My main research interests are:

• Predicting and mitigating the effects of severe weather on electric grids and other infrastructure.
• Developing storm impact models to forecast power outages on the electric distribution network.
• Assessing grid resilience improvements performed by electric distribution companies.
• Developing tools for storm response and power outage restoration.
• Performing Ground Validation (GV) of Global Precipitation Measurement (GPM) instruments through collaboration with NASA, with a particular focus on wintry precipitation.
• Studying precipitation microphysics.
• Developing wildfire ignition models.
• Addressing environmental justice concerns.
• Researching renewable energy integration.

Papers published in Peer-Reviewed Journals:

Filipiak, B.C., Astitha, M. and Cerrai, D., 2025. Assessing dynamic and thermodynamic variability in initial and boundary conditions for snowstorm prediction in the Northeast United States. Journal of Geophysical Research: Atmospheres, 130(19), p.e2025JD044240.

Prevezianos, A., Emmanouil, S., Watson, P.L., Zhang, X., Cerrai, D., Pasqualini, D. and Anagnostou, E.N., 2025. A data-driven identification scheme for winter weather events: Integrating historical storm reports and atmospheric reanalysis. Journal of Hydrometeorology, 26(10), pp.1353-1379.

Filipiak, B.C., Wolff, D.B., Spaulding, A., Tokay, A., Helms, C.N., Loftus, A.M., Chibisov, A.V., Schirtzinger, C., Boulanger, M.J., Pabla, C.S. and Bliven, L., 2025. Winter precipitation measurements in new England: Results from the global precipitation measurement ground validation campaign in Connecticut. Earth System Science Data Discussions, 2025, pp.1-45.

Nyame, S., Taylor, W.O., Hughes, W., Hong, M., Koukoula, M., Yang, F., Spaulding, A., Luo, X., Maslennikov, S. and Cerrai, D., 2024. Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages. IEEE Access, 13, pp. 42-55, doi: 10.1109/ACCESS.2024.3523415.

Khaira, U., Cerrai, D., Thompson, G. and Astitha, M., 2024. Integrating physics-based WRF atmospheric variables and machine learning algorithms to predict snowfall accumulation in Northeast United States. Journal of Hydrology, 644, p.132113. doi: 10.1016/j.jhydrol.2024.132113.

Hughes, W., Watson, P.L., Cerrai, D., Zhang, X., Bagtzoglou, A., Zhang, W. and Anagnostou, E., 2024. Assessing grid hardening strategies to improve power system performance during storms using a hybrid mechanistic-machine learning outage prediction model. Reliability Engineering & System Safety, 248, p.110169. doi: 10.1016/j.ress.2024.110169.

Jahan, I., Cerrai, D. and Astitha, M., 2024. Storm gust prediction with the integration of machine learning algorithms and WRF model variables for the Northeast United States. Artificial Intelligence for the Earth Systems, 3(3), p.e230047. doi: 10.1175/AIES-D-23-0047.1.

Wedagedara, H., Witharana, C., Fahey, R., Cerrai, D., Parent, J. and Perera, A.S., 2024. Non-Parametric Machine Learning Modeling of Tree-Caused Power Outage Risk to Overhead Distribution Powerlines. Applied Sciences, 14(12), p.4991. doi: 10.3390/app14124991.

Hughes, W., Nyame S., Taylor W.O., Spaulding A., Hong M., Luo X., Maslennikov S., Cerrai D., Anagnostou E.N., and Zhang W., 2024. A Probabilistic Method for Integrating Physics-Based and Data-Driven Storm Outage Prediction Models for Power Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 10, no. 2: 04024021. doi: 10.1061/AJRUA6.RUENG-1171.

Watson, P.L., Hughes, W., Cerrai, D., Zhang, W., Bagtzoglou, A. and Anagnostou, E., 2024. Integrating Structural Vulnerability Analysis and Data-Driven Machine Learning to Evaluate Storm Impacts on The Power Grid. IEEE Access, 12, pp.63568-63583. doi: 10.1109/ACCESS.2024.3396414

King, F., Pettersen, C., Bliven, L.F., Cerrai, D., Chibisov, A., Cooper, S.J., L’Ecuyer, T., Kulie, M.S., Leskinen, M., Mateling, M. and McMurdie, L., 2024. A comprehensive Northern Hemisphere particle microphysics data set from the precipitation imaging package. Earth and Space Science, 11(5), p.e2024EA003538. doi: 10.1029/2024EA003538.

Sahin, B., Udeh, K., Wanik, D.W. and Cerrai, D., 2024. Predicting Energy Demand Using Machine Learning: Exploring Temporal and Weather-Related Patterns, Variations, and Impacts. IEEE Access, 12, pp.31824-31840. doi: ACCESS.2024.3370442.

Taylor, W.O., Cerrai, D., Wanik, D., Koukoula, M. and Anagnostou, E.N., 2023. Community power outage prediction modeling for the Eastern United States. Energy Reports, 10, pp.4148-4169. doi: 10.1016/j.egyr.2023.10.073.

Yang, F., Koukoula, M., Emmanouil, S., Cerrai, D. and Anagnostou, E.N., 2023. Assessing the power grid vulnerability to extreme weather events based on long-term atmospheric reanalysis. Stochastic Environmental Research and Risk Assessment, 37(11), pp.4291-4306. doi: 10.1007/s00477-023-02508-y.

Wedagedara, H., Witharana, C., Fahey, R., Cerrai, D., Joshi, D. and Parent, J., 2023. Modeling the impact of local environmental variables on tree-related power outages along distribution powerlines. Electric Power Systems Research, 221, p.109486. doi: 10.1016/j.epsr.2023.109486.

