9 Solar generation can become a major and global source of clean energy by 2050. Nevertheless, few 10 studies have assessed its resilience to extreme events, and none have used empirical data to 11 characterize the fragility of solar panels. This paper develops fragility functions for rooftop and 12 ground- mounted solar panels calibrated with solar panel structural perf ormance data in the Caribbean 13 for Hurricanes Irma and Maria in 2017 and Hurricane Dorian in 2019. After estimating hurricane 14 wind fields, we follow a Bayesian approach to estimate fragility functions for rooftop and ground- 15 mounted panels based on observati ons supplemented with existing numerical studies on solar panel 16 vulnerability. Next, we apply the developed fragility functions to assess failure rates due to hurricane 17 hazards in Miami -Dade, Florida, highlighting that panels perform below the code require ments, 18 especially rooftop panels. We also illustrate that strength increases can improve the panels' structural 19 performance effectively. However, strength increases by a factor of two still cannot meet the 20 reliability stated in the code. Our results advocate reducing existing panel vulnerabilities to enhance 21 resilience but also acknowledge that other strategies, e.g., using storage or deploying other generation 22 sources, will likely be needed for energy security during storms . 23 Keywords: solar panels, fragili ty functions, hurricane hazards, Bayesian update, structural 24 reliability 25 1. INTRODUCTION 26 As the world transitions towards cleaner energy sources, the power system infrastructure is rapidly 27 changing. In 2019, installations of solar generators accounted for 40% of the electric generating capacity 28 installed in the United States (Perea et al., 2019) . Market and government projections state that solar 29 generation will be 20– 30% of the global electricity by 2050 (Shah & Booream -Phelps, 2015; Sivaram & 30 Kann, 2016; Solaun & Cerdá, 2019; The International Renewable Energy Agency, 2018) . As a result, the 31 resilience of the power system infrastructure is also changing. First, t he design standards or the level of 32 exposure of solar energy generating infrastructure can differ from current generation infrastructure. For 33 example, engineers design nuclear plant s or dams with risk category IV for safety in nuclear and 34 hydroelectric generation, source of 20% and 7% electricity generation in the United States (U.S. Energy 35 Information Administration, 2021) . This category provides the highest structural reliability le vels in the 36 ASCE7 -16 design code since failure “ could pose a substantial hazard to the community” (American Society 37 of Civil Engineers, 2017) . In contrast, engineers can design solar panels following conventional reliability 38 levels for rooftops, i.e., risk category II. Engineers can design them with even lower levels, i.e., risk category 39 I, if the solar installation structural failure “represents low risk to human life in the event of failure” as for 40 large ground- mounted installations in remote locations (Cain et al., 2015) . Moreover, by design, the solar 41 generators themselves must be placed outdoors and are directly exposed to extreme loads su ch as high 42 winds. This exposure level is markedly different from existing generating units typically within protective 43 infrastructure . For example, natural gas, source of 40% of the electricity in the United States (U.S. Energy 44 Information Administration, 2021) , is transported in pipes underground and is processed in power plants 45 with several key equipment within buildings , protecting them from winds . As solar generation becomes a 46 key source of our energy production, we need a better understanding of its resilience to natural hazards and 47 its ability to provide sufficient and reliable power during extreme load conditions. 48 Fragility functions describe the likelihood of damage (or failure) due to an extreme load, e.g., earthquake 49 shaking, hurricane wind. The development of fragility functions for energy generation components is 50 essential to understand the risk profile of power systems (Bennett et al., 2021; Winkler et al., 2010; Zhai et 51 al., 2021) . However, lack of data has prevented the assessment of panel vulnerability to extreme loads, 52 hindering our ability to understand the resilience of future power grids . Due to the lack of solar panel failure 53 data or appropriate experimental tests, Goodman (2015) used simplified numerical structural assessment to 54 propose the first solar panel fragility functions . The analysis focused on yielding onset o f rooftop panel 55 racks due to high wind loads. D ue to the lack of better models, its fragility function has also been applied 56 to ground- mounted solar panels (Bennett et al., 2021; Watson, 2018) . To the best of the authors’ knowledge, 57 data-driven assessments of solar panel vulnerability have not been c onducted. 58 In this paper, we fill this research gap by analyzing a novel dataset of solar panel structural performance in 59 60 sites in the Caribbean after the 2017 and 2019 hurricane seasons . This dataset captures these storms’ 60 severe impact on renewable infrastructure, especially in Puerto Rico (Kwasinski, 2018) . We use th is dataset 61 to propose the first data -driven fragility curves for both rooftop and ground- mounted solar panels. Through 62 a Bayesian approach, we supplement this empirical datas et with numerically driven fragility functions by 63 Goodman (2015) . Combining these different information sources results in more robust estimates of 64 fragility function parameters than those based on either observation or numerical simulation. We present 65 an algorithm based on Metropolis -Hastings (MH) Monte Carlo Markov Chain (MCMC) to solve the 66 Bayesian update with computational efficiency. Additionally, the Bayesian approach explicitly 67 characterizes the uncertainty in the fragility functions’ parameters, w hich is critical to account for the 68 uncertainty of key risk metrics, e.g., panels’ annual rate of failure. 69 Next, this paper shows an application of the developed fragility functions by assessing the structural 70 reliability of solar panels in Miami- Dade, Florida, to hurricanes. Our assessment combines our new fragility 71 functions and hurricane hazard modeling for mainland United States (Marsooli et al., 2019) . Finally, this 72 paper explores the value of increasing solar panel strength to, for example, assess its effectiveness in 73 reducing annual failure rates and meet different ASCE7 -16 standards for structural reliability. This paper 74 contributes to the body of literature on the risk of modern power systems to extreme events by providing 75 the fi rst data -inform ed fragility functions for solar panels and a holistic assessment of their reliability to 76 hurricanes. 