Introduction

During a major volcanic eruption, powerful volcanoes may launch sulfur gas high into the stratosphere, where it can form long-lasting aerosols persisting on time scales from months to years. These clouds of aerosols then diffuse into a layer that can cover the globe. Studies have shown that this process can result in a severe reduction of surface solar irradiance and reduce clear sky irradiance by as much as 13 percent (Robock 2000), with significant implications for solar PV production [Robock 2000, Bluth 1992, Hay and Darby 1984]. 

Obviously, the threat of these months to years-long volcano-induced periods of low solar irradiance presents a major risk to solar investor profitability and regional power grid reliability. However, these risks aren’t typically considered when investors and planners calculate expected production variability from new (or existing) utility-scale solar sites–primarily because conventional risk assessment approaches rely on a limited sample of historical irradiance data that coincidentally corresponds to a time period (beginning in 1998) without any of the major volcanic events that have affected North America.

A Limited Historical Record of Irradiance Data 

Due to the dearth of high-quality ground station solar irradiation measurements, the solar industry has come to rely on remote sensing data from geostationary satellite missions. Irradiance data from geostationary satellites is available starting in 1998. The resulting tradeoff between data quality and data breadth caused by this exclusive reliance on satellite data for production risk assessment evinces itself as a major gap in understanding just how low solar production can be if and when the next planetary scale volcanic eruption occurs. 

To put volcanic eruption risks to solar production into context, we estimate (as shown below) that volcanic aerosols can produce annual solar irradiance deficits 5 standard deviations below the satellite record estimate. In other words, using data from 1998-2023 to estimate an annual solar irradiance probability distribution at a solar site (as is commonly done today), a major volcanic eruption could lead to a year of observed solar irradiance 5 standard deviations lower than the mean expected value. Naïvely, a 5-sigma deviation is extremely unlikely: for example, if we assumed that annual irradiance was normally distributed, a 5-sigma event should happen on average 1 in 3.5 million years.

Yet we know that these risks are much more common–two eruption events occurred between 1982 and 1991–meaning that deriving a measure of irradiance variability using only the 1998-forward satellite record probably underestimates volcano risk. The industry, in effect, may be prioritizing short timescale accuracy at the expense of long-term risk assessment.

El Chichón and Pinatubo

The last fifty years have seen two eruptions with large scale persistent impacts to irradiance. The eruption of El Chichón in 1982 accelerated roughly 7 million tons of sulfur dioxide to atmospheric heights of over 22 kilometers. The eruption of Mt. Pinatubo a mere 9 years later resulted in a 35 km high plume of 20 million tons of sulfur dioxide (Bluth et al. 1992). Both plumes became entrained in the atmosphere, reacted with water vapor, and spread out to form reflective bands of sulphuric acid aerosols that encircled the globe within roughly three weeks and persisted for up to 3 years after their respective eruptions.¹

Although satellite data is not available before 1998, the Mauna Loa observatory in Hawaii captured the impact of these global aerosol blankets on solar irradiance through measurements of the apparent transmission of the atmosphere during hours with clear skies. The data, presented in Figure 1, shows clear sky irradiance dropping by more than 13% due to the impact of El Chichón in 1982 and 11% due to the impact of  Pinatubo in 1991.

To understand how these reductions in irradiance impacted the solar resource in the US, we aggregated surface solar irradiance from the ERA5² dataset in the southwestern states (CA, UT, CO, NV, AZ, NM) from 1950-2023. We found that the 4 years with the lowest mean irradiance on record followed these eruptions. When compared to the period since 1998, the year following El Chichón had an irradiance deviation of -5.3 𝝈; the year following Pinatubo had an irradiance deviation of -3.8 𝝈.

10 Lowest Surface Solar Irradiance Years: ERA5 1950-2023 (Avg of CA, UT, CO, NV, AZ, NM)
Rank	Year	Mean Annual GHI (W/m2)	Mean Annual GHI 1998-2023 (W/m2)	𝝈1998-2023	Notes
1	1983	221.5	234.9	-5.3	Year after El Chichón
2	1982	225.4	234.9	-3.8	El Chichón
3	1992	225.4	234.9	-3.8	Year after Pinatubo
4	1984	227.0	234.9	-3.3	2 years after El Chichón
5	1986	227.7	234.9	-2.9
6	1981	228.6	234.9	-2.5
7	1957	229.0	234.9	-2.3
8	1991	229.6	234.9	-2.1	Pinatubo
9	1987	229.9	234.9	-2.0
10	1998	230.3	234.9	-1.8

To visualize these years within the full ERA5 date range, Figure 2 plots the time series of regional annual average solar irradiance from 1950-2023. Again, we see that the years following El Chichón and Pinatubo are the lowest, appearing as outliers compared to the 1998-forward satellite record range (consistent with the 3+ sigma measurements above).

