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Contract No. DE-AC36-08GO28308

Integration, Validation, and 

Application of a PV Snow 

Coverage Model in SAM 

David Ryberg and Janine Freeman

National Renewable Energy Laboratory 

Technical Report

NREL/TP-6A20-64260

September 2015
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-

Integration, Validation, and 

Application of a PV Snow 

Coverage Model in SAM

David Ryberg and Janine Freeman

National Renewable Energy Laboratory 

Prepared under Task No. SS13.5020

Technical Report

NREL/TP-6A20-64260

September 2015
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Abstract

Due to the increasing deployment of photovoltaic (PV) systems in snowy climates, there is 

significant interest in a method capable of estimating PV losses resulting from snow coverage 

that has been verified for a variety of system designs and locations. Many independent snow 

coverage models have been developed over the last 15 years; however, there has been little effort 

verifying these models beyond the system designs and locations on which they were based.

Moreover, major PV modeling software products have not yet incorporated any of these models 

into their workflows. In response to this deficiency, we have integrated the methodology of the 

snow model developed in the paper by Marion et al. (2013) into the National Renewable Energy 

Laboratory’s (NREL) System Advisor Model (SAM). In this work, we describe how the snow 

model is implemented in SAM, and we discuss our demonstration of the model’s effectiveness at 

reducing error in annual estimations for three PV arrays. Next, we use this new functionality in 

conjunction with a long-term historical data set to estimate average snow losses across the 

United States for two typical PV system designs. The open availability of the snow loss 

estimation capability in SAM to the PV modeling community, coupled with our results of the

nationwide study, will better equip the industry to accurately estimate PV energy production in 

areas affected by snowfall.
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Introduction

Snow coverage of PV systems and the associated losses in energy production have been 

recognized by the PV community as a significant loss that must be accounted for (Marion et al. 

2013; Becker et al. 2006; Powers, Newmiller, and Townsend 2010; Andrews, Pollard, and 

Pearce 2013; Sugiura et al. 2003). However, the convoluted dynamics of snow coverage on PV 

systems (snow removal processes in particular), in addition to the large variability in determining 

a location’s typical snowfall over the course of a year, have made a reliable model capable of 

estimating these losses infeasible for general use. Previous studies on this topic have measured 

losses in annual energy production ranging from 0% (Andrews, Pollard, and Pearce 2013) to 

25% (Powers, Newmiller, and Townsend 2010). Of course, these studies vary substantially in 

terms of the type of the PV array employed and the physical location in which the study took 

place. Moreover, several empirical models have been developed by the community that can 

estimate these losses (Townsend and Powers 2011; Andrews and Pearce 2012); however, in 

almost all cases there has been little or no effort to verify these models beyond the systems on 

which their design was based. There is a clear need within the community for a model capable of 

reliably predicting PV snow losses for a variety of PV system types and in a variety of locations. 

For this purpose, we have integrated the PV snow coverage model developed by Marion et al. 

(2013) into the National Renewable Energy Lab’s (NREL) System Advisor Model (SAM). The 

following report details the methodology of the model’s implementation, the results of a 

validation study against three PV systems that were not involved in the model’s creation, and 

finally, the results of a national study using the new snow model in SAM.
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Implementation

Marion’s Model

The PV snow coverage model in Marion et al. (2013) calculates the percentage of a PV array that 

will be covered by snow given system tilt, daily snow depth measurements, and hourly plane of 

array (POA) irradiance and temperature values. The model considers snow sliding to be the 

dominant removal process and therefore does not account for snow melting or wind removal

(except in the case of flat fixed-tilt systems). Other works, independent of Marion’s analysis, 

have also expressed snow sliding as a dominant removal process (Becker et al. 2006; Andrews, 

Pollard, and Pearce 2013; Sugiura et al. 2003). A brief description of Marion’s model is as 

follows. 

At the beginning of each day, the model checks to see if a snowfall has occurred during that day. 

If it has, the model assumes that the PV array being simulated will be completely covered by 

snow. If a new snowfall is not detected, the coverage is left at its value at the end of the previous 

day. For each hour in the day, the array will remain covered unless the plane of array irradiance

and ambient temperature are sufficient to allow some of the accumulated snow to slide off the 

PV array. More specifically, snow sliding will only occur so long as the following inequality is 

satisfied:

 

>

where represents the ambient temperature, represents the plane of array irradiance, and 

represents Marion’s empirically defined value 80 /( ^2 ° ). If the model determines that 

sliding is possible during a particular hour, then the amount of the PV array that will be exposed 

in that hour, measured in tenths of a row’s total height (see Figure 1), is a function of the PV 

system’s tilt.

Figure 1. Simplified diagram of a PV array
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The amount that will be exposed, in tenths of total row height, can be found using:

 

= 1.97 sin( )

The 1.97 constant in this equation was experimentally determined by Marion et al. (2013) for 

roof-mounted systems in units of tenths of PV row height per hour, and will be referred to as the 

sliding coefficient. At the end of the hour during which the calculation permits sliding, the initial

PV snow coverage will be decremented by the snow slide amount. Finally, given the new height 

of snow relative to the PV row’s total height, and the configuration of PV strings in a row, the 

number of PV strings within the system which are not covered with snow is determined. These 

modules are allowed to operate normally while the energy production of the covered strings is 

set to zero. The model then moves on to the next hour in the day and repeats this process.

Marion et al. (2013) also provided a sliding coefficient for ground-mounted systems, which is 

reported as 6.0 tenths of PV row height per hour. The discrepancy between this value and the 

1.97 value stems from the necessity of roof mounted systems to account for snow accumulating 

at the lower edge of an array and preventing snow removal from the lower modules. To date, 

however, only the sliding coefficient for roof-mounted systems has been incorporated into SAM 

because it was determined from a larger sample size in Marion’s analysis and is therefore better 

validated.

Implementation in SAM

The final implementation of Marion et al.’s (2013) model in SAM is procedurally very similar to 

Marion’s original model; however, there are a small number of differences that warrant 

discussion. Two of the changes were overrides that prevent illogical behavior. The first of these 

simply checks the snow coverage at the end of each time step and prevents the coverage from 

going below 0%. Second, when calculating the snow coverage at the beginning of each time step, 

we included an additional check for zero snow depth at that time. We assume that if the snow 

depth at that time is zero, then the coverage percentage on the PV array should also be zero. 

