The document summarizes the development of satellite modeling for the National Solar Radiation Database (NSRDB) to provide accurate surface solar radiation data. It describes the evolution from empirical to physical models using satellite measurements and ancillary data as inputs to radiative transfer models. Validation shows the new 2005-2012 dataset has a mean bias error of less than 5% for GHI and DNI compared to surface measurements, though uncertainty remains for cloudy cases. Future work aims to improve the model with higher resolution data and better representation of aerosols and surfaces.

1. NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
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

2. 2
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

3. 3
• 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?

4. 4
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

5. 5
Satellite image Cloud Properties
Solar Radiation
Satellite based
Cloud Retrieval
Model
Radiative
Transfer Models
Physical Approach to Satellite Modeling

6. 6
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

7. 7
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

8. 8
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

9. 9
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

10. 10
Validation with Surface Measurements
GHI: Mean Bias Error (MBE)
±5%

11. 11
Validation with Surface Measurements
GHI: Root Mean Square Deviation
(RMSD)

12. 12
Validation with Surface Measurements
DNI: Mean Bias Error (MBE)
±5%

13. 13
Validation with Surface Measurements
DNI: Root Mean Square Deviation
(RMSD)

14. 14
Validation with Surface Measurements
Bondville, IL: GHI for all cases

15. 15
Bondville, IL: GHIClear Sky Cases
Cloudy Sky Cases
Validation with Surface Measurements

16. 16
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.

17. 17
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.

18. 18
Thank You! Contact: manajit@nrel.gov
http://www.nrel.gov/solar_radiation
http://www.nrel.gov/docs/fy15osti/63112.pdf
http://nsrdb.nrel.gov