Data Challenges and Future Risk

As seen above, volcanic eruptions can be an enduring hazard to solar production in the United States. However, because historic eruptions are not captured by the satellite record, they are often not accounted for during due diligence or planning exercises. Moreover, as the solar PV industry has not existed at scale for long enough to have meaningful first hand experience with volcanic eruption related deficits, empirical or even anecdotal evidence of their effects is rare.   

Clearly, solar production risk assessments using only historical data since 1998 are not incorporating these relatively rare but impactful events. Yet the risk of the next major eruption over the next decade may be greater than the industry realizes. Estimates of major eruption frequency (Rougier et al. 2018, Sheldrake and Caricchi 2017, Pyle 1995), though imprecise, suggest that volcanoes at least as severe as Pinatubo and El Chichón may occur with a frequency of roughly 1 in 51 years (Sheldrake and Caricchi 2017)–corresponding to a 33% chance of occurrence during a 20 year solar project life cycle.

Sunairio Irradiance Simulations Reveal Long Tails Consistent with Volcanic Events

Overcoming the limitations of a small sample set when estimating risk is a key benefit of employing stochastic simulation methods. While grounded in high-quality satellite-based irradiance data, Sunairio’s stochastic climate simulations generate a wide range of realistic weather scenarios that can extrapolate beyond the limitations of a restricted date range, using statistical and machine learning methods to infer complex spatio-temporal relationships between weather variables, locations, and timepoints to create a broad data set from which to evaluate risk. Therefore, Sunairio’s simulations are both representative of local weather down to a few kilometers and useful for characterizing variability–including extreme events.

To demonstrate how Sunairio simulations can “see” weather risk beyond a limited historical record, we simulated 1,000 scenarios of hourly weather for 2024 at a location just outside of Albuquerque, New Mexico–using satellite-based irradiance data from 1998-2023. Figure 3 plots the annual average GHI of each of our simulations compared to two distributions from ERA5: the 1998-2023 period (satellite data period), and the full 1950-2023 period of record. 

In the left plot of Figure 3 we see that the Sunairio simulations show a left skew (risk of low solar irradiance) that notably isn’t present in the 1998-2023 ERA5 distribution–but is present in the right plot of historical data since 1950, overlapping with the occurrence of major volcano years. Sunairio simulations, in other words, replicate the true tail of the historic distribution (including years with volcanic activity) which isn’t seen in the limited satellite data period.

Conclusions

In this case study we showed that:

1. Major volcanic eruptions such as El Chichón (1982) and Pinatubo (1991) can significantly reduce surface solar radiation by creating a planetary-scale band of sulphuric acid aerosols that persist in the atmosphere for years. The effect is well documented by Moana Loa station observations.
   
   
2. Traditional solar PV production risk estimates do not incorporate the risk of volcanic events because they derive annual risk estimates using satellite-based irradiance data (which starts in 1998).
   
   
3. The signatures of El Chichón and Pinatubo eruptions are apparent in ERA5 reanalysis data: the four lowest mean annual irradiance years (between 1950 and 2023) are either eruption years or years immediately following those eruptions.
   
   
4. Using the 1998-2023 satellite data period as the basis for risk estimates, volcanic eruption years appear to be 3- to 5-sigma events (i.e. extraordinarily unlikely)–contradicting the research on volcanic eruption frequency.
   
   
5. Sunairio’s simulations replicate low-irradiance years at a frequency consistent with the historic record and reanalysis estimates. Using Sunairio simulations of Albuquerque, NM for 2024, the likelihood of a year having mean annual GHI equivalent to the 1983 El Chichón year is roughly 2.3%.