Without this check, snow would never slide off of zero-degree fixed-tilt PV arrays once it had 

accumulated. This second check was not required in Marion’s original model since that model 

was designed for system tilts between 10° and 45°. However, as we will discuss in the validation 

section, with this override the implemented PV snow coverage model also proves reasonable for 

flat systems.

Following this, by conducting a review of the currently available snow depth sensors, we found 

that many devices have an uncertainty between 0.5 and 1.0 cm. Therefore, we also included 

threshold values for minimum depth and minimum change in depth (delta), which are intended to 

filter out noise in the snow depth measurements and reduce spurious responses to data 

measurement uncertainty, such as findings of new snowfall during summer months. We 

incorporated these thresholds within the portion of the implementation that determines whether a 

new snowfall has occurred. If the original model identifies a new snowfall but either the total

snow depth is less than the depth threshold or the change in snow depth (with respect to the 

previous time step’s depth) is less than the delta threshold, then we assume this is an erroneous 

detection and it is ignored. We set the depth threshold to 1 cm and the delta threshold to 1 

cm/hour for consistency with snow depth measurement uncertainties; an additional sensitivity 
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analysis on these threshold values indicated that varying the threshold values within a range of 

0.5 to 1.5 cm and cm/hour respectively do not significantly affect estimated snow losses.

Lastly, Marion’s original model applied snow losses for an hourly simulation using a daily snow 

depth data set which was measured every day at 7am. However, SAM users may have access to 

more granular snow depth data. Additionally, SAM is capable of simulating PV performance for 

sub-hourly time intervals. Therefore, it was necessary to adapt the snow model’s implementation 

to allow for the usage of hourly and sub-hourly snow depth data sets as well as to accommodate 

sub-hourly simulations.

First, we sought to determine if the model would lose accuracy if the check for a new snowfall is 

performed hourly versus once a day. In order to compare the two methods fairly, we fabricated 

an hourly snow depth data set from a pre-existing daily data set by setting each hour in a single 

day to the snow depth record of the corresponding day in the daily set. Then, we executed the 

model in both its original daily form (including the changes discussed up until this point) and in 

another form that checks for a new snowfall at the start of each hour. We found that, as expected, 

the two methods produced identical results. Similarly, we conducted another study where we 

considered NREL’s Research Support Facility 2 (RSF2) PV array, which has hourly measured 

power outputs and meteorological data, including hourly snow depth data. We converted the 

hourly snow depth data for this site to a daily data set (by setting all values in a day to the value 

at 7 a.m. in the hourly set in accordance with Marion’s data collection procedure) and ran 

simulations in SAM with both sets. We found that the simulation with the hourly data set 

resulted in noticeably less error in annual energy compared to measured data than the simulation 

with the daily data (from a 2.3% error using the daily data to a 0.1% error for the hourly data). 

The method used to calculate these errors will be discussed further in the validation section. 

Because checking for a new snowfall at the beginning of hour as opposed to at the beginning of 

each day produced no difference when a daily snow depth data set was converted to an hourly 

set, and because the algorithm was shown to lose accuracy when the opposite conversion was 

performed, we decided to check for a new snowfall at the beginning of each time step for the 

final implementation. 

Second, we accounted for sub-hourly calculations. The workflow of the snow model is 

unchanged with the exception that the sliding coefficient and the delta threshold, which were 

originally determined as hourly values, are both scaled by the inverse of the number of time steps 

in an hour. For example, if SAM is provided with 15-minute weather data, then the sliding 

coefficient and the delta threshold are each multiplied by 0.25 to convert them from hourly to 15-

minute values.

Snow Model Usage in SAM

The resulting PV snow coverage model can be accessed in two separate ways: by running the 

model in conjunction with an ongoing PV simulation or by invoking the model after a complete 

PV simulation has occurred. In the desktop version of SAM, the snow model can be activated by 

navigating to the ‘Shading and Snow’ design page and selecting the ‘Estimate losses due to 

snow’ check box. Doing so will instruct the snow model to run in conjunction with the PV 

simulation and will logically be applied at the same point as similar losses (shading and soiling). 

In SAM’s workflow, this occurs after losses associated with the modules themselves (module 

efficiency and degradation) are applied, but before the inverter model is run. The same behavior 
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can be achieved either through SAM’s LK scripting language or in one of the SDK extension 

languages by setting the ‘en_snow_model’ variable to 1; its default value is 0, which deactivates 

the snow model. Once the snow model is activated, a SAM PV simulation can be executed 

normally and the final power reports will reflect snow loss estimations. Moreover, time series 

arrays of the amount of energy lost in each subarray due to snow coverage can be accessed 

through the ‘subarray[n]_snow_loss’ variables, where [n] is replaced with a specific subarray’s 

identification number. For more information on how to use SAM’s LK scripting language or 

access the SDK, visit the SAM webpage at sam.nrel.gov.

The second method of accessing the snow model—invoking the model after a simulation has 

occurred—can only be accomplished by using either SAM’s LK scripting language or through 

the SDK. Additionally, invoking the snow model in this manner requires that a time series array 

of the modeled system’s energy output be provided as an input to the snow model. This can be 

accomplished in one of two ways: 1) by executing a full SAM PV simulation, without having 

activated the snow model, in which case the required output variable will already be present in 

the SAM instance, or 2) by manually providing an energy output array, through the method 

discussed in SAM’s operating manual, and defining it as the ‘gen’ variable. The input ‘gen’ array

can be either DC or AC energy, a fact which we will discuss next. Once invoked, the snow 

model will calculate loss estimates due to snow coverage at each time step and will deduct the 

appropriate losses from the initial energy time series. Regardless of when the snow model is 

applied, its success is dependent on the input weather file containing valid snow depth data.

AC Versus DC Side Application

Snow coverage on PV arrays immediately results in a decreased DC power output by the array, 

so it makes the most sense to apply the losses estimated by a snow model at the same time that 

similar losses (shading and soiling) are applied. The empirical correlations in Marion’s model, 

however, were formulated using the measured AC power, so we examined whether the model is 

still valid when applied to the DC side of power conversion. 

As discussed previously, the way in which the model was implemented into SAM allows for 

execution of the snow model either simultaneously with SAM’s PV model workflow—

equivalent to applying snow losses on the DC side of power conversion—or independently after 

a SAM PV simulation has completed—equivalent to AC-side application. Fortunately, this made 

comparing the application of the model on either side a straightforward process. We ran a series 

of comparisons, each consisting of two simulations using identical system designs and weather 

files. One simulation included the snow model that ran during the SAM PV simulation (on the 

DC side) and the other simulation included the snow model that ran after the SAM PV 

simulation (on the AC side). In all cases, the final annual energy values predicted by the two 

simulations were within 2% of each other, which is within an acceptable error margin. This 

suggests that the side to which the snow model is applied is not of great importance. 

