Our team has a diverse background. We’ve compiled relevant publications from their expertise that contributes to the scope of CLEAN EARTH. We invite you to read their publications. 

  • Wang, G., & Sun, X. (2022). Monotonic increase of extreme precipitation intensity with temperature when controlled for saturation deficit. Geophysical Research Letters. Click here for Article.
  • Wang, G., Kirchhoff, C., Seth, A., Abatzoglou, J., Livneh, B., Pierce, D., Fomenko, L., Tengyu, D. (2020). Projected Changes of Precipitation Changes of Precipitation Characteristics Depend on Downscaling Method and Training Data: MACA versus LOCA Using the U.S. Northeast as an Example. American Meteorological Society. Click here for Article.
  •   T. Zaman, U. Khaira, M. Astitha, 2021: Updates on Accuracy Analysis in Wind Prediction Using High-Resolution WRF Simulations for an Offshore Wind Farm Facility in the Northeast Atlantic Cluster. AGU 2021 Fall Meeting, Dec 12-16, 2021.
  • 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.
  • Samalot, A.M. Astitha*, J. Yang, and G. Galanis, 2019: Combined Kalman Filter and Universal Kriging to Improve Storm Wind Speed Predictions for the Northeastern United States. Wea. Forecasting, 34, 587–601,
  • Yang, J., M. Astitha*, L. Delle Monache, and S. Alessandrini, 2018: An Analog Technique to Improve Storm Wind Speed Prediction Using a Dual NWP Model Approach. Mon. Wea. Rev., 146, 4057–4077,
  • J. YangM. 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,
  • Marina Astitha and Efthymios Nikolopoulos (co-Editors): “Extreme Weather Forecasting: State of the science, uncertainty and impacts”. Elsevier (edited contribution), October 2022. Paperback ISBN: 9780128201244.