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Contract No. DE-AC36-08GO28308

Temporal and Spatial 

Comparison of Gridded 

TMY, TDY, and TGY Data Sets

A. Habte, A. Lopez, M. Sengupta, and S. Wilcox

National Renewable Energy Laboratory

Technical Report

NREL/TP-5D00-60886

February 2014 
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Temporal and Spatial 

Comparison of Gridded TMY, 

TDY, and TGY Data Sets

A. Habte, A. Lopez, M. Sengupta, 

and S. Wilcox

National Renewable Energy Laboratory

Prepared under Task No. SS13.3521

Technical Report

NREL/TP-5D00-60886

February 2014 
NOTICE

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iii

Acknowledgments 

The continued support and indispensable advice of Daryl Myers (NREL, retired) and Thomas 

Stoffel (NREL, retired) is greatly acknowledged. We are grateful to Billy Roberts and Rebecca 

Stubbs for assisting in the production of maps and data processing. We thank Michael Dooraghi 

for his input and thorough review of the document. 

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iv

List of Acronyms 

CDF cumulative distribution frequency

COV coefficient of variation

DNI direct normal irradiance

GHI global horizontal irradiance

NSRDB National Solar Radiation Database

TDY typical direct (normal irradiance) year

TGY typical global (horizontal irradiance) year

TMY typical meteorological year

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v 

Executive Summary 

Typical Meteorological Year (TMY) data sets provide industry standard resource information for 

building designers and are commonly used by the solar industry to estimate photovoltaic and 

concentrating solar power system performance. They are the basis for system performance and 

economic models such as PVWatts,

1

 System Advisor Model (SAM),2 and Solar Prospector.3

Historically, TMY data sets were only available for certain station locations, but current TMY

data sets are available on the same grid as the National Solar Radiation Database (NSRDB) data 

and are referred to as the gridded TMY. This gridded TMY data is derived from the 10-km by 

10-km gridded NSRDB data (including the years from 1998 to 2009). A gridded TMY file is 

created by concatenating 12 typical meteorological months from statistically analyzed and 

selected individual months from the entire set of available years. The selected months (which 

may be from different years) are then joined to make one serially complete year. A typical 

meteorological month is generated through cumulative distribution frequency (CDF) analysis in 

which a typical month is selected closer to the long-term monthly average CDF (Wilcox and 

Marion 2008). For example, in this case the month of January was selected from the 12 Januarys 

during the 1998 to 2009 period. The remaining months were selected in similar fashion. Ten 

indices were used to select a TMY month. These are described in Table 1. The indices are 

essential for selecting the typical month using a weighting factor for each parameter, which 

ultimately includes considering the monthly mean and median and the persistence of weather. 

The series of steps that were carried out to acquire the typical month is described in the Users 

Manual for TMY3 Data Sets (Wilcox and Marion 2008). 

A system performance analysis for photovoltaics and concentrating solar power projects should 

generally consider high weighting factors for the solar components; as such, some of the 

meteorological parameters in Table 1 might not be relevant for these applications. For this 

reason, typical direct (normal irradiance) year (TDY) and typical global (horizontal irradiance) 

year (TGY) data sets were also produced, whereby the weighting focused on the irradiance 

components of the data rather than the meteorological data. The weighting factor for each solar 

component is described in Table 1. The process of generating TDY and TGY data sets is tailored 

to generating solar component values that are as close as possible to the long-term means for 

those parameters (as opposed to the “typical” conditions defined by the usual TMY statistical 

analysis) and are not influenced by other parameters. In this report, a comparison of TMY, TDY, 

and TGY data sets were performed to better understand the impact of ancillary weather variables

upon them. 

We analyzed the temporal and spatial variability of the typical year data sets. The spatial 

variability was calculated using the coefficient of variations of adjacent pixels on a monthly basis

for the TDY and TGY data sets and also for the TMY data set. The temporal variability was 

analyzed by calculating the standard deviation of the monthly and annual gridded data. These

analyses identified geographical areas of high and low temporal and spatial variability, thereby 

providing insight into the representativeness of a particular TMY data set for use in renewable 

energy as well as other applications. 