Nevertheless, users should be aware that slight differences are expected, particularly if they wish 

to post-process PV performance data obtained elsewhere using SAM’s snow loss model. Unless 

otherwise specified, it should be assumed that the snow model was applied on the DC side for 

the remaining analyses discussed in this work.
7

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Error Reduction Demonstration

Snow models have been available to the PV industry for close to two decades; however, in most 

cases there has not been a large validation effort. User confidence in the conclusions reached 

using a snow model may increase with greater similarity between the user’s system and the 

system, or systems, from which the snow model was developed, but this is not equivalent to 

systematic validation. Naturally, a complete validation of a snow model would require 

comparing simulation results to measured data sets for representatives of each type of system 

design in each type of weather climate—an effort which is far beyond the scope of this study 

even if such data sets existed. Nevertheless, in order to build confidence in the results reported 

by the PV snow coverage model implemented in SAM, we demonstrated the model’s 

effectiveness in reducing the error in estimating annual energy production with respect to 

measured data for three systems. None of these validation systems played a role in Marion’s 

original model’s conception.

Fixed-Tilt Systems

Two of the systems that were used for validation were the Forrestal system, located on the James 

Forrestal Building in Washington, D.C., and the RSF2 system, located on NREL’s Research 

Support Facility in Golden, Colorado. Both are fixed-tilt systems, with tilt angles of 0° and 10° 

respectively, that have previously been used as case studies for SAM validations. These

validations showed good agreement with SAM predictions excluding system downtime and time 

periods with heavy snowfall (Freeman et al. 2013). The concurrent weather data and measured 

data for the two systems come from the same data set used in Freeman et al. (2013); Nov. 2009 -

Jul. 2010 for the Forrestal system and 2012 for the RSF2 system.

The results of the snow model validation study are shown in Figure 2. For each month in a year, 

the figure shows SAM’s predicted energy output using the snow model in blue, the actual 

measured output of the system in green, and SAM’s predicted energy output when not using the 

snow model in red. The number of hours within each month that have a snow coverage 

percentage above zero, as calculated by the snow model, is displayed above the bars. Each 

system had several days of down-time, measurement failure, or missing data. For instance, there 

are no measured data for the Forrestal system spanning the entire months of August, September,

and October. The Forrestal system is also missing 7 days of data in mid-July as well as the first 

13 days of November, which is why these months display less power output than would be 

expected during these times. The RSF2 system fares better in the sense that the measured data 

are only missing 38 days throughout the year. Twenty-two of these missing days occur in the 

month of July and the remaining 16 are distributed sporadically throughout the year. For our 

analysis, if the measured data were missing or were otherwise flagged as erroneous for a 

particular time period, then the simulated outputs for that time period were ignored and weren’t 

factored into error calculations.
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Figure 2. Results from the validation study using the Forrestal system in Washington, D.C. and the 

RSF2 system in Golden, Colorado

Fortunately, very few of the missing days occur during the months with heaviest snowfall 

(January, February, and December). Before error calculations were made, however, a scaling 

factor was applied to the results of each simulation in order to provide the best fit to the 

measured data during the summer months (April – Aug) and these values are displayed in the 

top-left corner of each plot of Figure 2. This was done in order to help isolate any error changes 

which resulted from activating the snow model, rather than having this error skewed by 

systematic error that is present year-round. Table 1 displays the errors compared to measured 

data associated with each of the winter months, as well as the errors in total annual energy. Error 

calculations were performed using:
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= × 100%

Reductions in absolute annual error were also included in order to provide a sense of how 

changes in each particular month affected the total annual error. These values are calculated 

using:

. . = ( | | | | )

In Table 1, positive error values correspond to an over-prediction of the estimated energy 

production, while negative values indicate an under-prediction. A decrease in the absolute value 

of these error percentages, reported as a positive value in the ‘Absolute Annual Error Reduction’ 

row, reflects an improvement in the simulation’s annual prediction, because the estimated energy 

production with the snow model activated is closer to the measured production. In the majority 

of cases, the snow model is observed to significantly improve the monthly estimated energy. 

December of the RSF2 system is the only exception to this, which will be discussed below. For 

both systems, the month of February contributed most to annual error before the snow model was 

employed, and likewise showed a huge improvement in energy production estimations—

improving the total annual error by close to 5% in each case. Most importantly, the snow model 

is observed to greatly improve SAM’s post-scaling factor estimate in annual energy in both 

cases: from total absolute annual error of 9.9% to 1.9% for the Forrestal system and from 7.3% 

to 0.1% for the RSF2 system. 

Table 1. Monthly and Annual Errors With and Without Snow Model

The snow model is observed to both over-predict and under-predict energy estimates in an 

unforeseeable fashion on a monthly, daily, or hourly basis. This is expected behavior, however, 

since Marion et al. (2013) states that the original model performs well on an annual average 

despite the fact that “large differences between modeled and measured energy losses should be 

expected for monthly or shorter time periods” (119). For this reason, results from the model 

implemented in SAM should only be factored into annual considerations and not applied to 

monthly or shorter time periods.

January February December Annual

Forrestal

With Model (%) -2.9 -85.0 -3.0 -1.9

Without Model (%) 11.3 337.6 40.4 9.9

Absolute Annual Error Reduction (%) 0.6 5.4 1.7 8.0

RSF2

With Model (%) -2.4 64.3 -23.2 -0.1

Without Model (%) 12.7 302.1 13.4 7.3

Absolute Annual Error Reduction (%) 0.6 4.7 -0.5 7.2
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One-Axis Tracking Systems

Sun tracking systems are believed to be much less affected by snow coverage than fixed-tilt 

systems due to the vibrations and movements of the panels. Nevertheless, the ability to estimate 

snow losses on tracking systems, however slight they may be, is a recognized need. Because it 

does not address any potential effects of system movement, Marion’s PV snow coverage model 

was originally only intended for fixed-tilt systems, so we investigated the implemented model’s 

efficacy when applied to tracking systems. Unfortunately, we did not have measured data for a 

two-axis tracking system, but we were able to apply our model to a one-axis tracking system: the 

Mesa Top PV array located in Golden Colorado. The measured data and system specifications 

were also taken from the work by Freeman et al. (2013). This was the third system used in our 

validation study and the results of this work are shown in Figure 3, while the associated errors 

are included in Table 2.