77 2. SOLAR PANEL STRUCTURAL PERFORMANCE DATA 78 2.1. Panel damage data 79 Our dataset is an extended version of the “Solar Under Storm” reports’ panel failure dataset (Burgess et al., 80 2020; Burgess & Goodman, 2018) . The initial dataset consists of 26 sites primarily located in residential 81 buildings in Puerto Rico for rooftop panels. “Solar Under Storm” focus es on reporting main failure 82 mechanisms in rooftop installations with qualitative descriptions of failure modes in the Caribbean after the 83 large hurricanes Irma and Maria in 2017 and Dorian in 2019. The study reports frequent failures in racks 84 and the clips that attach the panel to the racks (Burgess et al., 2020) . Unlike Goodman (2015) , which covers 85 the early serviceability damage state, i.e., yielding onset in racks, the identified damage conditions in the 86 dataset introduce a damage state of structural collapse (Figure 1) . 87 (a) Residential rooftop panels (b) Large ground -mounted panels Figure 1. Example of solar panel damage in dataset. (a) Rooftop panels: Clip failures in the bolt connection between panels and racks (red arrows) lead to panel uplift (see bolts in circle with zoom -in view). Clamp failures (see rectangle with zoom -in view) lead to blown racks (see red line where a rack used to be placed) (Burgess et al., 2020) . (b) Large ground- mounted panels: Satellite imagery shows the scale of the wind damage in comparison to the pre -hurricane view in the rectangle (National Oceanographic and Atmospheric Administration, 2021) . In large -scale failures, multiple failure modes were found, including debris impact from damaged panel ar rays. Because the “Solar under Storm” dataset focuses on failed rooftop panels, we extended the dataset to cover 88 panels that survived the hurricanes. The data extension is critical to properly fit fragility functions with data 89 representing various panels’ structural performance . By surveying local engineers in Puerto Rico, we 90 extended the dataset to 46 sites. Supplementary Table 1 shows the list of the rooftop solar panels, their 91 geographical coordinates, and their failure mode , e.g., Figure 1 a. Out of the 46 sites, 46% experienced clip 92 (clamp) failures, 17 % racking failures, 4% roof attachment failures, and 50% either rack or connection or 93 roof attachment failure. Most panels unde rwent damage due to debris impact (65% in the initial dataset). It 94 is important to note that debris failure was primarily part of a cascading mechanism with projectiles 95 originating from the damaged panels themselves. Figure 2a shows a map with all the panel installation sites, 96 indicating clip, racking, or attachment failures. The plot also shows that Hurricane Irma, Maria, and 97 Dorian’s tracks were near the sites. 98 For ground- mounted solar panels, we surveyed reports and newspapers to determine panels’ failures in 99 large sites. Utility -scale solar installations are primarily ground -mounted, each one composed of hundreds 100 or thousands of panels. Thus, their failures ofte n have media coverage. We visually verified the installation 101 damage with high- resolution satellite imagery from the National Oceanic and Atmospheric Administration 102 (NOAA) (National Oceanographic and Atmospheric Administration, 2021) and Google Earth Satellite 103 Imagery. We obtained information for 14 large panel installations with 13 MW of capacity on average in 104 the Caribbean for Hurricanes Irma and Maria in 2017. The “Solar Under Storm” study also surveyed a few 105 of these installations , but it did not report the installations’ geographical coordinates to preserve the 106 confidentiality of the sites (Burgess & Goodman, 2018) . Supplementary Table 2 shows the list of these 107 ground- mounted solar panels, their geographical coordinates, capacit y, and the percentage of failed panels 108 (see site failure in Figure 1b). 36% of sites reported significant failures in more than 50% of their panels, 109 including the Humacao Solar Farm with 40 MW of capacity, the second largest solar farm in Puerto Rico 110 (Institute for Energy Research, 2018) . Figure 2b shows installations indicating the sites with significant 111 failures, i.e., more than 50% of failed panels. The reported failures included clip (clamp) failures, racking 112 fractures and buckling, bolt shear failure, and bolt loosening (Burgess & Goodman, 2018) . We observed 113 evidence of cascading structural failures triggered by debris from damaged panels in large sites, suggesting 114 that damage in a few panels can progress quickly to massive failur es. This observation is consistent with 115 the cascading failures of clip (T -clamps) fractures found in the more detailed post -hurricane structural 116 inspections (Burgess & Goodman, 2018) . 117 (a) Residential rooftop panels (b) Large ground -mounted panels Figure 2. Solar panel sites in collected dataset after Hurricanes Irma and Maria in 2017 and Hurricane Dorian in 2019. The lines indicate the hurricane tracks, and the panels with failures (clip, racking, rooftop attachment) and without failures are highlighted in the map . Failure in the panel array is defined as either clip, racking, or roof attac hment (in case of rooftop panels) failures in more than 50% of the panels. In Puerto Rico, where 50% and 59% of the inspected rooftop and ground- mounted panels were located , 118 wind design levels range from 63 to 72 m/s and from 57 to 76 m/s for structures with r isk categor ies I and 119 II, respectively (American Society of Civil Engineers, 2017) . As mentioned earlier, the ASCE7- 16 requires 120 solar panels on residential buildings to be designed with a risk category of II. Ground- mounted solar panels 121 can be designed with a risk category I since they “represent low risk to human life in the event of failure”. 122 While the structural design levels for ground- mounted solar panels are lower, our described findings 123 reported fewer sites with large failures than rooftop panels (50% versus 60%). For further assessment, we 124 analyzed the wind conditions that the panels experienced. 125 2.2 Wind conditions 126 We obtained the hurricanes’ tracks, their radii of maximum wind, and maximum winds from the revised 127 HURDAT2 Atlantic hurricane database (Landsea & Franklin, 2013) . We estimated axisymmetric winds 128 circulating counterclockwise based on a tropical cyclone wind profile model (Chavas et al., 2015) . We then 129 combined these circulating winds with the estimated backg round winds (Lin et al., 2012) to calculate the 130 resulting asymmetric wind fields for each hurricane. For smoothness, we interpolated HURDAT2 3-hour 131 timesteps and thus the corresponding wind fields to obtain maximum wind at each panel site for every 10 132 minutes (Supplementary Figure 1). 