Recent Publications

Selected Peer Review Articles

  • Jahan, D. Cerrai, M. Astitha*, 2023: “Storm gust prediction with the integration of machine learning algorithms and WRF model variables for NE US”. Artif. Intell. Earth Syst. Accepted 04/02/2024.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.

Books

  • Marina Astitha and Efthymios Nikolopoulos (co-Editors): “Extreme Weather Forecasting: State of the science, uncertainty and impacts”. Elsevier (edited contribution), October 2022. eBook ISBN: 9780128202432, Paperback ISBN: 9780128201244.

Selected Recent Conference Presentations

  • Khaira and M. Astitha, 2024: Enhancing Snowfall Accumulation Prediction in the Northeastern United States: Integrating NWP, Probabilistic Forecasts, and Machine Learning, AMS 104th Annual Meeting, Jan 28-Feb 1, 2024. Poster presentation.
  • Zaman, M. Astitha, M. Badhan, G. Wang, E. D. Gutmann 2024: Assessing Climate Change Impact on Green (Wind) Energy Resources in the Northeast U.S. using a Statistical Downscaling Approach. AMS 104th Annual Meeting, 15th Conference on Weather, Climate, and the New Energy Economy, January 27-31, 2024.
  • Khaira, D. Cerrai, G. Thompson, M. Astitha, 2023: Advancing Snowfall Prediction in the Northeast United States: An Integrated Machine Learning and Numerical Weather Modeling Approach., AGU 2023 Fall Meeting, Dec 11-15, 2023. Poster presentation.
  • Zaman, T., Astitha, B. Kosovic, P. Jimenez Munoz, T. W. Juliano and M. Eghdami 2023: Advancements in Offshore Wind and Turbulence Prediction for the Northeast US using a Three-Dimensional PBL Parameterization in the WRF model. AGU 2023 Fall Meeting, Dec 11-15, 2023. (Poster presentation)
  • Tasnim Zaman, Marina Astitha, Makduma Badhan, Yelin Jiang, Guiling Wang, Ethan Gutmann, Patrick Hawbecker, and Timothy W. Juliano: Offshore wind prediction and statistical downscaling for climate change assessment of wind energy in the Northeast US. 103rd American Meteorological Society Annual Meeting, 14th Conference on Weather, Climate, and the New Energy Economy, Jan 8-12, 2023, Denver, CO.
  • Christina Feng Chang, Marina Astitha, Penny Vlahos, Valerie Garcia, 2023: Comparing the capability of two ML algorithms to assess physical and biological oxygen indicators in freshwater ecosystems using simulated environmental variables. 103rd American Meteorological Society Annual Meeting, 22nd Conference on Artificial Intelligence for Environmental Science, Jan 8-12, 2023, Denver, CO.
  • Israt Jahan, Marina Astitha, Diego Cerrai, 2023: Application of Machine Learning (ML) Algorithms for Wind Gust prediction: a Comparison between WRF and AI. 103rd American Meteorological Society Annual Meeting, 22nd Conference on Artificial Intelligence for Environmental Science, Jan 8-12, 2023, Denver, CO.
  • Ummul Khaira, Diego Cerrai, Greg Thompson, and Marina Astitha, 2022: Challenges and successes of snowfall forecasting using machine learning and numerical prediction for the Northeast United States. AGU 2022 Fall Meeting, Chicago, Dec 12-15, 2022.
  • Israt Jahan, Marina Astitha, Diego Cerrai, 2022: Improving wind gust prediction with the combination of WRF and machine learning algorithms. AGU 2022 Fall Meeting, Chicago, Dec 12-15, 2022.
  • Tasnim Zaman, Marina Astitha, Patrick Hawbecker, and Timothy W. Juliano, 2022: Impact of High-resolution Mesoscale and Atmospheric Stability on predicting Offshore Wind in the Northeast Atlantic Cluster. AGU 2022 Fall Meeting, Chicago, Dec 12-15, 2022.
  • Tasnim Zaman, Marina Astitha, Makduma Badhan, Yelin Jiang, Guiling Wang, Ethan Gutmann, 2022: Application of statistical downscaling for climate change assessment of wind energy in the Northeast U.S. AGU 2022 Fall Meeting, Chicago, Dec 12-15, 2022.
  • Christina Feng Chang, Marina Astitha, Yongping Yuan, Chunling Tang, Penny Vlahos, Valerie Garcia. 2022: Applying a Machine Learning and Multi-Media Modeling Framework to Predict Tributary Phosphorus Loads. The 22nd Annual Community Modeling and Analysis System (CMAS) Conference, University of North Carolina, 17-19 Oct 2022.
  • Ummul Khaira, Diego Cerrai, Greg Thompson, and Marina Astitha, 2022: Challenges and successes of snowfall forecasting using machine learning and numerical prediction for the Northeast United States. AGU 2022 Fall Meeting, Chicago, Dec 12-15, 2022.