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Conference Paper

NREL/CP-6A20-74386

July 2020

A Physical Downscaling Algorithm for 

the Generation of High-Resolution 

Spatiotemporal Solar Irradiance Data

Preprint

Grant Buster, Michael Rossol, Galen Maclaurin, 

and Manajit Sengupta

Presented at the 2019 International Solar Energy Society (ISES) Solar World 

Congress

Santiago, Chile

November 4–7, 2019
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Conference Paper

NREL/CP-6A20-74386

July 2020

A Physical Downscaling Algorithm for 

the Generation of High-Resolution 

Spatiotemporal Solar Irradiance Data

Preprint

Grant Buster, Michael Rossol, Galen Maclaurin, 

and Manajit Sengupta

Suggested Citation

Buster, Grant, Michael Rossol, Galen Maclaurin, and Manajit Sengupta. 2020. A Physical 

Downscaling Algorithm for the Generation of High-Resolution Spatiotemporal Solar 

Irradiance Data: Preprint. Golden, CO: National Renewable Energy Laboratory. 

NREL/CP-6A20-74386. https://www.nrel.gov/docs/fy20osti/74386.pdf.
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A Physical Downscaling Algorithm for the Generation of HighResolution Spatiotemporal Solar Irradiance Data

Grant Buster, Michael Rossol, Galen Maclaurin, and Manajit Sengupta

The National Renewable Energy Lab (NREL), Golden, Colorado (United States)

Abstract

High-resolution solar resource data are required for solar generation models such as the System Advisor Model

(SAM), power system models such as the Integrated Grid Modeling System (IGMS), and renewable integration 

studies. These models and tools require information at a temporal resolution of minutes and spatial resolution of 

1–2 km. The National Solar Radiation Database (NSRDB) provides 20 years of solar resource information 

covering North and South America at a 30-minute, 4-km by 4-km resolution. This paper summarizes the 

development of a physics-based methodology to downscale the NSRDB to higher spatiotemporal resolution for 

use in various downstream models. The method uses the NSRDB radiative transfer models and is sufficiently 

generic to downscale any solar resource data from the NSRDB without any site-specific tuning required. 

Keywords: solar resource data, spatiotemporal data, downscaling methodology

1. Introduction

High-resolution solar resource information is used in solar generation models such as the System Advisor Model 

(SAM) (Blair et al., 2017). The solar generation data are then used in production cost models for renewable 

integration studies and in capacity expansion models. Further, new electric power system modeling platforms,

such as the Integrated Grid Modeling System (IGMS) (Palmintier et al., 2017), require high-resolution solar 

generation as input. Long-term high-resolution solar resource information can be reliably derived using satellitebased models such as the National Renewable Energy Laboratory’s Physical Solar Model (PSM), which was used 

to develop the National Solar Radiation Database (NSRDB) (Sengupta et al., 2018). Although data from the 

NSRDB are available every 30 minutes at a 4-km by 4-km resolution, downstream models generally require 

significantly higher temporal and spatial resolution information. In this study, we develop a physics-based 

approach to generate 5-minute, 2-km data from the NSRDB. This methodology uses atmospheric input data 

including clouds and aerosols to generate the high-resolution solar resource data sets. The results of this approach 

are shown to be accurate when compared to high-temporal-resolution ground measurement data.

2. Methodology

2.1 Downscaling Methods

As illustrated in Fig. 1, the methodology presented here performs spatial and temporal interpolation on the PSM 

input variables, then leverages the PSM to calculate irradiance at the desired resolution.

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Fig. 1: Physical downscaling methodology block diagram

Spatial interpolation is performed first. This method uses a spatial downscaling method similar to that used in the 

NSRDB, as described by Sengupta et al. (2018), which downscales ancillary data from the Modern-Era 

Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), to the NSRDB grid. PSM input 

variables are calculated at a high-resolution grid cell by either taking the value of the nearest-neighbor native grid 

cell or performing inverse distance weighted (IDW) interpolation between several nearest-neighbor native grid 

cells. Variables with established elevation scale heights (temperature, pressure, aerosol optical depth, and total 

precipitable water) are corrected for changes in elevation that occur within native grid cells. Details on the spatial 

interpolation method of each PSM input variable are outlined in Tab. 1.