133 For evaluation, we compared the resulting wind estimates to the hourly wind records from the NOAA 134 National Centers for Environmental Information (2001) ’ Global Integrated Surface Dataset during 135 Hurricane Maria from the weather station at the San Juan International Airport in P uerto Rico ( Figure 3). 136 No other stations reported wind data from Puerto Rico for the event. Unfortunately, wind data were not 137 gathered for the most intense period; nevertheless, data during and before August 20th, 2017, show that our 138 wind estimates and records closely follow each other. During August 20th, both datasets showed rapid wind 139 intensification, at least up to the ~30 m/s, when records stopped. Our estimates indicate that winds peaked 140 at 60 m/s on August 20th, 2017. 141 Figure 3. Comparison of wind estimates from Chavas et al. (2015) , (C15), and the wind records from NOAA National Centers for Environmental Information (2001) , (NCEI), at the San Juan International Airport. Using a multiplicative factor from an empirical formula (Vickery & Skerlj, 2005) , we converted the 1 -m 142 sustained wind estimates at the panel sites to 3 -second gusts to be compatible with the wind load metrics 143 for structural design (American Society of Civil Engineers (ASCE), 2017) . Failures in rooftop panels were 144 caused by gusts start ing at 73 m/s, with a mean in all sites of 81 m/s ( Figure 4a). Failures in ground- mounted 145 panels were caused by gusts starting at 83 m/s, with a mean of 91 m/s (Figure 4b) . The solar panel dataset 146 is suitable for assessing fragility functions as it contains ranges of gusts where failure occurrence has large 147 variability (Figure 4). For example, between 70 and 90 m/s, several sites with rooftop panels experienced 148 both failure and no failure . Getting data in this range is critical for fragility functions to appropriately 149 capture the uncertainties in panel failure when transitioning from low winds to high winds. 150 (a) Rooftop panel (b) Ground -mounted panel Figure 4. 3-s gust distributions for panels with (black) and without (gray) damage. The data are shown as points and the empirical probability density functions are estimated using a Gaussian kernel 3. BAYESIAN FRAMEWORK FOR FRAGILITY FUNCTION UPDATES 151 3.1. Fragility func tion 152 Fragility functions with lognormal shape are typically used to model infrastructure’s damage due to wind 153 hazards and multiple other extreme loads (Ellingwood et al., 2004; Shinozuka et al., 2000; Straub & der 154 Kiureghian, 2008) . Its shape is given by 155 𝑞𝑞(𝑤𝑤;𝜐𝜐,𝛽𝛽)=Φ�ln (𝑤𝑤)−ln (𝜐𝜐) 𝛽𝛽� (1) where 𝑞𝑞 is the probability of panel failure due to a wind gust 𝑤𝑤 , 𝜐𝜐 is the wind gust with a failure probability 156 of 50%, 𝛽𝛽 is a normalizing factor, and Φ is the cumulative standard normal distribution function. 𝛽𝛽 defines 157 the width of the transition range between winds with low and high failure probability, and it is a measure 158 of aleatory uncertainty in the vulnerability anal ysis. For example, a 𝛽𝛽 value of 0 would be equivalent to a 159 deterministic assessment, where the panel would fail after a given wind threshold. 160 We follow a Bayesian approach to fit solar panels’ fragility functions due to two key factors. 161 • The Bayesian formulation can represent f ragility functions’ significant epistemic uncertainties 162 through random fragility function parameters , υ and 𝛽𝛽. Treating υ and 𝛽𝛽 as random variables rather 163 than deterministic parameters allows for the propagation of their uncertainty to solar panel damage 164 predictions in risk analysis . 165 • The Bayesian approach allows for the combination of multiple sources of information to improve 166 the fragility function characterization. The dataset presented in this paper provides the opportunity 167 for a data- driven, probabili stic description of panel failure . However, the number of samples is not 168 high, e.g., 46 and 14 for rooftop and ground- mounted panels, respectively. Thus, t hrough the 169 Bayesian approach, we use Goodman (2015) ’s numerical assessment as prior information and then 170 combine it with the dataset for the final fragility function estimates. 171 In the Bayesian formulation, the posterior distribution 𝑝𝑝(𝜐𝜐,𝛽𝛽|𝑥𝑥) after combining both data sources is 172 𝑝𝑝(𝜐𝜐,𝛽𝛽|𝑥𝑥)=𝑝𝑝(𝑥𝑥|𝜐𝜐,𝛽𝛽)𝑝𝑝(υ,β) ∫∫𝑝𝑝(𝑥𝑥|𝜐𝜐,𝛽𝛽)𝑝𝑝(υ,β)dυdβ (2) where 𝑥𝑥={𝑥𝑥1,𝑥𝑥2,…,𝑥𝑥𝑛𝑛} is the vector containing the failure information at each site, thus 𝑥𝑥 𝑛𝑛∈{0,1} 173 equals zero if the panel did not fail and one if it failed , and 𝑛𝑛 is the number of sites, i .e., equal to 46 and 14 174 for rooftop and ground- mounted panels, respectively. The limit state for rooftop panel failure is defined as 175 extensive damage, including clip, racking, or roof attachment failures. Hereafter, we refer to this damage 176 state as panel failure. The limit state for failure in the large ground- mounted panels is defined as extensive 177 damage, including clip and racking failures, in more than 50% of their panels . 𝑝𝑝(𝑥𝑥|𝜐𝜐,𝛽𝛽) is the likelihood 178 function of observing the dataset for fixed values of 𝜐𝜐 and 𝛽𝛽, and 𝑝𝑝(υ,β) is the prior distribution of 𝜐𝜐 and 179 𝛽𝛽. 180 3.2. Prior 181 As in the Bayesian approach , 𝜐𝜐 and 𝛽𝛽 from Equation (2) are random variables rather than deterministic 182 values . Additionally, 𝜐𝜐 and 𝛽𝛽 can only be positive numbers. Accordingly, we use lognormal distributions 183 to model the prior. T he probability density functions ( pdfs) of 𝑝𝑝(𝜐𝜐) and 𝑝𝑝(𝛽𝛽) are given by 184 𝑝𝑝(𝜐𝜐)=1 𝜐𝜐𝜎𝜎𝜐𝜐√2𝜋𝜋exp�−(𝑙𝑙𝑛𝑛𝜐𝜐−𝜇𝜇𝑣𝑣)2 2𝜎𝜎𝜐𝜐2� (3) 𝑝𝑝(𝛽𝛽)=1 𝛽𝛽𝜎𝜎𝛽𝛽√2𝜋𝜋exp�−�𝑙𝑙𝑛𝑛𝛽𝛽−𝜇𝜇𝛽𝛽�2 2𝜎𝜎𝛽𝛽2� (4) where 𝜇𝜇𝜐𝜐 and 𝜎𝜎𝜐𝜐 are hyperparameters defining the logarithmic mean and standard deviation of 𝜐𝜐. 