 

1 http://www.nrel.gov/rredc/pvwatts/ 2 https://sam.nrel.gov/ 3 http://maps.nrel.gov/prospector 

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vi

Table of Contents 

List of Figures ......................................................................................................................................... vii

List of Tables........................................................................................................................................... vii

1 Introduction......................................................................................................................................... 1

2 Objective ............................................................................................................................................. 1

3 Method................................................................................................................................................. 2

4 Results ................................................................................................................................................ 3

4.1 Comparison of TMY, TDY, and TGY Data Sets........................................................................... 3

4.2 Spatial Variability of TMY, TDY, and TGY Data Sets ................................................................. 6

4.3 Temporal Variability of the Typical Year Data Set...................................................................... 11

5 Summary ........................................................................................................................................... 13

6 References ........................................................................................................................................ 14

7 Appendix A: Monthly Spatial Variability......................................................................................... 15

7.1 Monthly DNI Spatial Differences for the 3 x 3 Grid.................................................................... 15

7.2 Monthly DNI Spatial Differences for the 5 x 5 Grid.................................................................... 16

7.3 Monthly DNI Spatial Differences for the 7 x 7 Grid.................................................................... 17

7.4 Monthly GHI Spatial Differences for the 3 x 3 Grid.................................................................... 18

7.5 Monthly GHI Spatial Differences for the 5 x 5 Grid.................................................................... 19

7.6 Monthly GHI Spatial Differences for the 7 x 7 Grid.................................................................... 20

8 Appendix B: Monthly Temporal Variability..................................................................................... 21

8.1 Monthly DNI Standard Deviation (%) ......................................................................................... 21

8.2 Monthly GHI Standard Deviation (%) ......................................................................................... 22

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vii

List of Figures 

Figure 1. The distribution of the pixels in each spatial variability analysis. The black center pixels 

were compared to each of the gray pixels. ...................................................................................... 3

Figure 2. The top two figures represent DNI data sets for TMY (DNI) and TDY developed using 

meteorological and irradiance weighting factors, respectively. The bottom two figures 

represent GHI for TMY (GHI) and TGY developed using meteorological and irradiance 

weighting factors, respectively. ........................................................................................................ 4

Figure 3. (Left) DNI percent comparison (TDY-TMY (DNI)) and (right) GHI percent comparison 

(TGY-TMY (GHI)). The histogram box represents 95% of the difference. ...................................... 5

Figure 4. Meteorological parameters used in the production of TMY data sets compared to the TDY 

and TGY meteorological parameters ................................................................................................ 6

Figure 5. Understanding spatial variability of neighboring pixels for the 3 x 3, 5 x 5, and 7 x 7 grid 

comparisons for the annual TMY, TDY, and TGY data sets (pixels within the United States 

boundary). Dots are COVs, and the red line represents the mean of the 95% confidence level, 

which is the data in the green box.................................................................................................... 9

Figure 6. Map showing spatial variability between neighboring pixels ............................................. 10

Figure 7. Differences among various grid comparisons for the month of January. The arrow 

demonstrates the increased spatial variability with increased distance between pixels. ......... 11

Figure 8. Monthly standard deviation distribution in kWh/m2/day for the NSRDB gridded DNI and 

GHI data set....................................................................................................................................... 12

Figure 9. An illustration of temporal variability for the month of May using the NSRDB data set 

(1998–2009) for both (a) DNI and (b) GHI........................................................................................ 13

List of Tables

Table 1. Weighting Parameters for the TMY, TDY, and TGY Data Sets (Modified from Wilcox and 

Marion 2008)........................................................................................................................................ 2

Table 2. Average COV for a Typical Data Set Under Different Pixel Distance Comparisons ............. 7

Table 3. Monthly COV for 3 x 3, 5 x 5, and 7 x 7 Grid Comparisons ..................................................... 8

Table 4. Average Standard Deviation (Temporal Variability) Obtained From the NSRDB 1998–2009 

Data Set............................................................................................................................................. 12

