Global Validation of the REST2 Irradiance Model · (Angstrom’s Exponent) ECMWF-MACC (Monitoring...

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


Precipitable

Water (cm)

ECMWF-MACC Spatial resolution: ~0.7 degree


Surface

Pressure (Pa)

ECMWF-MACC Spatial resolution: ~.7 degree


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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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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?

email: [email protected] [email protected]

Global Validation of the REST2 Irradiance Model · (Angstrom’s Exponent) ECMWF-MACC (Monitoring...

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