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Next-Generation Satellite Modeling for the National Solar Radiation Database (NSRDB)

Dr. Manajit Sengupta Aron Habte , Anthony Lopez, and Andrew Weekley, NREL Christine Molling CIMMS, University of Wisconsin Andrew Heidinger, NOAA PV Modeling Workshop, Cologne, Germany October 22-23, 2015

Work is funded by the US Department of Energy

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Evolution of Solar Resource Data 1952-1975 SOLMET1 [ERDA, NOAA, 1979]

1961-1990 NSRDB2 [DOE, NOAA, 1994]

1991-2005 NSRDB-II3 [DOE, NOAA, 2007]

1998-2014 NSRDB [DOE, NOAA, UW 2015]

National Solar Radiation Data Base

(1)

248 stations with

26 Measurement

Stations

1977-80

(2)

239

Modeled

Stations with

56 partial

measureme

nt stations

1990

(3)

1,454 Modeled

Locations

1991-2005

http://nsrdb.nrel.gov

Satellite-based, gridded

4 km x 4 km

Half-hourly

1998-2014

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• Empirical Approach (Industry standard

traditional approach):

– Build model relating satellite measurements and ground

observations.(cloud index and clearness index)

– Use those models to obtain solar radiation at the surface

from satellite measurements.

• Physical Approach: (the new approach)

– Retrieve cloud and aerosol information from satellites

– Use the information in a radiative transfer model

How do satellites model surface radiation?

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F+G = a – b F-

TOA F+

TOA F+TOA

Basic principle

Richard Perez, et al.

Clearness Index Satellite Reflectance

(cloud Index)

Empirical Approach to Satellite Modeling

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Satellite image Cloud Properties

Solar Radiation

Satellite based Cloud Retrieval Model

Radiative Transfer Models

Physical Approach to Satellite Modeling

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Physical Approach to Satellite Modeling

Satellite Retrieval: Inputs: Satellite radiance from 5 channels of GOES Outputs: cloud mask Cloud properties including cloud type and cloud optical thickness

Ancillary data: Aerosols from MODIS and MISR satellites Ozone TOMS/OMI satellites Water Vapor from NWP model (GFS) Snow from NSIDC Atmospheric Profiles Temperature, Pressure from (GFS)

Radiative Transfer Model: Clear Sky: REST2 All Sky: FARMS Inputs: Aerosol, Water Vapor, Ozone, Elevation, cloud mask, cloud properties

Output: GHI and DNI under all sky conditions

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GIS based data access with web-service for multi-pixel download Includes ancillary meteorological data for PV/CST modeling using SAM

Accessing the NSRDB Data

http://nsrdb.nrel.gov

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Product Timeline

• Beta product (2005-2012) currently online (V. 1)

• V2 Product (1998-2014) available by October 2015

• Typical Meteorological Year (TMY) product by October 2015

• Quarterly monthly update available from 2016

• From 2016 annual datasets will be available by following March

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Validation of Satellite product (V1.0.1) using Ground Data

http://www.esrl.noaa.gov/gmd/grad/surfrad/

Code Name Latitude Longitude Elevation Time Zone Installed

BND Bondville, Illinois 40.05° N 88.37° W 230 m 6 hours from UTC Apr-94

TBL Table Mountain, Boulder, Colorado 40.13° N 105.24° W 1689 m 7 hours from UTC Jul-95

DRA Desert Rock, Nevada 36.63° N 116.02° W 1007 m 8 hours from UTC Mar-98

FPK Fort Peck, Montana 48.31° N 105.10° W 634 m 7 hours from UTC Nov-94

GCM Goodwin Creek, Mississippi 34.25° N 89.87° W 98 m 6 hours from UTC Dec-94

PSU Penn. State Univ., Pennsylvania 40.72° N 77.93° W 376 m 5 hours from UTC Jun-98

SXF Sioux Falls, South Dakota 43.73° N 96.62° W 473 m 6 hours from UTC Jun-03

NOAA SURFRAD DATASTREAMS: GHI, DNI and Diffuse Comparison with 2005-2012 satellite product

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Validation with Surface Measurements

GHI: Mean Bias Error (MBE)

±5%

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Validation with Surface Measurements

GHI: Root Mean Square Deviation (RMSD)

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Validation with Surface Measurements

DNI: Mean Bias Error (MBE)

±5%

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Validation with Surface Measurements

DNI: Root Mean Square Deviation (RMSD)

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Validation with Surface Measurements

Bondville, IL: GHI for all cases

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Bondville, IL: GHI Clear Sky Cases

Cloudy Sky Cases

Validation with Surface Measurements

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Conclusions

• New gridded satellite product available publicly from NREL (http://nsrdb.nrel.gov )

• Datasets is 4 km, 30 minute resolution with meteorological variable from NASA MERRA.

• 2005-2012 currently available with 1998-2014 online by the end of October.

• Accurate aerosol and water vapor information is critical in properly modeling clear sky GHI and DNI.

• Improved All Sky model FARMS for cloudy sky.

• Significant uncertainty in cloudy cases.

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Future Work

• Inclusion of daily variability of aerosols from MACC/aerosols.

• Improved surface albedo time series to reflect land use changes.

• Improved identification of high albedo surfaces (sand and snow).

• 5 minute data from GOES-R.

• Spectral long-term datasets in Plane of Array.

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Thank You! Contact: manajit@nrel.gov

http://www.nrel.gov/solar_radiation

http://www.nrel.gov/docs/fy15osti/63112.pdf

http://nsrdb.nrel.gov