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1 

1 Introduction 

Typical meteorological year (TMY) data was first developed by Sandia National Laboratories

with the intent of using the data set for solar heating and cooling applications. The development 

of TMY data at that time produced a consensus data set to be used in computer simulations. The 

application of TMY data sets has since evolved enormously and has reached the point at which 

the data sets are more frequently used by applications unrelated to building designers. Currently, 

the most popular uses are in the solar energy industry, where renewable energy analysts use 

TMY data sets as inputs into solar energy models that perform site selection of renewable energy 

technology deployment, system design, system performance, and system operations. However, it 

is important to understand the limitations of TMY data sets when using them for certain 

applications beyond their original intent. It should be noted that TMY data is based on historical 

data and lacks current or recent real-time meteorological or solar information as well as

information about climatic variability of a particular area. The data sets do not include the 

influence of extreme events such as a volcanic eruption or climatic phenomenon that occur at 

decadal or longer timescales. Therefore, TMY data sets should not be used for analysis that 

needs weather information for a particular period and/or to perform real-time or operational 

assessments of system performance of renewable energy technologies.

In addition to the historical TMY, the National Renewable Energy Laboratory (NREL) recently 

developed gridded typical direct (normal irradiance) year (TDY) and typical global (horizontal) 

year (TGY) data sets. These two data sets were developed using generally (but not exactly) the 

same principles guiding TMY data set generation from the NSRDB gridded satellite data. These 

gridded data sets provide a 10-km by 10-km pixel for each location/grid. The data sets contain 

hourly values of solar irradiance and some meteorological elements for one year that exemplifies

a longer or multiyear period. The two data sets were designed primarily for system performance 

analysts whose main interests are in the solar components for designing a particular renewable 

energy technology or project. These data sets provide another set of inputs for specific users; 

including other parameters that are traditionally used to generate TMY data in the abovementioned analyses could decrease the effectiveness of such analysis. It should be noted that the 

only difference between the TMY and newly developed TDY and TGY data sets is the weighting 

factor used for generating the data sets. For the TDY and TGY data sets, the weighting factors 

are only the solar parameters, whereas TMY includes non-zero weighting factors for both the 

solar and meteorological components. The TDY data set is developed using 100% weighting for 

direct normal irradiance (DNI), whereas the TGY data set uses 100% weighting for global 

horizontal irradiance (GHI). This indicates that the other meteorological indices were set to zero 

in both TDY and TGY cases. Therefore, users of these data sets should clearly understand the 

differences between these two types of data sets when applying them to a specific project.

2 Objective 

Three essential assessments were conducted in this report: (1) understanding the difference 

between TMY versus TDY and TMY versus TGY; (2) assessing the spatial variability among 

neighboring pixels of the gridded TMY, TDY, and TGY data; and (3) understanding the 

temporal variability of these data sets.

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2 

3 Method 

The gridded TMY, TDY, and TGY data sets were created using the 12-year (from 1998 to 2009) 

NSRDB gridded satellite data. These gridded data sets represent data for the center within a10-

km by 10-km area for each location/grid. Each data set contains hourly average values of solar 

irradiance and some meteorological elements during a period of one year that represent a longer 

or multiyear period. As discussed in the background section, typical year data sets for the TMY,

TDY, and TGY were developed using the weighting factors listed in Table 1. The meteorological 

data sets used to create the TMY were derived from the North American Regional Reanalysis 

data sets from the National Oceanic and Atmospheric Administration (NARR; Mesinger et al. 

2006). 

A comparison of TMY (DNI) versus TDY, and TMY (GHI) versus TGY was performed using 

simple subtraction of the typical yearly average data among the data sets. The percentage 

difference was obtained by dividing the difference of DNI and GHI by the TMY (DNI) and 

TMY (GHI) for the respective periods. 

Table 1. Weighting Parameters for the TMY, TDY, and TGY Data Sets (Modified from Wilcox and 

Marion 2008)

Index TMY TDY TGY

Max Dry Bulb Temp 1/20 0 0

Min Dry Bulb Temp 1/20 0 0 

Mean Dry Bulb Temp 2/20 0 0 

Max Dew Point Temp 1/20 0 0 

Min Dew Point Temp 1/20 0 0 

Mean Dew Point Temp 2/20 0 0 

Max Wind Velocity 1/20 0 0 

Mean Wind Velocity 1/20 0 0 

GHI 5/20 0 20/20

DNI 5/20 20/20 0 

The spatial variability analysis was implemented by comparing a center pixel with neighboring 

pixels (Figure 1). A coefficient of variation (COV) value was generated for each configuration 

shown in Figure 1. 