Temporal interpolation is performed on the high-spatial-resolution data. Variables such as the solar zenith angle 

can be readily calculated at higher spatiotemporal resolution and therefore add physical fidelity to the results. 

Continuous variables, such as aerosol optical depth and surface pressure, do not exhibit step changes throughout 

time, and a linear transition between time steps is assumed. 

Cloud properties are discrete variables that exhibit step changes throughout time and space and are the most 

difficult to interpolate. Without additional cloud data in the high-resolution data set, several assumptions must be 

made. When moving from a coarse spatial grid to a finer spatial grid, the cloud data are copied to the fineresolution nearest-neighbor grid cells. When performing the temporal interpolation, it is assumed that cloud type 

transitions are stepwise at the median time step between native time steps. Cloud properties (optical depth and 

effective particle radius) are linearly interpolated between their nominal values at native-resolution cloudy time

steps and zero at native-resolution clear time steps. Cloud properties are only used in the PSM during cloudy time

steps.

After the PSM input variables are downscaled to the desired spatiotemporal resolution using the methods 

summarized in Tab. 1, the PSM is executed to generate high-resolution all-sky irradiance data.

Tab. 1: Summary of PSM input variables and their interpolation methods

PSM Input Variable Name

Spatial 

Interpolation 

Method

Elevation 

Correction

Temporal 

Interpolation 

Method

Aerosol Optical Depth (AOD) IDW Yes Linear

Aerosol Single-Scatter Albedo (SSA) IDW No Linear

Angstrom Wavelength Exponent (Alpha) IDW No Linear

Angstrom Turbidity Coefficient (Beta) Calculated from AOD and Alpha

Aerosol Asymmetry Parameter IDW No Linear

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PSM Input Variable Name

Spatial 

Interpolation 

Method

Elevation 

Correction

Temporal 

Interpolation 

Method

Cloud Optical Depth NN No Linear

Cloud Particle Effective Radius NN No Linear

Cloud Type NN No Stepwise

Reduced Ozone Vertical Pathlength IDW No Linear

Solar Zenith Angle Calculated from the time index and spatial coordinate

Sun-Earth Radius Calculated from the time index

Surface Albedo IDW No Linear

Surface Pressure IDW Yes Linear

Total Precipitable Water IDW Yes Linear

2.2 High-Temporal-Resolution Synthetic Variability

A method for adding synthetic variability to cloudy irradiance at high temporal resolution is added to the PSM.

The synthetic variability is intended to represent some of the variability in optical transmittance that would be 

observed in thin clouds during sub-30-minute time steps. This feature is desired to mimic variable photovoltaic 

(PV) generation for high-resolution temporal modeling of renewable energy grid systems. The maximum

variability at a given high-resolution time step is calculated as a triangular distribution based on the instantaneous 

clear-sky ratio (the all-sky irradiance divided by the clear-sky irradiance). A maximum variability of 5 percent is 

added to the global horizontal irradiance (GHI) when the clear-sky ratio is 0.9 (thin clouds). The maximum 

variability decreases to zero when the clear-sky ratio is zero or one (no synthetic variability is added to extremely 

thick clouds or clear-sky conditions). The variability added to the GHI is then propagated to the direct normal 

irradiance (DNI) via the DISC model. This method conserves cumulative solar energy and adds a simple 

representation of high-temporal-resolution cloud variability. 

3. Validation

Validation is performed against high-temporal-resolution ground measurement data. The physical downscaling 

method is used to downscale 20 years of 30-minute native NSRDB data to 5-minute data for seven sites in the 

National Oceanic and Atmospheric Administration’s Surface Radiation Budget Network (SURFRAD). The 

downscaled 5-minute data are benchmarked against available 5-minute ground measurement data. Similar to the 

NSRDB validation (Habte et al., 2018), the 5-minute ground measurement data are calculated based on a 1-hour 

moving average of the measurement data to account for the comparison of point ground measurements against the 

large spatial average represented by the physical downscaling method. Similar to previous NSRDB validation 

methods, the irradiance data are validated only for solar zenith angles less than 80 degrees. This comparison is 

intended to support the use of the physical downscaling algorithm as representative of realistic irradiance for a 

wide range of geographies and climates.