𝜇𝜇𝛽𝛽 and 185 𝜎𝜎𝛽𝛽 are hyperparameters defining the logarithmic mean and standard deviation of 𝛽𝛽. For simplicity, we 186 assume 𝜐𝜐 and 𝛽𝛽 are independent. Thus 187 𝑝𝑝(𝜐𝜐,𝛽𝛽)=𝑝𝑝(𝜐𝜐)𝑝𝑝(𝛽𝛽) (5) For Bayesian assessments, the data supporting the prior distribution need to be independent of the data used 188 for the parameter update. Thus, the selection of Goodman (2015) ’s fragility function is appropriate for this 189 study. The numerical assessment is based on code- conforming rooftop panels designed for wind conditions 190 in Atlanta, Georgia. The uncertainty in the fragility function stems from the stochastic velocity pressure 191 induced by winds acting on the panel. It also models stochasticity in material strength and construction 192 quality. Goodman (2015) ’s study is frequentist ; thus, the parameters defining the f ragility function in 193 Equation ( 1) are deterministic. The resulting fragility functions had a deterministic υ, gust for 50% -failure 194 probability, of 60 m/s and β of 0.13 for a panel on a 30o roof. 195 To use these numerical evaluations as a prior distribution, we adjusted their wind design conditions to the 196 Carib bean . Taking San Juan, Puerto Rico, as a reference, we scaled up 𝜐𝜐 to represent a local solar panel 197 design using the ratio between the wind design values in San Juan and Atlanta. We consider a design with 198 risk category II (wind with a return period of 700 years) for rooftop panels and a risk category I (wind with 199 a return period of 300 years) for the ground -mounted panels (Cain et al., 2015) . As a result, we used 𝜐𝜐 equal 200 to 85 m/s for rooftop panels and 81 m/s for ground- mounted pane ls. 201 We use the values of 85 m/s and 8 1 m/s to find the prior logarithmic means of 𝜐𝜐 (𝜇𝜇𝜐𝜐) for the rooftop and 202 ground- mounted solar panels, respectively, since they are equal to the prior medians (𝑒𝑒𝜇𝜇𝜐𝜐) in the lognormal 203 distributions . For the logarithmic means of 𝛽𝛽 (𝜇𝜇𝛽𝛽), we use Goodman (2015) ’s value of 0.13 for both panel 204 types. The logarithmic standard deviations ( 𝜎𝜎𝜐𝜐 and 𝜎𝜎𝛽𝛽) are a measure of epistemic uncertainty as data can 205 reduce them. We consider the results from Goodman (2015) ’s numerical assessment to be initial sound 206 data. Thus, we use it as an informative prior rather than using weakly informative or non- informative prior 207 (Gelman et al., 2013) . Accordingly, we set 𝜎𝜎𝜐𝜐 and 𝜎𝜎𝛽𝛽 equal to 0.5. This value is similar to other Bayesian 208 assessments for vulnerability curves (Noh et al., 2017) , and it account s for the lack of information (e.g., 209 actual material strength or failure modes) in the numerical study in reproducing panel failure. 210 3.3. Likelihood of observing the data 211 Panel failure follows a Bernoulli distribution with probability 𝑞𝑞 that is a function of the wind. Considering 212 that the failures at different 𝑛𝑛 sites are independent, then the likelihood of observing failures or non- failures 213 in 𝑛𝑛 sites is given by 214 𝑝𝑝(𝑥𝑥|𝜐𝜐,𝛽𝛽)=�𝑞𝑞𝑥𝑥𝑖𝑖 (1−𝑞𝑞)1− 𝑥𝑥𝑖𝑖𝑛𝑛 𝑖𝑖=1 (6) where 𝑥𝑥𝑖𝑖 is one if the panel failed at the site or zero otherwise and 𝑞𝑞 is found from the fragility function in 215 Equation (1) with parameters 𝜐𝜐 and 𝛽𝛽. 216 3.4. Posterior distribution 217 According to the Bayes rule for conditional probabilities, the posterior 𝑝𝑝(𝜐𝜐,𝛽𝛽|𝑥𝑥) can be found in Equation 218 (2). The numerator is the product of the likelihood of observing the panel failures and the prior distribution. 219 The denominator is the integral of this product through the entire parameter space of υ and β. Equations (5) 220 and (6) allow us to find the numerator in closed form , but the denominator requires a complex integration 221 that cannot be solved analytically . 222 3.5. Solving for the posterior distribution using MCMC 223 To ove rcome the challenge stemming from numerical integration, we followed an approach based on 224 MCMC (Liu, 2004) . MCMC only requires evaluating a proportional function to the posterior distribution 225 rather than the posterior itself . Thus, we can find samples from the posterior and circumvent the evaluation 226 of the integral with MCMC since 227 𝑝𝑝(𝜐𝜐,𝛽𝛽|𝑥𝑥)∝𝑝𝑝(𝑥𝑥|𝜐𝜐,𝛽𝛽)𝑝𝑝(𝜐𝜐,𝛽𝛽) (7) We use the Metropolis -Hastings (MH) MCMC algorithm to define a Markov Chain (MC) that sample s 228 from the posterior distributions of 𝜐𝜐 and 𝛽𝛽. With the MH algorithm, we define the MC as a random walk 229 through the parameter space of 𝜐𝜐 and 𝛽𝛽. To generate 𝑚𝑚-th sample pair (𝜐𝜐𝑚𝑚,𝛽𝛽𝑚𝑚) of the posterior, we sample 230 a candidate (𝜐𝜐∗,𝛽𝛽∗) using th e following uncorrelated bivariate normal distribution 231 (𝜐𝜐∗,𝛽𝛽∗)∼𝑁𝑁(𝝁𝝁(𝑹𝑹𝑹𝑹 ),𝝈𝝈(𝑹𝑹𝑹𝑹 )) (8) where 𝝁𝝁(𝑹𝑹𝑹𝑹 ) is the mean vector of the random walk, and it is equal to the last posterior sample 232 (𝜐𝜐𝑚𝑚−1,𝛽𝛽𝑚𝑚−1). 𝝈𝝈(𝑹𝑹𝑹𝑹 ) is the covariance matrix , equal to the diagonal matrix 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑�𝜎𝜎𝜐𝜐 (𝑅𝑅𝑅𝑅),𝜎𝜎𝛽𝛽 (𝑅𝑅𝑅𝑅)�. 𝜎𝜎𝜐𝜐 (𝑅𝑅𝑅𝑅) 233 and 𝜎𝜎𝛽𝛽 (𝑅𝑅𝑅𝑅) are calibrated values for an effective exploration of the high -probability regions, i.e., good 234 mixing . For this symmetrical random walk, the sample candidate (𝜐𝜐∗,𝛽𝛽∗) is accepted with probability 235 min (1,𝐴𝐴), where 236 𝐴𝐴=𝑝𝑝(𝑥𝑥|𝜐𝜐∗,𝛽𝛽∗)𝑝𝑝(𝜐𝜐∗,𝛽𝛽∗) 𝑝𝑝(𝑥𝑥|𝜐𝜐𝑚𝑚−1,𝛽𝛽𝑚𝑚−1)𝑝𝑝(𝜐𝜐𝑚𝑚−1,𝛽𝛽𝑚𝑚−1) (9) According to the MH properties, the MC has a stationary distribution, i.e., the resulting distribution when 237 the number of samples is sufficiently large, equal to the posterior distribution of υ and β in Equation (2). 238 This algorithm was implemented to assess the posterior of the fragility function parameters for both rooftop 239 and ground- mounted panels. We ensured a good mixing by calibrating 𝜎𝜎𝜐𝜐 (𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀) and 𝜎𝜎𝛽𝛽 (𝑅𝑅𝑅𝑅) such that the 240 average acceptance rate is around 25% as recommended in the literature (Chib & Greenberg, 1995; Robert, 241 2014) . Using the MH MCMC analysis, we sampled 1 0,000 realizations of υ and β from the posterior 242 distribution after a burn- in period containing 1,000 realizations. We selected the burn- in period after 243 verifying the MC stationarity (Supplementary Figure 2 ). 244 4. BAYESIAN UPDATES FOR FRAGILITY FUNCTIONS 245 4.1. Rooftop panels 246 We used the generated 10,000 samples to estimate the posterior distribution of the fragility function 247 parameters . For 𝜐𝜐, the median varied from 85 m/s in the prior to 80 m/s in the posterior, its standard 248 deviation from 51 m/s to 5 m/s, and its logarithmic standard deviation from 0.5 to 0.07 (Figure 5a). The 249 similar prior and posterior medians show that the numerical analysis in Goodman (2015) is consistent with 250 the observations of wind in terms of the 50%-failure probability. The significant decrease (90%) in the 251 standard deviation reveals the importance of the solar panel dataset in decreasing the initial epistemic 252 uncertainties of 𝜐𝜐. 253 For 𝛽𝛽 , the median varied from 0.13 in the prior to 0.32 in the posterior, its standard deviation from 0.08 to 254 0.11, and i ts logarithmic standard deviation from 0.5 to 0.30 ( Figure 5b). The posterior median of 𝛽𝛽 is 255 almost three times the prior value. Such an increase reveals the inconsistency of the numerical analysis in 256 Goodman (2015) with the empirical data in terms of the aleatory uncertainty measured by 𝛽𝛽. The numerical 257 analysis implies that the transition range between winds with low and high failure probabilit ies is narrow . 258 Conversely, previous empirical evidence (Roueche et al., 2017, 2018) suggests that the 𝛽𝛽 value of 0.13 is 259 too small to characterize the uncertainty in wind hazards , implying a wider transition range between winds 260 with low and high failure probabilit ies. This observation demonstrates the importance of empirical data to 261 calibrate numerical analysis. 