ Cloud indexes (CI) are calculated using a Vaisala
proprietary algorithm.
§ Irradiance is calculated by modulating the CI values
with the clear sky values to calculate irradiance.
§ In the Perez model DNI is calculated from GHI using
Dr Perez's DIRINT methodology. In the REST2 model
a second modulation function is used to calculate DNI
from the CI and the clear sky DNI value.
§ These modulation functions vary regionally and
temporally as the CI values have a dependence on the
satellites being used to calculate them
§ The fit is based on ground-observed GHI and
calculated GHC, with kt = GHI(obs) / GHC(calc).
These kt values are then related to the satellite-based
CI values. Once this relationship is established ( kt =
f(CI) ), it is used to calculate GHI from satellite-based
CI and calculated GHC. In REST2 same is done for
DNI.
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11. Page © Vaisala
Methodology – Ground Stations
§ For validation purposes we used publicly available ground stations and those
from clients who authorized the release of their data for use in this validation.
§ Ground stations networks included World Climate Research Program, Baseline Surface
Radiation Network, National programs from the Indian Metrological Department and
NIWE, the Australian Bureau of Meteorology, the Japanese Meteorological Agency, the
National Solar Radiation Database, and several others.
§ Ground station data was lightly quality controlled on a site by site basis. All available
stations were used, there were no removals based on results.
§ We gathered GHI data from 186 public and 59 private sites, covering1689
station-years of observations.
§ For DNI we have 158 public, and 2 private sites, covering 1165 station-years
of observations.
§ The stations are independent of one another, and independent of the modeled
output. Beyond the handful of stations used to create the modulation functions
Vaisala does not allow local observations to affect our model, so that
comparisons can be made site to site on an hourly basis.
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12. Page © Vaisala
Results – By The Numbers
GHI DNI
9/28/1612
GHI Mean Values (N=252)
ParameterREST2Perez
MBE5.977.45
MBE Pct3.21%3.42%
RMS78.7079.44
RMS Pct38.14%38.43%
MAE38.0839.09
MAE Pct18.46%18.87%
DNI Mean Values (N=156)
ParameterREST2Perez
MBE2.645.86
MBE Pct11.11%12.20%
RMS188.56196.54
RMS Pct86.56%88.72%
MAE79.4987.05
MAE Pct38.06%41.10%

13. Page © Vaisala
Results – By The Numbers
GHI DNI
9/28/1613
GHI Wins At... (N=252)
ParameterREST2Tie (1%)Perez
MBE1441692
MBE Pct1442583
RMS1474857
RMS Pct1496142
MAE1624050
MAE Pct1616031
DNI Wins At.... (N=156)
ParameterREST2Tie (1%)Perez
MBE96753
MBE Pct931746
RMS1151031
RMS Pct932934
MAE135615
MAE Pct130179

14. Page © Vaisala
Results – GHI Perez SUNY
9/28/1614

15. Page © Vaisala
Results – GHI REST2
9/28/1615

16. Page © Vaisala
Results – DNI Perez SUNY
9/28/1616

17. Page © Vaisala
Results – DNI REST2
9/28/1617

18. Page © Vaisala
Conclusions
§ Tested 2 clear sky models with different turbidity inputs
against 158+ ground stations
§ Neither method tested of calculating irradiance is the most
accurate in all locations globally
§ Overall results are in favor of REST2 clear sky algorithm with
MERRA2 inputs
§ Vaisala intends to use multiple versions simultaneously and make
local decisions on which version is most accurate for conditions
§ More work could be done to see if further improvements are
possibly by using MERRA2 with the Perez SUNY algorithm,
regionally calibrating MERRA2 to aeronet stations, etc.
9/28/1618

19. Questions?
gwendalyn.bender@vaisala.com