Figure 3. Results from applying the implemented snow model to the Mesa Top one-axis tracking 

array in Golden, Colorado

Like the fixed-tilt systems, the Mesa Top simulations showed significant improvement when the 

snow model was activated to post-scaling factor simulations, reducing the annual energy 

estimation error from 5.2% to 1.1%. It can be seen in Figure 3, however, that a large portion of 

the annual error, both with and without the snow model, comes from September— a month with 

no measured snowfall. At this time we are unsure of the cause of the discrepancy observed in 

September; however, in order to isolate changes in error resulting from using the snow model,

we can artificially lower the simulation’s output in September to the measured value. By doing 

so, and recalculating the annual errors, we find that the annual error without activating the snow 

model would be 4.1% while the annual error with activating the snow model would be -2.3%. 

Both methods of calculating the annual error are reported in Table 2. While this still represents 

an improvement in the annual energy predictions, the improvement is not nearly as much as was 

observed for the fixed-tilt systems. Moreover, it is clear that the snow model is over-predicting 

snow losses in December by a large margin. As previously mentioned, this observation is likely 
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due to the fact that the snow model does not account for snow removal resulting from movement 

of the array’s panels, which likely speeds the snow removal process. Nevertheless, with the 

exception of December, the snow model is observed to significantly improve SAM’s energy 

production estimates for months with snowfall. These results build confidence that the snow 

model implemented in SAM can help reduce annual prediction error for tracking systems. 

Table 2. Monthly and Annual Errors With and Without Snow Model

This study also estimated the effect of nighttime system position on annual energy output. Most 

single-axis tracking systems, including the Mesa Top system, are kept in a “stow” position 

overnight. This means that the rows (Figure 1) are positioned such that average wind loads are 

minimized, which, for the Mesa Top system, amounts to a nighttime tilt of 5° to the east. Lower 

tilt angles inhibit snow removal by sliding, which is likely the only active snow removal process 

during the night. By instructing the simulation of the Mesa Top system to set the nighttime tilt 

angle to 20° instead of 5°, we found that the simulated system would produce an estimated 

19,800 kWh of additional energy over the course of the year, a 1.49% increase, as a direct result 

of snow sliding during the night. This suggests that an analysis of the empirical effect of 

increasing the tilt angle of the nighttime stow position in response to expected snowfall could 

quantify an increase in tracking system energy production.

January February October November December Annual Annual

altered 

September

Mesa 

Top

With Model 

(%) -1.0 -4.1 1.2 1.8 -47.7 -1.1 -2.3

Without 

Model (%) 21.0 22.3 7.6 12.3 11.6 5.2 4.1

Absolute

Annual

Error 

Reduction 

(%)

0.9 1.0 0.5 0.6 -1.7 4.1 1.9
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National Study

Following the validation exercises, we applied the snow model to a wide variety of locations in 

order to estimate typical trends in PV snow coverage losses across the continental United States. 

In order to accomplish this, we constructed two common system designs in SAM and then used 

the 1961-1990 NSRDB data set— the underlying historical data used to produce the NSRDB’s 

TMY2 data set. This data set is comprised of hourly meteorological weather data (including 

daily snow depth measurements) for 239 locations across the United States, spanning the years 

of 1961 through 1990. For each of the two system types, two simulations were conducted for 

every year at each location: one without the snow model activated as well as one with the snow 

model activated, and a percent difference was calculated.

The system designs we used assume the tilt-equals-latitude and tilt-equals-20° conventions, both 

of which are common designs in the PV industry. In the first case, not only is it thought that

setting the tilt angle of a fixed-tilt PV array to the latitude of its location will maximize the total 

solar radiation over a single year (ignoring shading effects of the surrounding environment), but 

also, since snowy climates are generally found in the northern areas of the country, the larger tilt 

angles at these locations are expected to promote quicker snow removal, thereby mitigating the 

array’s losses from snow coverage. On the other hand, system tilts of 20° are found fairly 

commonly in the industry due to the simplicity and common availability of this system design.

Beyond setting tilt angles to either the latitude or 20° for each simulation, however, all other 

parameters have been left as the SAM defaults for the detailed photovoltaic/ no financial model 

in version 2015.1.30. This facilitates replication and avoids listing the many input parameters 

required by SAM’s pvsamv1 compute module. The results of this study are displayed in 

Figure 4.

Figure 4 shows the average percent loss in PV production due to snow coverage over the thirty 

years at each of the TMY2 sites for both system designs (see Appendix for full data). For both 

system designs, most PV snow loss is concentrated in an area beginning in the Northeast, 

spanning the Great Lakes, and continuing into the Midwest and Rocky Mountain regions 

(particularly in Wyoming). This general trend was certainly an expected outcome of this study; 

however, it is still surprising to see that PV arrays in these areas might experience losses between 

15- 25% of their total available energy output. It is also surprising to see that areas which are not 

generally associated with snow, such as eastern Nevada, might also see losses between 5-10% of 

their annual PV energy production. There are pockets within the Rocky Mountain and Southwest 

regions, located in high altitude areas, which are estimated to suffer from much higher annual 

losses than the surrounding regions. One instance of this, Flagstaff, Arizona, was estimated to 

lose an average of 12% of its total annual energy to snow coverage while its closest neighbor, 

Phoenix, Arizona, was only estimated to lose 2%.

The most unexpected outcome of this study, however, is certainly the fact that 20° tilt systems 

actually showed slightly lower losses on a percentage basis compared against tilt-equals-latitude

systems at almost all locations, contrary to our initial hypothesis. This may be due to the fact 

that, even though the 20° systems likely suffer a higher percentage of monthly snow loss in the 

winter months compared to their tilt-equals-latitude counterparts, the benefit of lower tilt angles 

during the summer is enough to overcome this effect. Of course, these maps only display the 

average percent loss at each location and say nothing about the total annual production of either 
13

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

system. In general, even when accounting for snow coverage, the tilt-equals-latitude systems 

were still observed to produce more annual energy than their 20° tilt counterparts. 

Figure 4. Results from a national study modeling PV production losses due to snow coverage 

using both a fixed-tilt tilt-equals-latitude and a constant 20° tilt system design, the historical TMY2 

data set, and the newly implemented snow model in SAM 

Note: The values displayed at each of the data sites in this study were found by determining, for 

each individual year and system type, the difference between the results of a simulation without 

the snow model activated and a simulation with the snow model activated. Then an average and 

standard deviation of these differences were calculated from the 30 years at each of the locations. 