262 We found a lack of correlation between 𝜐𝜐 and 𝛽𝛽 in the posterior as the Pearson’s coefficient between the ir 263 posterior samples was only 3x10−4. This result suggests independence between 𝜐𝜐 and 𝛽𝛽, as assumed in the 264 prior . 265 The Bayesian update from the parameters’ prior distribution to the posterior distribution brings important 266 implications for the fragility function of rooftop solar panels. The mean fragility function , describing the 267 probability of panel failure, for the posterior distribution can be found as 268 𝐸𝐸[𝑞𝑞(𝑤𝑤)]=��𝑞𝑞(𝑤𝑤;𝜐𝜐,𝛽𝛽)∞ 0∞ 0𝑝𝑝(𝜐𝜐,𝛽𝛽|𝑥𝑥)𝑑𝑑𝜐𝜐𝑑𝑑𝛽𝛽 (10) Equation (10) uses the posterior 𝑝𝑝 (𝜐𝜐,𝛽𝛽|𝑥𝑥) as the distribution of υ and β to find the posterior of 𝐸𝐸 [𝑞𝑞(𝑤𝑤)]. 269 Replacing 𝑝𝑝(𝜐𝜐,𝛽𝛽|𝑥𝑥) by the prior 𝑝𝑝(𝜐𝜐,𝛽𝛽) will result in the prior 𝐸𝐸[𝑞𝑞(𝑤𝑤)]. 270 a) 𝜐𝜐 (rooftop panel ) b) 𝛽𝛽 (rooftop panel ) c) 𝜐𝜐 (ground -mounted panel ) d) 𝛽𝛽 (ground -mounted panel ) Figure 5. The p rior and posterior distribution of 𝜐𝜐 and 𝛽𝛽 for rooftop solar panels. Samples from the posterior distribution were used to depict the histogram , and Gaussian kernel was used to develop each empirical pdf. We solved Equation (10) by averaging all 𝑞𝑞 values for the suite of 10,000 fragility functions, obtained by 271 evaluating the 10,000 samples of 𝜐𝜐 and 𝛽𝛽 (Figure 6a). With this procedure, we incorporate and propagate 272 the uncertainty in 𝜐𝜐 and 𝛽𝛽 to the fragility function. The deterministic prior distribution in Goodman (2015) 273 was used to set up the prior medians’ hyperparameters. However, the resulting mean fragility function 274 (𝐸𝐸[𝑞𝑞(𝑤𝑤)]) from the Bayesian prior is different than its frequentist counterpart due to its parameters’ 275 uncertain nature . The difference is negligible f or the wind with a 50%-failure probability (~85m/s for both) . 276 Yet, it is significant for the wind with a 10% and 90% -failure probability (71 versus 43 and 100 versus 167 277 m/s). The wider wind range in the transition from a 10% to a 90%-failure probability in the Bayesian 278 assessment results from the uncertainty propagation from 𝜐𝜐 and 𝛽𝛽 (Figure 5a and 5b’s grey curves ) to the 279 fragility functi on. 280 The posterior distribution changes the wind for 50% -failure probability only slightly ( -5%), from 86 m/s in 281 the prior to 80 m/s in the posterior. T he wind range that transitions from a 10% to a 90% -failure probability, 282 52 and 123 m/s, respectively, has a width that is 56% smaller than the prior. This reduction results from the 283 lower uncertainty on 𝜐𝜐 , whose standard deviation decrease s from 51 m/s in the prior to 5 m/s in the posterior 284 (Figure 5a). Moreover , the posterior fragility function shows a significantly narrower confidence interval 285 than the prior fragility function. These results demonstrate the importance of the Bayesian approach to 286 capture and reduce large initial uncertainties through empi rical data , not only in the fragility function 287 parameters ( 𝜐𝜐 and 𝛽𝛽), but also in the mean fragility function itself . 288 a) Rooftop panel b) Ground -mounted panel Figure 6. Fragility function s for random samples 𝜐𝜐 and 𝛽𝛽 according to their prior and posterior distributions. The solid thicker lines indicate the expectation of the failure probability over the parameters’ distribution, and the dashed lines indicate the mean plus and minus a standard deviation. Goodman* is th e deterministic fragility function adapted from Goodman (2015) . 4.2. Ground -mounted panels 289 The distribution of 𝜐𝜐 shows that the median varies from 81 m/s in the prior to 9 0 m/s in the posterior, its 290 standard deviation from 50 m/s to 6 m/s, and its logarithmic standard deviation from 0.5 to 0.07 (Figure 291 5c). The posterior s hows a significant reduction in the uncertainty of 𝜐𝜐, with a standard deviation 87% lower 292 than that of the prior . Such a reduction is very close to the one found in rooftop solar panels, even though 293 the number of data points is one -third of the latter. 294 For 𝛽𝛽, the median varies from 0.13 in the prior to 0.15 in the posterior, its standard deviation remains in 295 0.07, a nd its logarithmic standard deviation from 0.5 to 0.39 (Figure 5d). As a result, the posterior 296 distribution exhibits a slight shift to the right . The little variations in 𝛽𝛽 's standard deviation and logarithmic 297 standard deviation suggest that the number of data points is insufficient to substantially reduce uncertainty. 298 Following the same procedure for the rooftop panels, we estimated the mean fragility function (𝐸𝐸[𝑞𝑞(𝑤𝑤)]) 299 for ground- mounted solar panels (Figure 6b ). Unlike the posterior fragility function for rooftop panels, t he 300 posterior fragility function for ground- mounted panels has a higher wind value (+10%) for a 50% -failure 301 probability than its prior, 90 m/s versus 81m/s. This increase suggests that the panel in stallations for ground- 302 mounted solar panels were structurally sounder than for rooftop panels , whose wind for 50% -failure 303 probability in the posterior was 5% less than in the prior. This better structural performance may result from 304 more code enforcement, better member and connection installation (e.g., avoiding loose bolts), or proper 305 inspections (Burgess et al., 2020; Burgess & Goodman, 2018) . These panels are part of large installations 306 with massive investments from utility companies, which, unlike residential homes that install rooftop 307 panels, often have a budget for appropriate quality and control. 308 We found that the wind range that transitions from a 10% to 90% failure probability in the posterior, 73 309 and 116 m/s, i s reduced in 64% from the prior, 41 m/s and 160 m/s. This narrower range is driven mainly 310 by the lower standard deviation in 𝜐𝜐 (Figure 5c). Th is reduction in the transition range is larger than that in 311 the case of the rooftop panels ( Figure 6) because, unlike the rooftop panels, the ground- mounted panels’ 312 posterior of 𝛽𝛽 did not have a larger standard deviation than the prior . Furthermore , the posterior fragility 313 function shows a much narrower confidence interval than the prior fragility function. However, the 314 confidence interval is slightly wider than in rooftop panels because the ground -mounted panel dataset is 315 only a third of the rooftop panel dataset . 316 5. PANEL’S ANNUAL FAILURE RATE 317 To illustrate their application, w e use our fragility functions to assess solar panel risk for hurricane winds 318 for Miami -Dade, Florida, as a case study . Miami -Dade is exposed to similar wind hazards in Puerto Rico. 319 For example, the risk category II design wind (700- year return period) in San Juan, Puerto Rico, is 71 m/s 320 (159 mph), whereas the design wind in Miami -Dade is 73 m/s (164 mph). Different standards for solar 321 panel installation and code enforcement might be in place in Miami -Dade, especially for rooftop panels , 322 which perform ed worse than ground- mounted panels. However, more data collection efforts will be needed 323 to confirm whether panels in mainland United States have fundamentally different structural behavior than 324 those in the Caribbean. Du e to the lack of these datasets, here we use our fragility functions from the 325 Caribbean to study solar panels’ reliability and resilience performance in Miami -Dade; analysis for other 326 regions can be similarly performed. 