Notes

1. Not all eruptions disrupt the climate on a planetary scale. The eruption of Mt. St. Helens in 1980 and, more recently, the eruption of Hunga Tonga–Hunga Haʻapai in 2022 both produced large explosions but failed to propel large quantities of SO₂ into the stratosphere. Therefore, although they had catastrophic impacts on surrounding areas, the plume of aerosols were able to settle to the earth within a few weeks. ↩︎
2. Although ERA5 data is less accurate than satellite data, these errors are most extreme during cloudy conditions (Urraca 2018). Looking at states in the southwest (which have fewer cloudy hours) allows us to partially mitigate these errors. ↩︎

Citations

Bluth, G. J. S., et al. (1992) Global tracking of the SO2 clouds from the June 1991 Mount Pinatubo eruptions, Geophys. Res. Lett.

Hay, J. E. and Darby, R. (1984) El Chichón – influence on aerosol optical depth and direct, diffuse and total solar irradiances at Vancouver, B.C., Atmosphere-Ocean, 22:3, 354-368, DOI: 10.1080/07055900.1984.9649204

Pyle, D. M. (1995) Mass and Energy Budgets of explosive volcanic eruptions. Geophysical Research Letters; 22, 5 563-566.

Robock, A. (2000) Volcanic Eruptions and Climate. Reviews of Geophysics.

Rougier, J., et al. (2017) The global magnitude–frequency relationship for large explosive volcanic eruptions. Earth Planet. Sci. Lett.

Sheldrake T., and Caricchi L. (2017) Regional variability in the frequency and magnitude of large explosive volcanic eruptions. Geology; 45 (2): 111–114. doi: https://doi.org/10.1130/G38372.1

Urraca, R. et al. (2018) Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data. Solar Energy.

Sunairio is excited to announce it has been awarded a National Science Foundation (NSF) Small Business Innovation Research (SBIR) Phase I grant. The NSF SBIR program, which selects less than 14% of applying startups, funds high-impact technological research and development (R&D) across a wide range of disciplines.   

For Sunairio, the award will fund research into a new, highly scalable climate simulation engine that promises to generate climate insights for the energy sector at 1,000 times the resolution of traditional, physics-based global climate models, such as the ones which support the body of research conducted by the Intergovernmental Panel on Climate Change (IPCC). 

Sunairio’s new climate simulation solution is a novel combination of generative AI and advanced statistics that early research shows can replicate forward-looking local (3-km x 3-km) hourly weather patterns and climate trends for up to 15 years. This approach will fill a technical and commercial gap because the traditional global climate models are very computationally intense and, therefore, only run at 100-km x 100-km, which isn’t sufficient to accurately model site-specific weather risk at wind and solar farms. 

Read the full press release here.

Sunairio co-hosted a webinar with Yes Energy that demonstrated how to build actionable price models for term power markets that allow you to generate price distributions which incorporate weather volatility and renewables capacity scenarios.

REQUEST ACCESS TO THE WEBINAR

With ERCOT reporting almost one-third (30GW) of non-renewable generation capacity offline as of November 12th, it’s clear that we’re in the height of fall outage season. Accordingly, let’s take a look at generation outage trends, patterns, and the risk that extreme cold this winter will force generators offline.

For this blog post we’re going to focus on outages of non-renewable generation capacity, which could also be thought of as controllable–or thermal–generation capacity. In other words, we’re not focusing on outages of wind and solar resources (which we’ll tackle in a separate piece).

The Seasonality and Variability of Generation Outages

Figure 1 plots maximum daily RTO load and average daily generation outages in ERCOT for each year since 2017.  Both are seasonal, with generation outages increasing during the Spring and Fall shoulder periods of lowest load. Spring generation outages tend to be higher than Fall outages, even though spring loads are slightly higher–-likely because generation owners want to ensure that maintenance and repairs are completed before the high-demand (and potentially high-priced) Summer season. 

Variability around the seasonal trend is also very apparent, with Spring and Fall outages varying between 20 and 30 GW and even Summer outages exhibiting a range (from the lowest observed outage levels to the highest observed levels) of at least 5 GW–more than enough to tip ERCOT pricing from moderate to extreme levels in a typical Summer season.

Beyond the normal variability, a couple of generation outage events stand out as outliers–in particular February 2021 (light purple) and August/September 2017 (dark purple). As winter is coming, let’s take a closer look at the February 2021 event and see if we can infer the likelihood of a similar event occurring again over the next few months. 

How Cold Does it Need to Get in Texas for Outages to Spike in Winter?