Finally, each location was normalized by the average of the values without the snow model 

activated in order to isolate the effects of the snow losses as a percentage.
14

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

From observations based on Figure 4, we have constructed general trends in snow losses across 

the United States. Figure 5 displays these results. This map can be utilized as a starting point for 

snow loss estimations for PV systems where better snow loss information is unavailable. For 

instance, if a PV system is to be built in western Kansas and a simulation, which does not 

account for snow, of this system estimated that it would produce 8,000 kWh annually, then this 

figure indicates that the designer might also want to include a 4% to 10% loss attributed to snow.

It is important to note, however, that these values may not be representative of any particular 

year, nor do they account for any microclimates that might be present. Rather, these values are 

only meant to represent a starting point for estimating snow losses in a given area before a more 

specialized analysis can be performed. If measured snow depth data is available for a given 

location or year being analyzed, it would certainly be beneficial to simulate the PV system using 

the measured snow depth data instead of simply using the loss indicated in Figure 5.

Figure 5. General trends in average snow losses as a percentage of annual energy production 

Note: Like-colored regions have similar loss percentages and are labeled in the figure. The 

specific region around Nevada and the Four Corners states is special (indicated by an *) in that 

high altitude regions, such as Flagstaff, Arizona and Ely, Nevada should be considered to be in the 

next higher tier of snow losses. This plot is a broad enough generalization that it may apply to 

either a tilt=latitude or a tilt=20° system.

Finally, by sorting simulation results based on snow depth rather than geographic location, we 

were also able to find an interesting correlation between total snow depth throughout a year and 

the total percent loss resulting from snow coverage for the tilt-equals-latitude systems. The 

results of this are displayed in Figure 6. This figure uses the sum of hourly snow depths, found 

using:
15 

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

( ) = snow_depth[ ]

where S indicates the sum of hourly snow depths, h indicates the hour in the year, and 

snow_depth[ ] refers to the measured snow depth at the i

th hour. This quantity takes into account 

both the total snowfall throughout the course of a year as well as snow persistence during that 

year. The percent loss reported for each simulation was calculated in the same manner as it was 

for Figure 4: by subtracting the result of the simulation with the snow model activated from the 

result of the simulation without the snow model activated, followed by normalizing this number

by the latter. Figure 6 shows a predictable trend in which more snow depth throughout a year 

corresponds to higher percent loss. A fit for this data is shown in the figure; the spread of the 

percent loss at each summed snow depth value is generally observed to be close to ±50% of the 

fitted curve’s value at that point.

Figure 6. Correlation between the sum of the hourly snow depth array and the resulting percent 

loss for each year of each location in the tilt-equals-latitude national study

Conclusions and Future Work

For several years, the PV community has expressed a need for a reliable model that estimates 

losses in PV energy production resulting from snow coverage. In an effort to fill this void, we 

have implemented a snow model into SAM and demonstrated its effectiveness against two fixedtilt systems and a one-axis tracking system, none of which were a part of the model’s original 

development. Subsequently, we conducted a nationwide study to estimate national trends in 

snow loss percentages to serve as a starting point for more accurate modeling of PV production 

in snowy areas. 
16

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

The final implemented PV snow coverage model was kept as similar as possible to Marion’s 

original model (Marion et al. 2013). A few changes were necessary, however, in order to prevent 

illogical behavior of the model when implemented in SAM, as well as to accommodate for 

hourly and sub-hourly simulations. We showed that the model could be applied on either the DC 

or the AC side of power conversion with very little effect (<2%) on the resulting energy 

estimates. During our validation study, we observed that our implemented model decreased the 

estimated annual energy error (in comparison to measured values) by over 75% for the fixed-tilt 

systems and by 36% for the tracking system. These represent significant improvements to 

SAM’s performance in snowy conditions and, as a result, we are confident the snow model will 

provide meaningful snow loss estimations to SAM users. In this analysis, we also discovered that 

increasing the tilt angles of tracking systems during the night may have the potential to slightly 

increase the annual energy production of these systems by allowing snow to slide off more 

easily.

Our national study agreed with previous studies that PV snow coverage losses for fixed-tilt 

systems range from 0% to 25%, which is summarized in a map of general estimated snow loss 

trends across the continental United States. The results from this study can potentially be used as 

a starting point for future predictions of PV snow losses in these areas. Lastly, we were also able 

to produce a correlation between total snow depth over a year and the percent loss for fixed-tilt, 

tilt-equals-latitude systems, which could also be a good starting point for snow loss estimations if

a user has access to snow depth data.

There are certainly areas for improvement in the snow model. It has been shown to improve 

energy predictions on an annual scale, but the model can both over-predict or under-predict snow 

losses for any given hour, day, or month (Figure 2). Any effort to reduce these variations will do 

much to further improve upon the model’s accuracy. This could mean incorporating snow 

removal processes other than sliding, such as melting and wind removal. Another possible 

improvement would be to investigate whether or not the sliding coefficient discussed in the 

implementation section is a function of snow consistency or module temperature. Additionally, 

the model does not account for time-delayed effects, such as a particularly cold night freezing the 

bottom layer of snow onto a PV array and preventing sliding during the day. Lastly, although we 

demonstrated the model’s efficacy against three independent systems, this model will certainly 

not always produce reliable results for any PV system in any location. As measured data sets for 

other systems in snowy areas become available, we hope to continue validation of this snow 

model with additional systems.

In the meantime, we hope that this tool will allow the PV community to make more accurate 

annual energy estimates for systems in all areas of the United States, particularly in the areas 

which are heavily affected by snow, thereby encouraging more informed technical and financial 

decisions in the development of future PV systems. 
17

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

References

Marion, B.; Schaefer, R.; Caine, H.; Sanchez, G. (2013). “Measured and modeled photovoltaic 

system energy losses from snow for Colorado and Wisconsin locations.” Solar Energy 97; pp. 

112-121. 

Becker, G.; Schiebelsberger, B.; Weber, W.; Vodermayer, C.; Zehner, M.; Kummerle, G. (2006). 

“An approach to the impact of snow on the yield of grid-connected PV systems.” Proc. 

European PVSEC.

Powers, L.; Newmiller, J.; Townsend, W. (2010). “Measuring and modeling the effect of snow 

on photovoltaic system performance.” Presented at Photovoltaic Specialists Conference (PVSC). 