327 A study site in the mainland United States is chosen to leverage a synthetic hurricane database with 5018 328 landfalling storms in the United States generated from a statistical- deterministic tropical cyclone (TC) 329 model (Marsooli et al., 2019) . These synthetic hurricanes account for current climate conditions (from 1980 330 to 2005 ) according to the National Center for Environmental Prediction (NCEP) reanalysis. The 5018 331 synthetic storms correspond to ~1485 years of storm simulation. The model that generate s these storms 332 consist s of three stages: a genesis model; a beta- advection TC motion model; and a dynamical TC model 333 that captures how environmental factors inf luence TC development (Emanuel et al., 2008) . The model 334 solves the synthetic storms’ tracks, maximum sustained winds, and radii of maximum winds, and we use 335 its results at 2-hour intervals. We estimated the wind fields by combining the storm’s axisymmetr ic winds 336 circulating counterclockwise (Chavas et al., 2015) and background wind s (Lin et al., 2012) . The synthetic 337 hurricanes were evaluated with observations by Marsooli et al. (2019) . 338 We determine the annual rate of panel failure λ 𝑓𝑓 by combining the wind simulations with the Bayesian 339 fragility functions. The rate defines the average number of events leading to panel failures in a given year 340 assuming a Poisson process. In a frequentist analysis, the fragility function parameters υ and β are fixed. 341 Thus, λ𝑓𝑓(υ,β) can be estimated as 342 λ𝑓𝑓(υ,β)=�𝑞𝑞(𝑤𝑤;υ,β)𝑑𝑑λw∞ 0 (11) where λ𝑤𝑤 is the annual exceedance probability o f wind speed . It is the average number of events that result 343 in winds exceeding a threshold 𝑤𝑤 in a given year under a Poisson process of storm arrivals , and it can be 344 estimated from the synthetic storms. In our Bayesian framework, υ and β are random variables . Thus, λ𝑓𝑓 is 345 also a random variable. Accordingly, its probability density function p𝜆𝜆𝑓𝑓(λ) can be found as 346 p𝜆𝜆𝑓𝑓(λ)=��𝑝𝑝(𝜐𝜐,𝛽𝛽|𝑥𝑥)𝛿𝛿�𝜆𝜆−�𝑞𝑞(𝑤𝑤;υ,β)𝑑𝑑λ𝑤𝑤∞ 0�𝑑𝑑𝜐𝜐𝑑𝑑𝛽𝛽∞ 0∞ 0 (12) where 𝛿𝛿() is the Dirac delta function on 𝜆𝜆−∫𝑞𝑞(𝑤𝑤;υ,β)𝑑𝑑λw∞ 0. Equation ( 12) uses the posterior 𝑝𝑝(𝜐𝜐,𝛽𝛽|𝑥𝑥) 347 as the distribution of υ and β to find the posterior of p𝜆𝜆𝑓𝑓(λ). Replacing 𝑝𝑝 (𝜐𝜐,𝛽𝛽|𝑥𝑥) by the prior 𝑝𝑝(𝜐𝜐,𝛽𝛽) will 348 result in the prior p𝜆𝜆𝑓𝑓(λ). The expected value of λ𝑓𝑓, E[λ𝑓𝑓], can be found as: 349 E[λ𝑓𝑓]=����𝑞𝑞(𝑤𝑤;υ,β)𝑑𝑑λw∞ 0�𝑝𝑝(𝜐𝜐,𝛽𝛽|𝑥𝑥)𝑑𝑑𝜐𝜐𝑑𝑑𝛽𝛽∞ 0∞ 0 (13) Explicitly evaluating E[λ𝑓𝑓] and particularly p𝜆𝜆𝑓𝑓(λ) is computationally challenging by traditional numerical 350 integration. Thus, we used Monte Carlo analysis due to its simplicity to find such estimates. Using the 351 10,000 Monte Carlo samples of prior and posterior fragility functions, we estimated the prior a nd posterior 352 of λ𝑓𝑓 (Figure 7 ). 353 a) Rooftop panel b) Ground -mounted panel Figure 7. Probability density function p𝜆𝜆𝑓𝑓(λ) of the annual probability of failure rate of solar panels. Samples from the Monte Carlo simulations were used to fit empirical pdfs with a Gaussian kernel. Our results indicate a marked decrease in uncertainty for λ𝑓𝑓 in the posterior. The posterior standard 354 deviation and logarithmic standard deviation for rooft op panels are 1.2×10−2/yr and 6.3×10−1 , 355 whereas the priors’ ones are 5. 1×10−2/yr and 1.82. The posterior standard deviation and logarithmic 356 standard deviation for ground -mounted panels are 1.7×10−3/yr and 5.5×10−1, whereas the priors’ ones 357 are 5.7×10−2/yr and 1.87. This uncertainty decrease in the annual failure rate is consistent with the 358 observed posterior fragility function uncertainty reductions for rooftop and ground- mounted panels ( Figure 359 6). 360 For rooftop panels, the posterior E[λ𝑓𝑓] is 1.3×10−2/𝑦𝑦𝑦𝑦, i.e., return period of 75 years. U nder the 361 assumption of a Poisson process, this rate results in a 48% probability of failure in 50 years . This rate is 362 equivalent to a 33% failure probability in 30 years, often considered the usable panel service time . The 363 reliability index, defined as the inverse of the cumulative standard normal distribu tion function on the 364 survival probability, i.e., one minus the failure probability, in 50 years, is 0.04. This reliability is 365 significantly lower than the current standards in the ASCE7 -16. Using results from a recent study 366 (McAllister et al., 2018) , we estimated that a structure designed for wind s with a 700-year return period 367 (risk category II) should have a reliability index of 2.3 in 50 years , i.e., failure rate of 2. 3×10−4/𝑦𝑦𝑦𝑦. Thus, 368 our findings show that the structural reliability of rooftop solar panels in our dataset was significantly below 369 current code standards if similar panels are adopted in Miami -Dade . These results are consistent with the 370 observed structural deficienc ies in the installation and design of panels with failures in the dataset , e.g., 371 insufficient connection strength, lack of vibration- resistant connections (Burgess et al., 2020) . Thus, 372 significant gains in reliability could be achieved by increasing qual ity and control during design and 373 installation. 374 For ground- mounted panels, the posterior E[λ𝑓𝑓] is 2.0×10−3/𝑦𝑦𝑦𝑦, i.e., return period of 504 years. This rate 375 is equivalent to a 9% and a 6% probability of failure in 50 and 30 years , respectively . The reliability index 376 for 50 years is 1.3 . According to the ASCE7- 16, the reliability index for a structure designed for winds with 377 a 300-year return period (risk category I) is 1.9, i.e., failure rate of 6.1×10−4/𝑦𝑦𝑦𝑦 (McAllister et al., 2018) . 378 Thus, our results indicate that ground- mounted panels also have lower reliability than required by the 379 current code standards. These results are also consistent with previously reported structural deficiencies in 380 ground- mounted panels in the Caribbean, e.g., undersized racks, and undersized or under -torqued bolts 381 (Burgess & Goodman, 2018) . Nevertheless, the contrast between rooftop and ground- mounted panel 382 performance indicates that the latter had a significantly higher structural reliability than the former . 