1. 3 x 3 pixels: Each pixel represents a 10-km by 10-km area. In this comparison, the total 

area covered by the pixels was 30 km by 30 km. The center pixel was compared to the 

adjacent 8 pixels.

2. 5 x 5 pixels: The center pixel was compared to the farthest surrounding pixels in all 

directions. 

3. 7 x 7 pixels: The center pixel was compared to the 24 farthest surrounding pixels.

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3 

Figure 1. The distribution of the pixels in each spatial variability analysis. The black center pixels 

were compared to each of the gray pixels.

COV was calculated using the following equation: 

COV(TxYa, TxYb ) =

�� ∑ (TxYbi

i=n

i=1 − TxYa )2

n �

TxYa

 

Where, TxYa represent the average irradiance of the TMY, TDY, or TGY data set for a specified 

averaging time (monthly or yearly) of the center pixel, TxYb represents the average of the 

neighboring (or comparison) pixel of the TMY, TDY, or TGY irradiance data values, and n

represents the number of surrounding pixels in the comparison. 

The temporal variability was assessed using a standard deviation calculation of the monthly 

gridded NSRDB data. 

stdev = � ∑ (Xi − X n

i=1 )2

n − 1 

Where X represents the monthly mean data of the gridded NSRDB for an individual year and X

represents the average of the 12 months during the 1998 to 2009 data set, e.g., the average of 12 

Januarys during the 1998 to 2009 data set.

4 Results 

The purpose of the analysis was to determine the differences among the TMY, TDY, and TGY 

data sets and understand the temporal and spatial variability of the TMY, TDY, and TGY data

sets. 

4.1 Comparison of TMY, TDY, and TGY Data Sets 

The weights shown in Table 1 indicate that GHI and DNI had much higher weighting than the 

other atmospheric variables in the TMY calculations. 

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However, Figure 2 demonstrates that the differences among the TMY (DNI) and TDY, and TMY 

(GHI) and TGY were indeed small, and the figures appeared to be visually quite similar, even 

the contour lines where very small differences would be conspicuous. This verifies that the solar 

radiation inputs indeed dominated the other meteorological variables because of the high 

weighting assigned to those inputs. It also indicates that the 12-year period of the TDY and TGY 

data sets may be representative of the TMY data sets within the 500 W/m2

/day (0.5 kW/m2/day) 

resolution of the contour intervals shown in Figure 2. 

Figure 2. The top two figures represent DNI data sets for TMY (DNI) and TDY developed using 

meteorological and irradiance weighting factors, respectively. The bottom two figures represent 

GHI for TMY (GHI) and TGY developed using meteorological and irradiance weighting factors, 

respectively. 

Figure 3 shows the differences among the TMY and TGY or TDY in percent for a typical year. 

The histograms in the same figure show the result of the percent difference obtained using a 95 

percentile criteria (light green box). Ninety-five percent of the time, most locations demonstrated 

less than 1% difference between TMY (DNI) and TDY. The TMY (GHI) and TGY data sets 

appeared to have relatively small differences that were below 0.5% difference for nearly 95% of 

the time. Further, the speckled appearance of the difference plots (Figure 3) indicates a 

somewhat random distribution of higher magnitude differences, whereas one might expect 

smoother transitions as a function of climate or geography (and such artifacts are even visible in 

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5 

the plots as patterns in the speckles). This could possibly be attributed to the inevitable choosing 

by the algorithm of different month-years for the different data sets. While the algorithm 

attempts to force uniformity based on long term means, minor differences among years is not 

surprising. However, even the greatest differences among nearby pixels is still less than 1.5%, 

indicating acceptable uniformity for many applications. 