4. Results

4.1 Irradiance Validation Results

Fig. 2 shows the validation results when benchmarking the 5-minute downscaled data from the NSRDB against 

the 1-minute ground measurement SURFRAD data. Comparing Fig. 2 to the validation statistics from the NSRDB 

assessment (Habte et al., 2018) shows that there is a minor loss in accuracy in the downscaled 5-minute data. This 

minor loss in accuracy can be attributed to the synthetically created 5-minute data. Without the addition of accurate 

high-resolution input data (especially cloud data), the validation statistics are not expected to improve the NSRDB 

baseline validation (Habte et al., 2018); however, the limited difference from the baseline validation supports that 

the physical downscaling algorithm performs as expected.

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DNI GHI

MBE (%) RMSE (%)

Fig. 2: Validation statistics including mean bias error (MBE) and root mean square error (RMSE) of 5-minute DNI and GHI data 

generated from the physical downscaling algorithm. Downscaled data are validated against 20 years of ground measurements from 

seven SURFRAD sites.

A time-series comparison of the downscaled data to the SURFRAD ground measurements in Fig. 3 shows some 

of the benefits and limitations of the downscaling methodology. 

One useful feature of the downscaling methodology is an improved gap-filling methodology. Because the native 

NSRDB is a 30-minute time series, dramatic clear-to-cloudy or cloudy-to-clear transitions in 30-minute intervals 

can cause invalid irradiance results that get gap-filled from valid data from the nearest time steps. The gradual 

transitions in the 5-minute downscaled data makes these transitions less dramatic and can result in better irradiance 

calculations and less gap-filling. Additionally, the procedure for filling missing cloud property data is improved 

such that missing cloud properties are filled based on neighboring data before the irradiance is calculated. The 

improved procedure captures the cloudy time steps around hour 21:00 in Fig. 3. 

The effect of the synthetic variability in the downscaling methodology is also shown in Fig. 3 around hour 21:00. 

Because the cloud is relatively thin (cloudy irradiance is approximately 90 percent of clear-sky irradiance), there 

is significant estimated variability in the GHI. The variability in the ground-measured irradiance is shown to be 

significant in the right of Fig. 3 (instantaneous 1-minute ground measurement data). The downscaled data are not 

intended to represent the variability at a point measurement and are successful in representing the variability 

somewhere between the averaged measurement and the instantaneous measurement.

The obvious limitation of the downscaling methodology is that it cannot capture the sub-30-minute cloud 

transitions that are apparent in the instantaneous point-measurement data. Without accurate high-temporalresolution cloud input data, these high-frequency cloud transitions cannot be captured. Note, however, that the 

high-frequency 1-minute cloud transitions measured at a point source are not representative of the cloud transitions 

in the large grid cells that the NSRDB represents. The spatial disparity is a persistent issue when comparing 

downscaled NSRDB data to small solar power plants, as will be discussed in Section 4.3.

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Fig. 3: Comparison of GHI profiles for the 5-minute downscaled data, the NSRDB native 30-minute data, and SURFRAD ground 

measurement data. The left plot shows the ground measurement data as a 61-minute moving window average of 1-minute data, and 

the right plot shows the ground measurement data as instantaneous 1-minute data. 

4.2 High-Resolution Irradiance Data for Puerto Rico and the U.S. Virgin Islands

As part of a project funded by the U.S. Department of Energy, 20 years of 5-minute, 2-km downscaled NSRDB 

solar resource data was produced to assist in characterizing and evaluating available solar resource for Puerto Rico 

and the U.S. Virgin Islands, and to conduct a supply curve analysis for capacity expansion and production cost 

modeling. The figures below only focus on the main island of Puerto Rico, though data and analysis results were 

produced for the minor islands of Puerto Rico and the U.S. Virgin Islands. Fig. 4 and Fig. 5 show the downscaled 

irradiance data over the island. Fig. 4 shows how the algorithm downscales the native 4-km grid to a 2-km grid. 

The clear-sky irradiance in Fig. 4 shows how the spatial interpolation with elevation correction increases the 

resolution over the central mountain range. A time series of 5-minute downscaled solar resource data can be 

visualized for the whole island in Fig. 5. The transport of clouds in the downscaled data can be visualized as the 

cloudy regions move southwest throughout the image set.