383 6. STRONGER SOLAR PANEL S FOR GENERATION RESILIENCE 384 385 6.1. Assessing structural reliability and generation in stronger panels 386 We assessed panels’ strength increases by factors of 1.25, 1.50, 1.75, and 2.0. This wide range of strength 387 increases accounts for addressi ng various panel installations and design deficiencies reported in the 388 Caribbean. Existing studies already point to cost -effective solutions to correct these deficiencies, e.g., 389 torque checks on bolts, well -designed clips (Burgess et al., 2020; Burgess & G oodman, 2018) . 390 This range also covers increases in strength for critical infrastructure. Hospitals and fire stations require 391 that their buildings’ structural and non -structural components have higher strength for continuous 392 operations in a disaster emer gency response. Accordingly, solar panels serving these facilities must be 393 designed with a risk category IV , higher than for panels on residential ( risk category II) or utility -scale (risk 394 category I) installations . For example, the wind design in Miami -Dade is 69 m/s (154 mph) for a r isk 395 category I and 81 m/s (182 mph) for a r isk category IV. The difference represents a strength factor of 1.40 396 as the design force is proportional to the square of the design wind . 397 For our assessment , we multiplied the posterior samples of 𝜐𝜐 by the square root of the strength increase 398 factors, i.e., 1.12, 1.22, 1.32, 1.41. We let samples 𝛽𝛽 remain the same to limit the increase in uncertainty, 399 i.e., the transition from low -failure -probability to high- failure -probability winds . The resulting fragility 400 functions are shifted to the right of the posterior functions in Figure 6, reducing the likelihood of panel 401 failure (Figure 8). For example, the mean failure probability 𝑞𝑞 when rooftop panels undergo gusts of 60 402 m/s decreases from 0. 19 to 0.12, 0. 08, 0.05, and 0.04 for the strength factors of 1.25, 1.50, 1.75, and 2 .0, 403 respectively. Similarly, the mean 𝑞𝑞 when ground- mounted panels undergo gusts of 80 m/s decreases from 404 0.23 to 0.09, 0.04, 0.02 , and 0.01. 405 406 407 a) Rooftop panel b) Ground -mounted panel Figure 8. Mean fragility functions for panels with increases in strength. The factors that multiply each 𝜐𝜐 sample are equal to the square root of the strength factors in the labels. The dashed curves indicate the wind annual exceedance rates. The x -axis represents 3 -s gusts. Using Monte Carlo sampling , we estimated pλ𝑓𝑓(λ) for the different increases in strength (Figure 9). 408 Expectedly, increases in strength shift pλ𝑓𝑓(λ) to the left as they reduce the resulting annual rate of failure. 409 We also found E[λ𝑓𝑓] and assessed the corresponding panels’ structural reliability ( Table 1). The increases 410 in strength are effective at decreasing E[λ𝑓𝑓]. The strength factor of two reduces E[λ𝑓𝑓] by a factor of 3.9 and 411 2.5 for rooftop and ground- mounted panels , respectively . A more modest strength factor of 1.25 also 412 effectively decreases panel failure risk, reducing E [λ𝑓𝑓] by ~50% and ~70% for rooftop and ground- mounted 413 panels , respectively. Nevertheless, our results indicate that the reliability indexes for these stronger panels 414 are still below the ASCE7 -10 targets even for a risk category I, i.e., 1.9. 415 Table 1. Annual probability of panel failure and reliability indexes (for 50 years) for different increases in strength Strength Factor Rooftop panel Ground -mounted panels E[λ𝑓𝑓] (1/yr) Reliability index E[λ𝑓𝑓] (1/𝑦𝑦𝑦𝑦) Reliability index 1.0 0.0132 0.04 0.0020 1.30 1.25 0.0089 0.36 0.0012 1.58 1.50 0.0061 0.63 0.0010 1.66 1.75 0.0043 0.87 0.0009 1.73 2.0 0.0034 1.01 0.0008 1.77 These results highlight large structural vulnerabilities in solar panels since they do not reach code -level 416 reliability even if their strength is increased twice. These results suggest that existing lack of structural 417 design and limited inspections in the panel installations were significant (Burgess et al., 2020; Burgess & 418 Goodman, 2018) . High vulnerability to hurricane winds has been noted previously in building s. For 419 example, a previous study in Southern Florida determined that roof -to-wall connections with 3- 8d toenails 420 in wooden residential buildings have an annual failure rate between 0.005 -0.024 (Li & Ellingwood, 2006) . 421 These rates are com parable to the rooftop panels in our case study and below the performance of ground- 422 mounted panels (Table 1). Furthermore , roof panels with 6d nails @ 6/12 in . on these buildings showed 423 even poorer performance , with higher annual failure rates of 0.077- 0.137. 424 425 a) Rooftop panel b) Ground -mounted panel Figure 9. Probability density function of the annual failure rate of solar panels for different increases in panel strength. The labels indicate the strength factor increase. 426 6.2 Will stronger panels increase generation resilience? 427 428 As demonstrated previously, increasing panel strength will increase its reliability. However, other critical 429 factors also play a significant role in solar generation resilience, i.e., the ability to generate sufficient 430 electricity during storms . First, sol ar generation can decrease even if panels remain structurally sound and 431 functional during a hurricane . Ceferino et al. (2021) demonstrated that hurricane cloud s can reduce 432 irradiance and generation significantly through light absorption and reflection. For example, a category -4 433 hurricane can decrease the generation by more than 70%, even if the panels remain undamaged. Cloud- 434 driven generation losses can last for days, although they will bounce back to normal conditions in an 435 undamaged panel as the hurricane leaves. 436 Failure of supporting infrastructure can also decrease generation resilience even if panels withstand extreme 437 wind loads. Increasing the strength of rooftop panels on vulnerable roofs will not increase the global 438 reliability of the residential energy system. Global reliability must consider that panels can fail in a 439 cascading failure triggered by roof uplift, damaging the panel or its connections. The weakest link wil l 440 control the reliability of this in- series system. As mentioned previously, roof -to-wall connections with 3 - 441 8d toe nails or roof panels with 6d nails @ 6/12” exhibit ed similar or poorer performance than vulnerable 442 rooftop panels (Li & Ellingwood, 2006) . Strengthening panels on the se roofs will substantially increase 443 their local reliability (Table 1), but it will increase global reliability only negligibly. Conversely, roofs with 444 H2.5 hurricane clips in roof -to-wall connections and 8d nails @ 6/12'' in roof panels will make roofs an 445 appropriate supporting system through higher reliability (Li & Ellingwood, 2006) . Thus , our results 446 advocate for stronger panels but under a holistic assessment of global reliability. 