global validation of the rest2 solar model from vaisala

Global Validation of REST2 Incorporated into an Operational DNI and GHI Irradiance Model

Authored by: William T. Gustafson, Dr. Louise V. Leahy, and GwendalynBenderPresented by: Gwendalyn Bender

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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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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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Observations

Equipment Design

Uncertainty

Environment

Derates and Losses

Net Energy

Solar Energy Assessment Process

Satellite Data

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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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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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Methodology - ComponentsIMS DailyNorthernHemisphereSnow and Ice Analysis

Shuttle Radar TopographyMission (SRTM)

Perez SUNY 2002 or REST2 v9.0

MODIS or MERRA2 or ECMWF MACC

Proprietary3TIER Services cloud algorithm

5 Geostationary Satellites

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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 Linkevalues using Perez’s methodology. The Linkevalues 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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Methodology - Turbidity

Perez SUNY

QuantitiesSourceNotes

AOD at 550 nmMODISSpatial Res: 1.0 degreeTemporal Res: daily

PrecipitableWater (cm)

MODISSpatial Res: 1.0 degreeTemporal Res: monthly

REST2

QuantitiesSourceNotesAlpha (Angstrom Exponent),

MERRA2 Spatial Res: 0.5-0.625 degreeTemporal Res: 1 hours

AOD at 550 nmMERRA2 Spatial Res: 0.5-0.625 degreeTemporal Res: 1 hours

Precipitable Water (cm)

MERRA2 Spatial Res: 0.5-0.625 degreeTemporal Res: 1 hours

Surface Pressure (pa)

MERRA2Spatial Res: 0.5-0.625 degreeTemporal Res: 1 hours

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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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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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Results – By The Numbers

GHI DNI

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GHI Mean Values (N=252)ParameterREST2Perez

MBE5.977.45MBE Pct3.21%3.42%RMS78.70 79.44RMS Pct38.14%38.43%MAE38.08 39.09MAE Pct18.46%18.87%

DNI Mean Values (N=156)ParameterREST2Perez

MBE2.645.86MBE Pct11.11%12.20%RMS188.56196.54RMS Pct86.56% 88.72%MAE79.4987.05 MAE Pct38.06% 41.10%

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Results – By The Numbers

GHI DNI

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GHI Wins At... (N=252)ParameterREST2Tie (1%)PerezMBE1441692MBE Pct1442583RMS1474857RMS Pct1496142MAE1624050MAE Pct1616031

DNI Wins At.... (N=156)ParameterREST2Tie (1%)PerezMBE96753MBE Pct931746RMS1151031RMS Pct932934MAE135615MAE Pct130179

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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.

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