Andrews, R.W.; Pollard, A.; Pearce, J.M. (2013). “A new method to determine the effects of 

hydrodynamic surface coatings on the snow shedding effectiveness of solar photovoltaic 

modules.” Solar Energy Materials and Solar Cells 113; pp. 71-78. 

Sugiura, T.; Yamadaa, T.; Nakamuraa, H.; Umeyaa, M.; Sakutab, K.; Kurokawa, K. (2003). 

“Measurements, analyses and evaluation of residential PV systems by Japanese monitoring 

program.” Solar Energy Materials and Solar Cells 73(3); pp. 767-779. 

Townsend, T.; Powers, L. (2011). “Photovoltaics and snow: An update from two winters of 

measurements in the sierra.” in PVSC. 

Andrews, R.W.; Pearce, J.M. (2012). “Prediction of energy effects on photovoltaic systems due 

to snowfall events.” Presented at PVSC . 

Freeman, J.; Whitmore, J.; Kaffine, L.; Blair, N.; Dobos, A.P. (2013). “System Advisor Model: 

Flat plate photovoltaic performance modeling validation report.” NREL/TP-6A20-60204.

Golden, CO: National Renewable Energy Laboratory. Accessed April 

2015: http://www.nrel.gov/docs/fy14osti/60204.pdf. 

“National Solar Radiation Data Base: 1961- 1990: Typical Meteorological Year 2.” NSRDB

(1995). Accessed March 2015: http://rredc.nrel.gov/solar/old_data/nsrdb/1961-1990/tmy2/.
18

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

Appendix

Tabulated results from tilt-equals-latitude study

Locations whose losses are listed as a dash (-) were unable to be simulated in this study and do 

not reflect a calculation of zero snow loss for that location.