Figure 3. (Left) DNI percent comparison (TDY-TMY (DNI)) and (right) GHI percent comparison 

(TGY-TMY (GHI)). The histogram box represents 95% of the difference. 

Figure 4 demonstrates the spatial variability of the meteorological parameters that were used as 

weighting factors for the TMY data. The figure does not show clear separation among the TMY, 

TDY, and TGY meteorological parameters. This again affirms that the solar flux held more 

weight in generating these data sets (Sawaqed et al. 2005). 

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6 

Figure 4. Meteorological parameters used in the production of TMY data sets compared to the TDY 

and TGY meteorological parameters 

However, solar irradiance differences can be quite large when comparing monthly TMY (DNI) 

and TDY data sets, or monthly TMY (GHI) and TGY data sets, especially when a selected 

typical month for each typical year data set is obtained from a different year because of the 

differences in the weighting. These differences are presented in more detail in Section 4.2 and 

Section 4.3 on spatial and temporal variability, respectively. 

Overall, it appears that the annual differences among the data sets used to represent DNI and 

GHI during a typical year were minimal, with no discernible trend. 

4.2 Spatial Variability of TMY, TDY, and TGY Data Sets 

In this section, we tried to identify the spatial variability of the different typical year data sets. 

For those interested in the analysis of system performance, the results would assist them in better 

understanding the available resources and where to deploy specific types of energy systems. 

Further, the value of the spatial data is to understand the spatial limits of a measurement value. In 

areas with low variability, one may have confidence in the resource information over wide 

geographic area, whereas in a high variability area, more careful analysis may be required when 

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7 

considering closely spaced locations. However, information derived from the analysis using the 

TMY, TDY, or TGY data sets should be accompanied by other solar resource information, such 

as ground-measured or satellite-modeled data sets, because the TMY data lacks real-time and 

climatic information.

As presented in Figure 5, Figure 6, and Table 2, the spatial variability was analyzed using a COV

method. The results demonstrated that adjacent pixels have a lower COV, and as the distance 

increases between the center pixel (target) and the farthest pixel the difference increases. 

Table 2. Average COV for a Typical Data Set Under Different Pixel Distance Comparisons

COV

TMY (DNI) TDY TMY (GHI) TGY

3 x 3 0.01983 0.01884 0.00981 0.00917

5 x 5 0.02524 0.02416 0.01276 0.0121

7 x 7 0.02956 0.0285 0.01525 0.01461

This physically implies that the lower the variance between adjacent pixels, the higher the 

likelihood that the center pixel is similar to its neighbor pixel. As expected, the 3 x 3 grid shown 

in Figure 6 for the DNI and GHI data sets demonstrated less spatial variability. Table 3 and 

Appendix A: Monthly Spatial Variability demonstrate the monthly spatial differences for various 

grid comparisons. The winter months had higher spatial differences than the summer months. 

This was true for all grid comparisons. 