447 Structurally sound rooftop panels have the intrinsic advantage of delivering power even if the primary grid 448 is down. When inverters are within buildings, occupants can use their locally generated energy during an 449 outage (Cook et al., 2020) . Access to power can be vital for residential buildings, especially if heatwaves 450 following storms increase the demand for cooling (Feng et al., 2021) . Access to energy is also pivotal to 451 sustaining emergency response operations for critical infrastructure such as hospitals or fire stations. 452 Communities can further utilize locally generated energy through energy sharing and microgrids to increase 453 households’ access to power after a disaster , even for those who did not instal l panels (Ceferino et al., 2020; 454 Patel et al., 2021) . Nevertheless, solar panels will not replace the need for backup generation units for 455 resilience, especially for critical facilities, and fully charged behind -the-meter batteries must complement 456 them for power access during an emergency response. 457 Stronger panels will also increase power security at the utility level by avoiding massive structural failures 458 at the generation sites, as in Figure 1b. As noted previously, solar panels are directly exposed to wind. Poor 459 structural performance in utility companies’ solar installations could result in significant generation losses 460 and outage s that can affect the disaster emergency response and recovery activities. Recently, Hurricane 461 Ida caused damage to the power system that resulted in ~1M outages in Louisiana, reducing electricity 462 access by more than 60% in more than ten parishes (counties ), critically affecting the functionality of the 463 water system and delaying recovery (J. D. Goodman et al., 2021; Prevatt et al., 2021) . While solar 464 generation losses could be potentially offset by other generating sources during an emergency response, 465 adopting vulnerable panels in our grid will be a missed opportunity to make our power systems resilient . 466 Solar is projected to be an important generat ion source in our future grids. Simultaneously, hurricanes are 467 projected to be more intense in the future climate (Knutson et al., 2020) . Governments invest massively to 468 redesign the grid and transition to cleaner energy (The International Renewable E nergy Agency, 2018) . 469 Thus, our results advocate for governments to leverage this unique opportunity to change the grid’s risk 470 trajectory course by strengthen ing the infrastructure that will provi de our future communities with energy 471 safety . 472 7. CONCLUSIONS 473 This paper presented the first data- driven fragility curves for solar panels under hurricane wind loads. The 474 article estimated the fragility curves using data on the structural performance of 46 rooftop panels in 475 residential buildings and 14 large ground- mounted solar panel arrays in utility generation sites. Solar panel 476 failure data was collected after Hurricanes Maria and Irma in 2017 and Hurricane Dorian in 2019 in the 477 Caribbean. Further, this paper assessed solar generation resilience and its improvemen ts with stronger 478 panels. 479 We used a Bayesian approach to supplement the panel dataset with an existing numerical assessment of 480 panel failure. Using a Markov Chain Monte Carlo algorithm, we estimated the posterior distributions of 481 fragility param eters for the rooftop and ground- mounted panels separately. Our results show significant 482 reductions in epistemic uncertainty for 𝜐𝜐 (wind for a 50%-failure probability) in rooftop and ground- 483 mounted panels with 90% and 87% decreases in the standard deviation. Using Monte Carlo, we then 484 propagated the uncertainty in the parameters to the fragility functions, showing significantly narrower 485 confidence intervals. This result highlighted the importance of characteriz ing fragility functions with 486 ground- truth data . 487 We combined our fragility functions with a hurricane hazard assessment in Miami -Dade, Florida, using 488 Monte Carlo simulations. Miami- Dade has similar hurricane hazards to Puerto Rico, where most damage 489 data was collected. Our estimates of the annual rate of p anel structural failure indicated that the panels are 490 below the current structural reliability standards specified in ASCE7 -16. These performance deficiencies 491 were particularly striking for rooftop panels (estimated failure rate of 1.3×10−2/𝑦𝑦𝑦𝑦 versus 2.3×10−4/𝑦𝑦𝑦𝑦 492 in the code ), whose documented installation issues and frequent lack of structural design made them 493 particularly vulnerable to high winds . 494 Finally, we analyzed the implications of building stronger solar panels by up to a factor of two due to 495 improvements in the panels’ installations, structural design, or higher structural requirements. We show 496 that increasing panel strength effectively reduces the annual failure rate. However, even the factor of two 497 is still insufficient to meet annual failure rates in the ASCE7 -10 (reliability index of 1.9 for the lowest risk 498 category) for rooftop and ground- mounted panels (reliability indexes of 1.01 and 1.77). 499 As we transition towards cleaner energy sources and solar generation becomes an essential component of 500 our grid, ensuring its resilience is critical for our communities. Our paper argues that increasing panels’ 501 structural strength has critical implications for enhancing generation resilience during extreme storms. In 502 the context of growing hurricane hazards due to climate change, panels must at least meet existing code 503 structural performance standards. However, we also discuss that generation losses might arise even if 504 panels can sustain high wind speeds. Thus, we point out different plans, such as using backup power, 505 behind- the-meter storage, or sharing energy, to address such losses during hurricane emergencies in order 506 to sustain a proper response to hurr icanes. 507 8. AKNOWLEDGMENTS 508 We thank Sanya Detweiler from the Clinton Foundation, Chris Needham and Solar Frank Oudheusden from 509 FCX Solar, and Christopher Burgess from the Rocky Mountain Institute for sharing their reports, photos, 510 and relevant documentation on solar panels’ structural performance after Hurricanes Maria a nd Irma in 511 2017 and Dorian in 2019. We also acknowledge the financial support by the Andlinger Center for Energy 512 and the Environment at Princeton University through the Distinguished Postdoctoral Fellowship. 513 Additionally, this research was also supported by the NSF Grant 1652448. The authors are grateful for their 514 generous support. 515 9. SUPPLEMENTARY INFORMATION 516 Supplementary Tables 1 and 2 a nd Supplementary Figure 1 to 4 can be found a t: 517 https://tinyurl.com/mw224py5 518 10.