City State Average Percent 

Loss

STD Percent 

Loss City State Average Percent Loss

STD Percent 

Loss

Abilene TX 0.7 0.7 Lander WY 18 4.6

Akron OH 9.7 2.6 Lansing MI 12.2 2.7

Alamosa CO 11.8 5.5 Las Vegas NV 0 0.2

Albany NY 10.4 3.9 Lewistown MT 13.4 6.6

Albuquerque NM 1.6 1.1 Lexington KY 3.7 2.6

Allentown PA 6.6 3 Lihue HI 0 0

Alpena MI 18.6 4 Little Rock AR 0.7 0.7

Amarillo TX 3 1.7 Long Beach CA - -

Anchorage AK 14.8 6.1 Los Angeles CA 0 0

Annette AK 2.5 2.1 Louisville KY - -

Arcata CA 0 0.1 Lubbock TX 1.6 1.1

Asheville NC 2 1.4 Lufkin TX 0.1 0.2

Astoria OR 0.3 0.6 Lynchburg VA - -

Athens GA - - Macon GA 0.1 0.3

Atlanta GA 0.2 0.3 Madison WI 14.3 4.1

Atlantic City NJ 2.7 1.8 Mansfield OH 9.7 2.8

Augusta GA 0.2 0.3 Mason City IA 15.6 5.5

Austin TX 0.1 0.3 Massena NY 16.7 4.3

Bakersfield CA 0 0 Mcgrath AK 26.9 6.7

Baltimore MD 3 2 Medford OR 0.3 0.5

Barrow AK 55.9 10.2 Memphis TN 0.8 0.9

Baton Rouge LA 0 0.1 Meridian MS 0.2 0.4

Bethel AK 25.5 9.3 Miami FL 0 0

Bettles AK 33.8 5.2 Midland TX - -

Big Delta AK 25.8 6.3 Miles City MT - -

Billings MT 12.2 3.7 Milwaukee WI 12.4 4

Binghamton NY 14.5 2.8 Minneapolis MN 17.7 4.9

Birmingham AL 0.2 0.3 Minot ND - -

Bismarck ND 16.5 4.8 Missoula MT 7.8 3.1

Boise ID 3.7 3 Mobile AL 0.1 0.2

Boston MA 6 2.4 Moline IL 9.4 3.8

Boulder CO 10.1 2.7 Montgomery AL 0.1 0.2

Bradford PA 18.1 2.3 Muskegon MI 11.4 2.8

Bridgeport CT 5.1 2.4 Nashville TN 1.8 1.4

Bristol TN 2.7 1.6 New Orleans LA 0 0.1

Brownsville TX 0 0 New York City NY 3.7 1.9

Buffalo NY 12.9 2.6 Newark NJ 4.4 2.1

Burlington VT 15.1 4.6 Nome AK 31.3 8.9

Burns OR - - Norfolk NE 11.3 4

Cape Hatteras NC 0.2 0.4 Norfolk VA 1.1 1.2

Caribou ME 23.6 4.5 North Bend OR 0.1 0.4

Casper WY 15 3.6 North Platte NE 8.8 3.6

Cedar City UT 7.4 3.5 Oklahoma City OK 1.8 1.5

Charleston SC - - Olympia WA 0.9 1.2

Charleston WV 5.2 2.3 Omaha NE 9.7 3.7

Charlotte NC 0.8 0.6 Pendleton OR 2.4 1.9

Chattanooga TN 0.5 0.5 Peoria IL 8.3 4

Cheyenne WY 11 3 Philadelphia PA 3.5 2

Chicago IL 9.5 3.6 Phoenix AZ 0 0

Cleveland OH 10 2.5 Pierre SD 11.1 4.3

Cold Bay AK 12.1 4.8 Pittsburgh PA 8.1 2.8

Colorado 

Springs CO 4.4 3.9 Pocatello ID 8 3.5

Columbia MO 6 2.3 Port Arthur TX 0 0.1

Columbia SC 0.2 0.4 Portland ME 12.4 3.1

Columbus GA 0.1 0.2 Portland OR 0.3 0.4

Columbus OH 6.2 3.3 Prescott AZ 1.9 1.5
19

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

City State Average Percent 

Loss

STD Percent 

Loss City State Average Percent Loss

STD Percent 

Loss

Concord NH 12.6 3.5 Providence RI 5.9 2.3

Corpus Christi TX 0 0 Pueblo CO 5.1 2.5

Covington KY 5 3.3 Quillayute WA 0.8 1.2

Cut Bank MT - - Raleigh NC 1.2 1

Daggett CA 0 0.1 Rapid City SD 11.3 3.8

Dayton OH 7 3.5 Redmond OR 2.8 1.4

Daytona Beach FL 0 0 Reno NV 2.8 1.7

Des Moines IA 10.7 4 Richmond VA 2.1 1.4

Detroit MI 9.2 3.3 Roanoke VA 2.2 1.7

Dodge City KS 5.1 2.2 Rochester MN 18.8 5.5

Duluth MN 26.5 5 Rochester NY 13 2.3

Eagle CO - - Rock Springs WY 14 5

Eau Claire WI 19.5 4.8 Rockford IL 11.7 4.4

El Paso TX 0.5 0.7 Sacramento CA 0 0

Elkins WV - - Saint Cloud MN 19.7 5.6

Elko NV 6.1 3.7 Salem OR 0.4 0.8

Ely NV 11.3 4.5 Salt Lake City UT 7.7 3.3

Erie PA 10.7 2.4 San Angelo TX 0.4 0.3

Eugene OR 0.5 0.8 San Antonio TX 0.1 0.2

Evansville IN 3.8 2.6 San Diego CA 0 0

Fairbanks AK 24.9 5.9 San Francisco CA 0 0

Fargo ND 19.9 6.4 San Juan PR 0 0

Flagstaff AZ 11.9 4.5 Santa Maria CA 0 0

Flint MI 11.8 2.8 Sault Ste. 

Marie MI 25.4 4.9

Fort Smith AR 1.2 1.4 Savannah GA 0.1 0.3

Fort Wayne IN 8.9 3.2 Scottsbluff NE 8.9 1.9

Fort Worth TX 0.5 0.6 Seattle WA 0.6 1

Fresno CA 0 0 Sheridan WY 14.7 3.4

Glasgow MT 13.8 5.5 Shreveport LA 0.3 0.5

Goodland KS 7.8 2.5 Sioux City IA 10.9 4.3

Grand Island NE 9.8 3.8 Sioux Falls SD 13.8 5

Grand Junction CO 7 3.8 South Bend IN 11.9 3

Grand Rapids MI 12.4 3.1 Spokane WA 4.8 2.8

Great Falls MT 11.8 4.1 Springfield IL 7.2 3.2

Green Bay WI 16.4 4.4 Springfield MO 3.9 1.8

Greensboro NC 1.4 1.1 St Paul Is. AK 20.1 11

Greenville SC 0.6 0.5 St. Louis MO 5.2 2.8

Guam PI 0.2 0 Sterling VA 3.3 1.9

Gulkana AK 23.1 5.6 Syracuse NY 13.9 2.7

Harrisburg PA 5.6 2.7 Talkeetna AK 20.4 7.2

Hartford CT 8.1 2.7 Tallahassee FL 0 0

Helena MT 9.6 3.9 Tampa FL 0 0

Hilo HI 0 0 Toledo OH 9.5 3.2

Honolulu HI 0 0 Tonopah NV 1.8 1.2

Houghton MI 16.9 9.3 Topeka KS 5.5 2.7

Houston TX 0 0.1 Traverse City MI 16.4 3.3

Huntington WV 4 2.3 Tucson AZ 0.1 0.1

Huntsville AL 0.5 0.6 Tucumcari NM - -

Huron SD 13.9 4.6 Tulsa OK 2 1.5

Indianapolis IN 6.6 3.3 Victoria TX 0 0.1

International 

Falls MN 25.7 5.6 Waco TX 0.2 0.3

Jackson MS 0.2 0.4 Waterloo IA 12.7 4.7

Jacksonville FL 0 0.1 West Palm 

Beach FL 0 0

Kahului HI 0 0 Wichita Falls TX 1 1

Kalispell MT - - Wichita KS 4 2.3

Kansas City MO 4.7 3.1 Wilkes-barre PA 8.9 3

Key West FL 0 0 Williamsport PA 7.4 2.8

King Salmon AK 15.3 8 Wilmington DE 3.6 2.2

Knoxville TN 1.5 1.2 Wilmington NC 0.3 0.5

Kodiak AK 7.7 4.7 Winnemucca NV 4.1 2.9

Kotzebue AK 38 7.9 Worchester MA 12.3 4.3

La Crosse WI - - Yakima WA 2.8 1.6

Lake Charles LA 0 0.1 Yakutat AK 14.1 6.4

Youngstown OH 10.6 2.8
20

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

Tabulated results from 20° tilt study

Locations whose losses are listed as a dash (-) were unable to be simulated in this study and do 

not reflect a calculation of zero snow loss for that location.