In short: quite cold for Texas. Excess February 2021 generation outages were caused by Winter Storm Uri, which in turn knocked out power for much of the state over a week-long period. Figure 2 plots the daily minimum ERCOT temperature (population-weighted) against average daily outages for December, January, and February.

We see essentially no correlation between temperature and generation outage levels–with the notable exception of extreme cold starting on February 15th, 2021 forcing approximately 10-15 GW of generation offline  (with respect to February 13th and 14th–right before the cold snap). 

Outages climbed significantly when temperatures bottomed out close to 10 degrees F and stayed elevated even after the daily lows increased, leading one to suspect the existence of a cold temperature threshold that’s likely to cause excess generation outages that may persist for days (or longer). Looking at February 2021, we estimate that this threshold is somewhere around 10 degrees F. 

Of course, while this analysis evaluates weather conditions across the entire ISO in order to infer an estimate of ISO-wide excess generation outages, the weather conditions that force generators offline are local. To make more precise cold-weather outage models, we’d want to repeat this analysis by grouping generators according to regional weather zones and then comparing local weather to regional outages.    

Will ERCOT Rule Changes Prevent Excess Cold-weather Outages From Happening Again?

We’re skeptical. The primary rule change that ERCOT instituted (at the behest of the Public Utility Commission) is a weatherization standard that will be “enforced”-without penalties. To be in compliance with the standard, generators are to:

1. Implement additional weather emergency preparation measures reasonably expected to ensure sustained operation at 95th percentile minimum average 72-hour wind chill value in the ERCOT weather study for facility’s weather zone
   
2. Create list of all cold weather critical components, review annually prior to beginning of winter, and update as necessary

(Source)

In other words, there’s still no economic penalty for an ERCOT generation resource that’s unavailable in a grid emergency. As a consequence, most thermal generators (e.g. natural gas plants) still do not have firm fuel supply arrangements with either pipelines or commodity suppliers. That means that on very cold winter days, a natural gas generator is not only subject to outages resulting from its own mechanical failures--but also from failures of its natural gas supply (pipeline capacity is maxed out, lower daily gas production due to natural gas wells freezing). 

How Can Sunairio Quantify the Likelihood of Extreme-Cold-Induced Outages this Winter?

We can use Sunairio weather simulations to find out the odds of breaching the 10 F daily low temperature threshold we identified above. We execute the following query to get simulations from Snowflake:

select
    convert_timezone('Etc/UTC', 'US/Central', sim_datetime) as sim_datetime,
    sim_number,
    sim_value*1.8 + 32 as sim_value -- Converting C to F
from
    ercot_weather_sims
where
    location = 'rto'
    and variable = 'temp_2m'
    and convert_timezone('Etc/UTC', 'US/Central', sim_datetime) 
        between '2023-12-01 00:00' and '2024-02-29 23:59'

By downloading this with the Snowflake connector in Python, you can make the Pandas DataFrame a timeseries of simulations with a simple pivot:

cur = conn.cursor()
cur.execute(query)
temp_sims_df = cur.fetch_pandas_all().pivot(index='SIM_DATETIME', columns='SIM_NUMBER', values='SIM_VALUE')
cur.close()

Then, with the resulting 1,000 hourly simulations of ERCOT population weighted hourly temperature for Dec 1, 2023 through Feb 29, 2024 we can calculate the probability distribution of minimum daily low temperatures for each simulation path (in python, for example):

temp_sims_df.resample('d').min().min().hist()

This gives us the histogram in Figure 3. The height of the bars in this histogram represent the number of paths in our 1,000-path simulation with the corresponding daily minimum temperature on the the x-axis. Most of the distribution is around 22 degrees. In other words, we’d expect the coldest daily low temperature this winter to be about 22 degrees.

So what’s the likelihood of having a daily low temperature 10 degrees or lower? We just count the number of paths < 10 and divide by the total number of paths (1,000).

In  [1]: (temp_sims_df.resample('d').min().min()<10).sum() / 1000
Out [1]: 0.055

We find a 5.5% chance (according to these assumptions) of experiencing extreme cold that could result in excessive non-renewable generation outages this winter. 

Sunairio Outage Simulations Natively Account for Cold-Weather Effects

At first glance, excess generation outages similar to those caused by Winter Storm Uri may seem like outliers. However, armed with a theory (cold temperatures beyond a certain threshold cause a cascade of multi-day gas supply and gas generation problems) and forward looking simulation data (1000 weather simulation paths) we can model the chance and effect of another similar event.