Taylor, W.O., Nyame, S., Hughes, W., Koukoula, M., Yang, F., Cerrai, D. and Anagnostou, E.N., 2023. Machine learning evaluation of storm-related transmission outage factors and risk. Sustainable Energy, Grids and Networks, 34, p.101016. doi: 10.1016/j.segan.2023.101016.

Hughes, W., Zhang, W., Cerrai, D., Bagtzoglou, A., Wanik, D. and Anagnostou, E., 2022. A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation. Reliability Engineering & System Safety, p.108628. doi: 10.1016/j.ress.2022.108628.

Taylor, W.O., Watson, P.L., Cerrai, D. and Anagnostou, E.N., 2022. Dynamic modeling of the effects of vegetation management on weather-related power outages. Electric Power Systems Research, 207, p.107840. doi: 10.1016/j.epsr.2022.107840.

Taylor, W.O., Watson, P.L., Cerrai, D. and Anagnostou, E.N., 2022. A statistical framework for evaluating the effectiveness of vegetation management in reducing power outages caused during storms in distribution networks. Sustainability, 14(2), p.904. doi: 10.3390/su14020904.

Yang, F., Cerrai, D. and Anagnostou, E.N., 2021. The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling. Forecasting, 3(3), pp.501-516. doi:10.3390/forecast3030031.

Capecchi, V., Antonini, A., Benedetti, R., Fibbi, L., Melani, S., Rovai, L., Ricchi, A. and Cerrai, D., 2021. Assimilating X-and S-band Radar Data for a Heavy Precipitation Event in Italy. Water, 13(13), p.1727. doi: 10.3390/w13131727

Taylor, W.O., Anagnostou, M.N., Cerrai, D. and Anagnostou, E.N., 2020: Machine Learning Methods to Approximate Rainfall and Wind From Acoustic Underwater Measurements (February 2020). IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3007557.

Watson P., Cerrai, D., Koukoula M., Wanik, D.W. and Anagnostou, E.N., 2020: A Weather-Related Power Outage Model with a Growing Domain: Structure, Performance, and Generalizability. The Journal of Engineering, 10, 817-826, doi: 10.1049/joe.2019.1274.

Cerrai, D., Yang, Q., Shen, X., Koukoula, M. and Anagnostou E.N., 2020: Brief communication: Hurricane Dorian: automated near-real-time mapping of the“unprecedented” flooding on the Bahamas using SAR. Natural Hazards and Earth System Sciences 20, 1463-1468, doi: 10.5194/nhess-20-1463-2020.

Alpay, B.A., Wanik, D.W., Watson, P., Cerrai, D., Liang, G. and Anagnostou E.N., 2020: Dynamic Modeling of Power Outages Caused by Thunderstorms. Forecasting, 2(2), pp.151-162. doi: 10.3390/forecast2020008.

Yang, F., Wanik, D.W., Cerrai, D., Bhuiyan, M.A.E. and Anagnostou, E.N., 2020: Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration. Sustainability, 12 (4), p.1525, doi: 10.3390/su12041525.

Cerrai, D., Koukoula, M., Watson, P. and Anagnostou, E.N., 2020: Outage prediction models for snow and ice storms. Sustainable Energy, Grids and Networks, 21, p.100294, doi: 10.1016/j.segan.2019.100294.

Cerrai, D., Watson, P. and Anagnostou, E.N., 2019: Assessing the effects of a vegetation management standard on distribution grid outage rates. Electric Power Systems Research 175, 105909, doi: 10.1016/j.epsr.2019.105909.

Cerrai, D., Wanik, D.W., Bhuiyan, M.A.E., Zhang, X., Yang, J. and Anagnostou, E.N., 2019: Predicting Storm Outages through New Representations of Weather and Vegetation. IEEE Access, 7, 29639-29654, doi:10.1109/ACCESS.2019.2902558.

Cioni, G., Cerrai, D. and Klocke, D., 2018: Investigating the predictability of a Mediterranean Tropical-like Cyclone using a storm-resolving model. Q. J. Royal Meteorol. Soc. 144 (714), 1598-1610, doi: 10.1002/qj.3322.

Wanik, D.W., Anagnostou, E.N., Astitha, M., Hartman, B.M., Lackmann, G.M., Yang, J., Cerrai, D., He, J. and Frediani, M.E., 2018: A Case Study on Power Outage Impacts from Future Hurricane Sandy Scenarios, J. Appl. Meteor. Climatol., 57 (1), 51-79, doi: 10.1175/JAMC-D-16-0408.1.

Miglietta, M. M., Cerrai, D., Laviola, S., Cattani, E. and Levizzani, V., 2017: Potential vorticity patterns in Mediterranean "hurricanes", Geophys. Res. Lett., 44, 2537-2545, doi:10.1002/2017GL072670.

UConn to Host 57th North American Power Symposium

Dr. Cerrai served as general co-chair for the 57th North American Power Symposium, held October 26–28 in Hartford, CT. The event was a notable success, drawing 400 attendees and receiving 300 research paper submissions from across the field. In addition to leading the organization of this major scholarly gathering, Prof. Cerrai also oversaw the selection and presentation of the student paper awards, underscoring his commitment to supporting emerging researchers in power and energy systems.

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Diego Cerrai Wins NSF CAREER Award for Advancements in Power Outage, Restoration Modeling

Dr. Cerrai has received a National Science Foundation (NSF) Faculty Early Career Development Program grant (CAREER) Award. This award will support Cerrai’s research aimed at deepening our understanding of snow and ice accretion on infrastructure. This understanding will enable the development of models capable of forecasting its occurrence, and accurately predicting power outages and their restoration during winter storms.