Stochastic modeling of solar irradiance during hurricanes

Luis Ceferino, Ning Lin, Dazhi Xi

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


The unprecedented growth of solar generation adoption indicates that solar can become a significant source of modern and clean energy for our power grids in just a few decades. Despite solar’s growing criticality for generation, few studies have proposed models to assess solar generation during extreme natural events. In particular, hurricanes bring environmental conditions that may drastically reduce solar generation even if solar infrastructure remains fully functional. Here, we present a stochastic model to quantify irradiance decay during hurricanes. The model is developed through mixed-effect regression on a dataset that merges historical Global Horizontal Irradiance and Atlantic hurricane activity. The data showed higher irradiance decays for higher hurricane categories and closer to the hurricane center due to optically thick clouds that absorb and reflect light. Accordingly, our model describes the irradiance decay as a function of hurricane category and the distance to the hurricane center normalized by the hurricane size. We test four irradiance decay functions with varying complexities and rank their performance based on the Akaike Information Criterion. Our analysis demonstrates that the hurricane’s radius of outermost closed isobar performs best as the size metric for normalizing distance. To showcase the methodology’s applicability, we use it to generate stochastic simulations of irradiance in the Southern United States during a synthetic storm from its genesis to its dissipation. Our results show that generation in Miami-Dade, Florida, can decrease beyond 70% in large regions during a category-4 hurricane even if the solar infrastructure is undamaged. Furthermore, generation losses can also last beyond three days, and this timeframe will be exacerbated if solar panels become non-functional. Our follow-up study integrates the proposed model with panel fragility functions to offer analysis capabilities for forecasting time-varying solar generation during hurricanes.

Original languageEnglish (US)
Pages (from-to)2681-2693
Number of pages13
JournalStochastic Environmental Research and Risk Assessment
Issue number9
StatePublished - Sep 2022

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Environmental Chemistry
  • Water Science and Technology
  • Safety, Risk, Reliability and Quality
  • General Environmental Science


  • Disaster resilience
  • Hurricanes
  • Optically thick clouds
  • Solar irradiance
  • Solar panels


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