At Sunairio, we model generation outages (including the effect of extreme cold weather) as a percentage of thermal energy in parallel with weather simulations-ensuring that data and analyses are not left in the dark.

Conclusions

• Non-renewable generation outages in ERCOT follow a seasonal pattern and peak in Spring, though with significant variability.
  
• On an ISO population-weighted basis, daily low temperatures need to drop to about 10 degrees Fahrenheit to cause excess generation outages.
  
• New ERCOT rule changes require power plants to attest to winter weatherization but still don’t provide a strict penalty for noncompliance or unavailability.

• The odds of a cold-weather-induced generation outage event happening during Winter 2023-2024 is about 1 in 20.
  
• Sunairio non-renewable generation outage simulations for ERCOT incorporate these dynamics.

Overview

Average 100m wind speeds in parts of the U.S. Midwest were lower than ever during May through July of this year (based on ERA5 data from 1950 to the present). For example, the plot below displays 30-day average 100m wind speeds for Clay County, MO (just outside of Kansas City) with 2023 highlighted in yellow:

Besides causing significant wind energy production and revenue shortfalls, this record low-wind period has precipitated questions throughout the wind energy industry related to the role that climate change is playing, the likelihood of similar events happening in the future, and strategies to mitigate impact.

Let’s start by looking at the trends themselves.

Annual and Seasonal Trends in 100m Wind Speed

On an annual basis, average 100m wind speeds have been declining in much of the Eastern and Midwest U.S., including wind-rich onshore areas of IL, IA, KS, NE, and MN as well as wind-rich offshore areas off the coasts of NJ, NY, CT, and MA. The following map shows wind trends across the US, with regions of statistically significant (p < 0.01) trends bounded by a black border.

Annual trends, however, don’t reveal differences throughout the year. For example, here’s a map of wind trends for January and August:

Note the strong negative trends in August throughout the northern Midwest and East Coast (including offshore) which are either not present or much less pronounced in January. In these areas, in other words, not only is It getting less windy—it is getting significantly less windy in summer.

What’s causing this?

Climate Change Impacts

At Sunairio, we get versions of this question all the time. “Is this climate change?”, “Is this going to get worse?”

What we know is that global winds experienced a pronounced decline starting around the 1980s. This “global stilling” has been less intense since 2010, though wind speeds are still below pre-1979 averages. The Intergovernmental Panel on Climate Change forecasts slowing wind speeds through 2100–at which point they predict that annual average wind speeds could drop by 10%. Unfortunately, compared to the body of knowledge on global temperature trends, there’s not as much scientific consensus on the exact causes of trends in wind speed––though two major drivers are thought to be controlling: 1) changes in large-scale atmospheric circulation and 2) an increase in “surface roughness” (e.g. forest growth, urbanization).

To visualize what’s happening more precisely, we created an animation of the probability distribution of summer wind speeds for a county in Kansas relative to 1950-1979.

This animation helps convey an important point about the relationship between climate trends–which are usually described by changes in averages over time–and the likelihood of extremes: a small shift in the center of a weather probability distribution may cause a large change in extreme event frequency. As the animation progresses, note that the likelihood of extremely calm (low-wind) periods is much more pronounced than the relatively small shift left of the overall wind speed distribution.

The Difference in Wind Power Production

With some wind power curve assumptions we can calculate how much less wind power was produced than expected during May-July 2023. As we see in the figure below, many areas with high concentrations of wind farms saw production deficits of 20%-30% translating to approximately 17,000,000 MWh less wind energy production than expected throughout the U.S.

Note that this is 20%-30% lower than expected wind power production estimates which already account for the observable, negative trends in wind speed over time (meaning that current-year expectations should generally be lower than historical averages). If we calculated the drop in wind power against a simple historical average, the deficit would be even greater.  

Financial Budgeting Implications  

Wind power production expectations are typically the most critical input in financial budgets for utility-scale wind farm owners. Traditionally, renewable energy asset owners have set production and revenue budgets at what they consider to be median (or P50) levels based on a backward-looking analysis of historical weather. 

In this context, an event such as the May-June 2023 period would be considered a loss (relative to budget)–and because the P50 expectations are usually not adjusted for climate trends, those losses would likely have been greater than the 20%-30% we estimated.