Fig. 4: Instantaneous clear-sky GHI resulting from the spatial downscaling of 4-km data (left) to 2-km data (right) with elevation 

correction

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Fig. 5: DNI at 5-minute intervals for Puerto Rico during 1 hour on January 1, 2017

4.3 Comparison to Real PV Plant Generation

Capacity factor profiles based on the 5-minute downscaled resource data are compared to 5-minute generation 

data from four utility-scale PV plants in Puerto Rico. The downscaled Puerto Rico resource data are run through 

the SAM PV performance model (Blair et al., 2017) to calculate 5-minute system capacity factor profiles. Fig. 6

shows examples of both good and bad comparisons for the generation data. 

Qualitatively, the 5-minute calculated performance data benchmarks very well against the plant performance data, 

as shown in the left of Fig. 6. Nearly all the significant ramping events and even some momentary periods of clear 

or cloudy conditions are well captured. The actual generation data presented in Fig. 6 are understandably noisier 

than the calculated performance data because the actual generation data are from a solar plant approximately 0.4

km2in area (based on Google Maps satellite imagery), whereas the NSRDB cloud data are sourced from a 16 km2 

grid cell. Therefore, the actual generation represents a relatively small discrete location while the calculated 

performance represents a larger regional average.

As shown in the right of Fig. 6, however, some periods of generation show very poor comparisons. The plant data 

are shown to ramp dramatically between a seemingly unrealistically low generation value to the nominal 

generation during a period of approximately 20 minutes. The plant data frequently exhibit significant deviations 

from expected generation, resulting in a poor comparison to the calculated generation profiles. Data from all four 

plants contain numerous days that appear to be the result of curtailment, faulty measurement equipment, or other 

systemic errors. As a result, a long-term quantitative comparison of the calculated performance data to the plant 

measurement data was determined to not be meaningful and is not presented here. 

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Fig. 6: Comparison of calculated PV generation data from the 5-minute downscaled NSRDB resource data to 5-minute solar power 

plant data in Puerto Rico. Capacity factor profiles are normalized on a 7-day window.

5. Conclusion

High-resolution solar resource information is a critical need for power generation, integration studies, and power 

system studies and can be provided by downscaling the data available from the NSRDB. This study summarizes 

the development of a physics-based downscaling methodology and presents the results of downscaled resource 

data. A validation study based on 20 years of ground measurement data is presented, supporting that the results 

from the downscaling method are sufficiently accurate. An example application of the downscaling methodology 

to provide high-resolution solar resource data to plan a renewable energy grid for Puerto Rico and the U.S. Virgin 

Islands is also presented. Finally, a comparison of a system performance analysis to actual PV plant data are 

presented, showing good agreement when measurement data.

Although the methodology presented in this study is useful given the relatively coarse NSRDB, it also provides a 

framework for developing more advanced methods to downscale PSM input data. Future work will focus on 

developing methods that can better predict downscaled data inputs to the PSM, thereby increasing the accuracy 

of the synthetically downscaled irradiance data.

6. Acknowledgments

The authors acknowledge Nick Grue, Aron Habte, Andrew Kumler, Billy Roberts, and Yu Xie for their support 

of the Puerto Rico project. 

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy 

Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the 

U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, 

acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish 

or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

A portion of this research was performed using computational resources sponsored by the Department of Energy's 

Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory.

7. References 

Blair, N., DiOrio, N., Freeman, J., Gilman, P., Janzou, S., Neises, T., Wagner, M., 2017. System Advisor Model 

(SAM) General Description (Version 2017.9.5) (NREL/TP-6A20-70414). National Renewable Energy 

Laboratory, Golden, CO. https://www.nrel.gov/docs/fy18osti/70414.pdf.

Habte, A., Sengupta, M., Lopez, A., Xie, Y., Maclaurin, G., 2018. Assessment of the National Solar Radiation 

Database (NSRDB 1998–2016). World Conference on Photovoltaic Energy Conversion 2018 (WCPEC-7), 

NREL/CP-5D00-71607.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications 7
Palmintier, B., Hale, E., Hansen, T.M., Jones, W., Biagioni, D., Sorensen, H., Wu, H., Hodge, B.M., 2017. 

IGMS: An integrated ISO-to-appliance scale grid modeling system. IEEE Trans. Smart Grid 8, 3, 1525-1534, 

doi: 10.1109/TSG.2016.2604239.

Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., Shelby, J., 2018. The National Solar Radiation Data 

Base (NSRDB). Renewable and Sustainable Energy Reviews, 89, 51-60, ISSN 1364-0321.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications 8