City State Average Percent 

Loss

STD Percent 

Loss City State Average Percent Loss

STD Percent 

Loss

Abilene TX 0.7 0.6 Lander WY 16.6 4.4

Akron OH 8.9 2.4 Lansing MI 11.3 2.5

Alamosa CO 10.6 4.9 Las Vegas NV 0 0.1

Albany NY 9.5 3.5 Lewistown MT 14.7 3.7

Albuquerque NM 1.5 1 Lexington KY 3.5 2.4

Allentown PA 6.1 2.7 Lihue HI - -

Alpena MI 17 3.5 Little Rock AR 0.6 0.7

Amarillo TX 2.8 1.6 Long Beach CA - -

Anchorage AK - - Los Angeles CA 0 0

Annette AK - - Louisville KY - -

Arcata CA 0 0.1 Lubbock TX 1.5 1.1

Asheville NC 1.9 1.3 Lufkin TX 0.1 0.2

Astoria OR 0.3 0.5 Lynchburg VA - -

Athens GA - - Macon GA 0.1 0.2

Atlanta GA 0.2 0.3 Madison WI 12.9 3.7

Atlantic City NJ 2.6 1.6 Mansfield OH 8.9 2.5

Augusta GA 0.2 0.3 Mason City IA 14.1 5

Austin TX 0.1 0.3 Massena NY 15.3 3.9

Bakersfield CA 0 0 Mcgrath AK - -

Baltimore MD 2.8 1.8 Medford OR 0.3 0.4

Barrow AK - - Memphis TN 0.8 0.9

Baton Rouge LA 0 0.1 Meridian MS 0.2 0.3

Bethel AK - - Miami FL 0 0

Bettles AK - - Midland TX - -

Big Delta AK - - Miles City MT - -

Billings MT 10.8 3.3 Milwaukee WI 11.2 3.6

Binghamton NY 13.5 2.5 Minneapolis MN 15.9 4.6

Birmingham AL 0.2 0.3 Minot ND - -

Bismarck ND 14.5 4.3 Missoula MT 7.1 2.8

Boise ID 3.3 2.5 Mobile AL 0.1 0.2

Boston MA 5.7 2.1 Moline IL 8.5 3.4

Boulder CO 9.4 2.4 Montgomery AL 0.1 0.2

Bradford PA 16.8 2.1 Muskegon MI 10.9 2.6

Bridgeport CT 4.8 2.2 Nashville TN 1.7 1.3

Bristol TN 2.5 1.5 New Orleans LA 0 0.1

Brownsville TX 0 0 New York City NY 3.5 1.8

Buffalo NY 11.9 2.3 Newark NJ 4.1 1.8

Burlington VT 13.8 4 Nome AK - -

Burns OR - - Norfolk NE 10.1 3.7

Cape Hatteras NC 0.2 0.4 Norfolk VA 1.1 1.1

Caribou ME 21.4 3.9 North Bend OR 0.1 0.4

Casper WY 13.4 3.2 North Platte NE 8 3.3

Cedar City UT 7 3.2 Oklahoma City OK 1.7 1.4

Charleston SC - - Olympia WA 0.8 1

Charleston WV 4.9 2.1 Omaha NE 8.8 3.4

Charlotte NC 0.8 0.6 Pendleton OR 2.1 1.6

Chattanooga TN 0.5 0.5 Peoria IL 7.6 3.6

Cheyenne WY 10.3 2.8 Philadelphia PA 3.3 1.9

Chicago IL 8.6 3.1 Phoenix AZ 0 0

Cleveland OH 9.2 2.2 Pierre SD 10 4

Cold Bay AK - - Pittsburgh PA 7.6 2.6

Colorado 

Springs

CO 7.1 2.9 Pocatello ID 7.4 3.5

Columbia MO 5.6 2.2 Port Arthur TX 0 0.1

Columbia SC 0.2 0.4 Portland ME 11.5 2.9

Columbus GA 0.1 0.2 Portland OR 0.3 0.3

Columbus OH 5.7 3 Prescott AZ 1.8 1.3

Concord NH 11.7 3.3 Providence RI 5.6 2.2

Corpus Christi TX 0 0 Pueblo CO 4.8 2.1

Covington KY 4.5 3 Quillayute WA 0.9 1

Cut Bank MT - - Raleigh NC 1.1 0.9

Daggett CA 0 0 Rapid City SD 10.4 3.6
21

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

City State Average Percent 

Loss

STD Percent 

Loss City State Average Percent Loss

STD Percent 

Loss

Dayton OH 6.4 3.2 Redmond OR 2.4 1.1

Daytona Beach FL 0 0 Reno NV 2.6 1.6

Des Moines IA 9.7 3.8 Richmond VA 2 1.3

Detroit MI 8.4 2.9 Roanoke VA 2.2 1.6

Dodge City KS 4.7 2 Rochester MN 16.9 5

Duluth MN 23.6 4.5 Rochester NY 12 2

Eagle CO - - Rock Springs WY 13.3 4.3

Eau Claire WI 17.3 4.4 Rockford IL 10.8 3.8

El Paso TX 0.5 0.6 Sacramento CA 0 0

Elkins WV - - Saint Cloud MN 17.7 5

Elko NV 5.7 3.4 Salem OR 0.4 0.6

Ely NV 10.2 3.9 Salt Lake City UT 7.2 3

Erie PA 9.9 2 San Angelo TX 0.4 0.3

Eugene OR 0.4 0.7 San Antonio TX 0.1 0.2

Evansville IN 3.5 2.5 San Diego CA 0 0

Fairbanks AK - - San Francisco CA 0 0

Fargo ND 17.6 5.6 San Juan PR - -

Flagstaff AZ 12.5 4 Santa Maria CA 0 0

Flint MI 10.9 2.5 Sault Ste. 

Marie

MI 22.8 4.6

Fort Smith AR 1.1 1.4 Savannah GA 0.1 0.2

Fort Wayne IN 8 3 Scottsbluff NE 8.2 1.8

Fort Worth TX 0.5 0.6 Seattle WA 0.5 0.8

Fresno CA 0 0 Sheridan WY 13.5 3.3

Glasgow MT 12.2 4.9 Shreveport LA 0.3 0.4

Goodland KS 7.2 2.2 Sioux City IA 10 4.2

Grand Island NE 8.8 3.4 Sioux Falls SD 12.6 4.7

Grand Junction CO 6.4 3.4 South Bend IN 10.9 2.8

Grand Rapids MI 11.6 3 Spokane WA 4.3 2.5

Great Falls MT 10.4 3.7 Springfield IL 6.6 3

Green Bay WI 14.6 3.9 Springfield MO 3.7 1.8

Greensboro NC 1.3 1 St Paul Is. AK - -

Greenville SC 0.6 0.5 St. Louis MO 4.8 2.6

Guam PI - - Sterling VA 3.2 1.7

Gulkana AK - - Syracuse NY 12.8 2.3

Harrisburg PA 5.4 2.5 Talkeetna AK - -

Hartford CT 7.5 2.5 Tallahassee FL 0 0

Helena MT 8.6 3.4 Tampa FL 0 0

Hilo HI - - Toledo OH 8.6 2.9

Honolulu HI - - Tonopah NV 1.9 1.1

Houghton MI 20.3 3 Topeka KS 5 2.4

Houston TX 0 0.1 Traverse City MI 15.4 3.1

Huntington WV 3.9 2.1 Tucson AZ 0.1 0.1

Huntsville AL 0.5 0.6 Tucumcari NM - -

Huron SD 12.3 4.2 Tulsa OK 1.8 1.4

Indianapolis IN 6 2.9 Victoria TX 0 0.1

International 

Falls

MN 22.4 5.1 Waco TX 0.2 0.2

Jackson MS 0.2 0.3 Waterloo IA 11.4 4.2

Jacksonville FL 0 0.1 West Palm 

Beach

FL 0 0

Kahului HI - - Wichita Falls TX 1 0.9

Kalispell MT - - Wichita KS 3.6 2.1

Kansas City MO 5.3 2.6 Wilkes-barre PA 8.3 2.8

Key West FL - - Williamsport PA 6.9 2.5

King Salmon AK - - Wilmington DE 3.5 2.2

Knoxville TN 1.5 1.1 Wilmington NC 0.3 0.4

Kodiak AK - - Winnemucca NV 3.7 2.5

Kotzebue AK - - Worchester MA 11.6 4.1

La Crosse WI - - Yakima WA 2.6 1.5

Lake Charles LA 0 0.1 Yakutat AK - -

Youngstown OH 9.8 2.6