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8 

Table 3. Monthly COV for 3 x 3, 5 x 5, and 7 x 7 Grid Comparisons

COV

Month TMY (DNI) TDY TMY (GHI) TGY

3 x 3 

January 0.078 0.070 0.039 0.036

February 0.073 0.068 0.037 0.035

March 0.056 0.051 0.029 0.027

April 0.042 0.038 0.023 0.020

May 0.041 0.037 0.021 0.019

June 0.037 0.034 0.019 0.017

July 0.033 0.031 0.017 0.016

August 0.036 0.033 0.019 0.017

September 0.034 0.032 0.019 0.017

October 0.039 0.038 0.022 0.020

November 0.052 0.049 0.027 0.025

December 0.072 0.066 0.036 0.033

5 x 5 

January 0.092 0.083 0.046 0.043

February 0.085 0.079 0.044 0.041

March 0.065 0.060 0.035 0.032

April 0.049 0.045 0.027 0.024

May 0.049 0.044 0.025 0.022

June 0.044 0.041 0.023 0.021

July 0.040 0.038 0.021 0.019

August 0.043 0.040 0.023 0.020

September 0.041 0.038 0.023 0.020

October 0.047 0.045 0.027 0.024

November 0.062 0.058 0.033 0.031

December 0.085 0.078 0.043 0.040

7 x 7 

January 0.101 0.092 0.052 0.048

February 0.093 0.087 0.048 0.045

March 0.072 0.066 0.038 0.035

April 0.054 0.049 0.029 0.027

May 0.054 0.049 0.028 0.025

June 0.050 0.047 0.025 0.023

July 0.046 0.043 0.024 0.022

August 0.049 0.045 0.026 0.023

September 0.046 0.042 0.026 0.023

October 0.052 0.050 0.030 0.028

November 0.069 0.064 0.037 0.035

December 0.094 0.087 0.049 0.045

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Further, the degree of spatial difference was relatively higher in DNI compared to GHI (Figure 5, 

Figure 6, and Table 3). The DNI COV for both the TMY (DNI) and TDY data sets showed 

higher spatial variations, ranging from 0.03% during summer to 0.08% during winter; however, 

the GHI spatial variation in the TMY (GHI) and TGY data sets ranged from 0.02% during 

summer to 0.04% during winter. 

In general, the spatial information of the pixels would assist energy analysts in selecting a useful 

pixel resolution of solar resource data for specific applications (Gueymard et al. 2011). 

Figure 5. Understanding spatial variability of neighboring pixels for the 3 x 3, 5 x 5, and 7 x 7 grid 

comparisons for the annual TMY, TDY, and TGY data sets (pixels within the United States 

boundary). Dots are COVs, and the red line represents the mean of the 95% confidence level, 

which is the data in the green box. 

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Figure 6. Map showing spatial variability between neighboring pixels

The higher variation in the DNI than the GHI was attributed to the opacity of sky and also 

aerosols (Gueymard et al. 2011). The magnitude of variation (COV) in the DNI increased rapidly 

as the compared distance between the center pixel and the farthest pixel increased (Figure 6). 

The variation of DNI with adjacent pixels could provide system performance analysts with 

essential information on how the system energy output will be spatially variable in certain 

locations. 

The above inferences about adjacent pixels containing relatively less variance also hold true for 

the monthly data sets. For instance, Figure 7 shows DNI data for the month of January for the 3 x 

3, 5 x 5, and 7 x 7 grid comparisons. As shown in the figure, it is evident that there was less 

variance in the 3 x 3 matrix than in the 5 x 5 and 7 x 7 comparisons. 

Additionally, Figure 7 shows that the spatial differences between the TMY (DNI) and TDY data

sets are very similar for each grid comparison. 

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11 

Figure 7. Differences among various grid comparisons for the month of January. The arrow 

demonstrates the increased spatial variability with increased distance between pixels. 

4.3 Temporal Variability of the Typical Year Data Set 

The TMY, TDY, and TGY data sets have natural diurnal and seasonal variations and represent a 

year of typical climatic conditions of a location (Wilcox and Marion 2008). However, the data 

sets do not include extreme climatic events such as wildfire or volcanic eruptions. Therefore, 

care should be taken when using a typical year data set. 

In this section, a standard deviation for the monthly gridded NSRDB (1998–2009) data was 

calculated. The purpose of this analysis was to understand the effectiveness of the TMY data in 

representing the long-term variations. Table 4 demonstrates the average standard deviation for 

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12 

each month from the entire 12-year data set. As shown in the table, the months of February and 

May had higher standard deviations for DNI and GHI, respectively. 

Table 4. Average Standard Deviation (Temporal Variability) Obtained From the NSRDB 1998–2009 

Data Set 

Month Average Std. Deviation 

(DNI) 

Average Std. Deviation 

(GHI) 

January 0.623 0.206 

February 0.828 0.328 

March 0.729 0.349 

April 0.676 0.364 

May 0.752 0.434 

June 0.748 0.415 

July 0.592 0.324 

August 0.561 0.301 

September 0.597 0.299 

October 0.733 0.321 

November 0.635 0.23 

December 0.606 0.187 

Figure 8 shows the standard deviation distribution of the GHI and DNI data set for all pixels in 

the United States. The yellow box illustrates 95% confidence interval coverage, and the red line 

is the mean point for the 95% confidence interval data. 

