Astitha, Marina

Marina Astitha

Associate Professor, Civil and Environmental Engineering

Email marina.astitha@uconn.edu
Phone 860-486-3941
Mailing Address Civil and Environmental Engineering 261 Glenbrook Road, Unit 3037 Storrs, CT 06269-3037
Campus Storrs
Link
Google Scholar Link

Brief Bio

 

  • Deployment of a high-resolution wind prediction system for the offshore wind farm facilities in the Northeast Atlantic cluster. Marina Astitha (PI), Eversource Energy. 9/2022-9/2027.
  • Improving Extreme Weather Forecasting Capabilities in support of Power Outage Prediction Activities: Phase II – wind gust and winter weather. Marina Astitha (PI), Eversource Energy Center, 09/2023-08/2026.
  • Power system vulnerability assessment under a changing climate. Xinxuan Zhang (PI), Marina Astitha, Guiling Wang, Stergios Emmanouil (co-PIs). 09/2023-08/2025.
  • DLR-RENEW: Dynamic Line Rating Robust Validation, Enhancement and Field Demonstration in New England with Changing Weather and Offshore Wind Integration. DOE, PI: Junbo Zhao, co-PI: Marina Astitha. 11/1/23-10/31/26.
  • Offshore Wind Power Forecasting and Grid Integration, DOE NETL. PI: E. Anagnostou, co-PIs: M. Astitha, J. Zhao, G. Matheou, M. Pena Mendez. Budget: 1.625M. 12/31/2024-12/30/2025.
  • NSF IUCRC Phase I Grant, University of Connecticut, E. Anagnostou (PI UConn), Marina Astitha (senior personnel).  7/2023-6/2028.
  • NSF IUCRC WISER: “A Wind power resources for Northeast US under a changing climate”. PI: M. Astitha (UConn), co-PI: J. Freedman (UAlbany). 75K, 02/06/2025-12/31/2025.
  • Zaman, E. Gutmann, G. Wang, and M. Astitha*, 2025: “Validation of the Ensemble Generalized Analog Regression Downscaling (En-GARD) model to downscale near-surface wind speed for the Northeast US”. Journal of Hydrometeorology, https://doi.org/10.1175/JHM-D-24-0075.1.
  • Khaira, D. Cerrai, G. Thompson, M. Astitha*, 2024: “Integrating physics-based WRF atmospheric variables and machine learning algorithms to predict snowfall accumulation in Northeast United States”. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2024.132113.
  • T. Zaman, T. Juliano, P. Hawbecker, M. Astitha*, 2024: “On predicting offshore hub-height wind speed and wind power density in the Northeast US coast using high-resolution WRF model configurations during anticyclones coinciding with wind drought”.  Energies, https://doi.org/10.3390/en17112618.
  • Jahan, D. Cerrai, M. Astitha*, 2024: “Storm gust prediction with the integration of machine learning algorithms and WRF model variables for NE US”. Artif. Intell. Earth Syst. DOI: https://doi.org/10.1175/AIES-D-23-0047.1. 
  • Feng-Chang, P. Vlahos, M. Astitha*, 2024: Assessing physical and biological lake oxygen indicators using simulated environmental variables and machine learning algorithms. Environmental Modelling and Software, https://doi.org/10.1016/j.envsoft.2024.106024.
  • Feng-Chang, M. Astitha*, Y. Yuan, C. Tang, P. Vlahos, V. Garcia, U. Khaira, 2023:  A new approach to predict tributary phosphorus loads using machine learning and dynamic modeling systems. Artificial Intelligence for the Earth Systems-AIES. DOI: https://doi.org/10.1175/AIES-D-22-0049.1.
  • Khaira, M. Astitha*, 2023: “Exploring the real-time WRF forecast skill for four tropical storms, Isaias, Henri, Elsa and Irene, as they impacted the Northeast United States” Remote Sens. 2023, 15(13), 3219; https://doi.org/10.3390/rs15133219.
  • Feng-Chang, V. Garcia, P. Vlahos, C. Tang, D. Wanik, J. Yan, J. Bash, M. Astitha*, 2021: Linking multi-media modeling with machine learning to assess and predict lake chlorophyll-α concentrations. Journal of the Great Lakes Research. Volume 47, Issue 6, December 2021, Pages 1656-1670.