But even with budgets that account for declining wind speeds, asset owners face a second problem: how to anticipate production variability–i.e., the risk of a low wind or high wind periods. This variability is essential to estimate in order to ensure sufficient capital reserves to survive adverse events and anticipate the magnitude of cost-saving plans that might be necessary to hit annual profit targets. Traditionally, these variability assessments (also known as production risk assessments) have also been accomplished using history. As the May-July 2023 event shows, however, using a limited sample of historical wind speeds as a proxy for future wind speed risk–and not accounting for climate trends–does not work.

Grid Reliability Implications

Wind farm owners aren’t the only stakeholders who depend on wind power. Regional power grids increasingly rely on renewables for essential grid-balancing energy. Events such as the May-July 2023 low-wind period expose utilities and ISOs to significant risks because resource adequacy modeling uses historical weather data only, and this event was (by virtue of being a record) outside the modeled range of possibilities. 

Luckily, regional power grids did not suffer reliability events during this period, likely due to a range of factors–the most significant being an additional 12.5 GW of solar capacity being installed year over year (including almost 4 GW in what the EIA classifies as the “West North Central”) and a relatively mild summer in the upper Midwest.

What Does Work: Stochastic Climate Simulation

For all the issues described above, Sunairio incorporates forward-looking climate modeling to assess renewable energy production risks. Our solution is stochastic in nature, easily scalable , and done at very high temporal (hourly) and spatial (3km or less) resolutions. When we simulate 1000 outcomes of a future time period, it’s like having access to 1000 probabilistic climate-aware possibilities. 

With 1000 probabilistic outcomes of current and future-year wind speed, we can accurately quantify expected (or median/P50) wind production, and risks to that production, much more accurately (at least 4x more accurately) than is possible using historical data alone. Moreover, we can predict the likelihood of extreme weather events happening–even those that are statistically possible but not in the historical record (yet).

For example, our simulations show that the May-July 2023 event should occur at a frequency of about 1 in 83 years. Perhaps even more relevant: any 3-month period (not just May-July) with an equivalent wind production capacity deficit will occur roughly 1 in every 10 years. These are extreme events–but they will occur again, and they shouldn’t be considered unknowable risks.    

Conclusion

North American wind speeds are declining in some of the most wind-rich areas–where a significant amount of wind energy capacity is either already or soon to be installed. 

When future wind speed expectations are tied to historical averages and historical ranges as part of long-range planning exercises, these wind energy production estimates will eventually be wrong. 

A robust solution to this problem is to use forward-looking climate simulation for wind energy production risk assessment.

Sunairio is proud to join EPRI's Climate READi Affinity Group. Climate READi is the industry's collaborative approach to managing climate risk in the power sector. Sunairio will be supporting the effort by providing our technical expertise in predictive climate and energy analytics.

Solar production risk assessment is traditionally based on hypothetical power generation on Typical Meteorological Years (TMYs) or historical weather data. These approaches ignore climate trends and extrapolate from small datasets, leading to production estimates that are unreliable and drift from reality over time. Sunairio addresses these shortcomings via a system of stochastic climate simulations to create arbitrary amounts of realistic local- climate-adjusted weather data.

On 100 representative US solar sites, we first verified that the Sunairio simulation method generated realistic weather. We then found TMYs to be unrepresentative and to overpredict power generation at 85% of the sites. We found site-dependent local-climate-trend production adjustments to range from -6.16% to 2.77% of predicted production in 2022 — adjustments that grew in magnitude to -13.48% to 4.63% when extrapolated out to 2034. An overall negative GHI trend of −0.225 W/m² per year (83% of the sites had negative GHI trends) caused overall production losses of 2.43% in 2022 and 4.98% in 2034 with respect to TMY estimates. Finally, the degree of uncertainty in production estimates was at least four times lower in the Sunairio simulation data compared to production estimates using historical samples.

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Check out the latest contributors to Sunairio's tech: Nick, Natalie, Kim, Katie and Gabe--engineering students at Northwestern University who just finished their junior year. Sunairio sponsored a student project (IEMS 394) and asked this team to help us understand wind turbine icing dynamics in real life. Icing events can be catastrophic for wind farm energy production--and they're really hard to predict. But now we have a probabilistic wind turbine icing model that quantifies the risk of these events (in terms of probability of capacity factor loss) as a function of weather. All thanks to the hard work of this Northwestern University engineering student team!