Figure 8. Monthly standard deviation distribution in kWh/m2/day for the NSRDB gridded DNI and 

GHI data set 

The hypothesis was that the temporal variations that are apparent in Figure 8 would be captured 

by the TMY data set. This was true in the absence of extreme weather condition such as volcanic 

eruption or extensive wildfire. As shown in Figure 9, it is obvious that some months had higher 

standard deviations than others. Particularly, the month of May had high standard deviation in 

both the DNI and GHI data sets; therefore, the typical year data set would have some limitations 

in representing the month of May. 

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Figure 9. An illustration of temporal variability for the month of May using the NSRDB data set 

(1998–2009) for both (a) DNI and (b) GHI 

As illustrated above, the standard deviations of the irradiance for each month provide useful 

information on the temporal variability of a typical data set. As mentioned in previous sections, 

the typical year data set is generated through CDF analysis in which a typical month is selected 

with the monthly CDF closer to the long-term monthly average CDF. This means that the TMY 

generated for the month of May (Figure 9) (especially some pixels located in the western states 

such as California and Oregon) will have higher temporal variability. The selected month of May 

using the CDF will have relatively less representation of a typical month compared to other 

selected months. The annual temporal variations included in the typical data set do not depict the 

monthly variations; therefore, the user of the TMY data set must assess the importance of shorter 

term (monthly) versus long-term (annual) temporal variability when applying the data to a 

specific system performance project. Examples include the importance of heating and airconditioning loads (or the lack of the same) in the expected service area. 

5 Summary 

The results of the analysis depicted in this report show that the spatial and temporal variability 

have similar structure under the TMY (DNI) and TDY data set and also under the TMY (GHI) 

and TGY data set selection criteria. This is because the solar flux component is apparently the 

dominant weighting factor in the production of these data sets. However, a month-to-month 

comparison among typical data sets might have some differences because the sequence of month 

selection from each year in a typical data set could be different. For instance, the TMY (DNI) for 

the month of January could come from 1999, but the TDY could be from 2001. 

Overall, the inter-annual temporal and spatial variability were captured well among these data 

sets. Very few months demonstrated relatively higher temporal variability (standard deviation) 

than others. For these months, a typical data set would lack a full representation of the monthly 

variations when developing a typical year data set. In future productions of TMY data sets, it is 

recommend that a standard deviation could be used as one of the weighting factors, or as a 

source of additional uncertainty in the development of a typical year data set. 

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

Gueymard, C.A.; Wilcox, S.M. (2011). “Assessment of Spatial and Temporal Variability in the 

U.S. Solar Resource from Radiometric Measurements and Predictions from Models Using 

Ground-Based or Satellite Data.” Solar Energy (85); pp. 1,068–1,084. 

Mesinger, F., et al. (2006). “North American Regional Reanalysis.” Bulletin of the American 

Meteorological Society (87); pp. 343–360. 

Sawaqed, N.M.; Zurigat, Y.H.; Al-Hinai, H. (2005). “A Step-by-Step Application of Sandia 

Method in Developing Typical Meteorological Years for Different Locations.” International 

Journal of Energy Research (29:8); pp. 723–737. 

Wilcox, S.; Marion, W. (2008). Users Manual for TMY3 Data Sets. NREL/TP-581-43156. 

Golden, Colorado: National Renewable Energy Laboratory, April. Accessed November 2013: 

http://www.nrel.gov/docs/fy08osti/43156.pdf. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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7 Appendix A: Monthly Spatial Variability 

7.1 Monthly DNI Spatial Differences for the 3 x 3 Grid 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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7.2 Monthly DNI Spatial Differences for the 5 x 5 Grid 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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7.3 Monthly DNI Spatial Differences for the 7 x 7 Grid 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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7.4 Monthly GHI Spatial Differences for the 3 x 3 Grid 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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7.5 Monthly GHI Spatial Differences for the 5 x 5 Grid 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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7.6 Monthly GHI Spatial Differences for the 7 x 7 Grid 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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8 Appendix B: Monthly Temporal Variability 

8.1 Monthly DNI Standard Deviation (%) 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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8.2 Monthly GHI Standard Deviation (%) 

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