  • Luo, M. Astitha*, C. Hogrefe, R. Mathur, ST Rao, 2020: Evaluating Seasonality and Trends in Modeled PM2.5 Concentrations Using Empirical Mode Decomposition. Atmos. Chem. Phys., 20, 13801–13815, 2020.https://doi.org/10.5194/acp-20-13801-2020.
  • Huiying Luo, Marina Astitha*, S. Trivikrama Rao, Christian Hogrefe & Rohit Mathur (2020) Assessing the manageable portion of ground-level ozone in the contiguous United States, Journal of the Air & Waste Management Association, 2020, https://doi.org/10.1080/10962247.2020.1805375.
  • S.T. Rao*, H. Luo, M. Astitha, C. Hogrefe, V. Garcia, R. Mathur, 2020: “On the Limit to the Accuracy of Regional Air Quality Models”. Atmos. Chem. Phys., 20, 1627–1639, 2020. https://doi.org/10.5194/acp-20-1627-2020.
  • Zefan Tang, Peng Zhang*, Kunihiro Muto, Martial Sawasawa, Marissa Simonelli, Christopher Gutierrez, Jaemo Yang, Marina Astitha, Robert Manning, James Mader, 2020: “Extreme Photovoltaic Power Analytics for Electric Utilities”, IEEE Transactions On Sustainable Energy, Vol. 11, No. 1, Jan 2020, doi:10.1109/TSTE.2018.2884500.
  • Yang, J., Astitha, M.*, & Schwartz, C. S., 2019. Assessment of storm wind speed prediction using gridded Bayesian regression applied to historical events with NCAR’s real‐time ensemble forecast system. Journal of Geophysical Research: Atmospheres, 124, 9241–9261. https://doi.org/10.1029/2018JD029590.
  • Samalot, M. Astitha*, J. Yang, G. Galanis, 2019: A combination of Kriging and Kalman filtering applied to wind speed prediction of storms for NE U.S. Weather and Forecasting. https://doi.org/10.1175/WAF-D-18-0068.1 Published Online: 5 April 2019.
  • Huiying Luo, Marina Astitha*, Trivikrama Rao, Christian Hogrefe, Rohit Mathur, 2018: A New Method for Probabilistic Assessment of the Efficacy of Emission Control Strategies in Meeting the Ambient Ozone Standard. Atmospheric Environment, 199,233-243,  https://doi.org/10.1016/j.atmosenv.2018.11.010.
  • Yang, J., Astitha*, L. Delle Monache, S. Alessandrini, 2018: An Analog technique to improve storm wind speed prediction using a dual NWP model approach. Monthly Weather Review, https://doi.org/10.1175/MWR-D-17-0198.1.
  • Astitha, M.*, Kioutsioukis, I., Fisseha, G. A., Bianconi, R., Bieser, J., Christensen, J. H., Cooper, O. R., Galmarini, S., Hogrefe, C., Im, U., Johnson, B., Liu, P., Nopmongcol, U., Petropavlovskikh, I., Solazzo, E., Tarasick, D. W., and Yarwood, G.: Seasonal ozone vertical profiles over North America using the AQMEII3 group of air quality models: model inter-comparison and stratospheric intrusions, Atmos. Chem. Phys., 18, 13925-13945, https://doi.org/10.5194/acp-18-13925-2018, 2018.
  • Wanik, D.*, E. Anagnostou,   Astitha, B.  Hartman, G.  Lackmann, J.  Yang, D.  Cerrai, J.  He, and M.  Frediani, 2017:  A Case Study on Power Outage Impacts from Future    Hurricane    Sandy    Scenarios.    J.    Appl.    Meteor.    Climatol., ., 57, 51–79,    https://doi.org/10.1175/JAMC-D-16-0408.1.
  • Astitha*, M., Luo, H., Rao, S.T., Hogrefe, C., Mathur, R., Kumar, N., 2017: Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States, Atmospheric Environment, 164 (2017) 102-116, https://doi.org/10.1016/j.atmosenv.2017.05.020.
  • Yang, M. Astitha*, E. Anagnostou, B. Hartman, 2017: Using a Bayesian regression approach on dual-model weather simulations to improve wind speed prediction. Journal of Applied Meteorology and Climatology, Vol 56, 1155-1174, https://doi.org/10.1175/JAMC-D-16-0206.1.