Date of Completion
Marina Astitha, Emmanouil Anagnostou, Amvrossios Bagtzoglou
Field of Study
Master of Science
The scope of this study is to identify and improve wind speed prediction errors for storms that have impacted the Northeastern United States during 2003-2014. Accurate wind speed prediction under storm occurrences is significant to identify and assess impacts to the environment and critical infrastructure. Post-processing of a numerical weather prediction model (Weather Research and Forecasting-WRF) was used in the form of Universal Kriging for spatial interpolation and Kalman Filter for bias reduction. Two strategies for using the Kalman Filter in combination with Universal Kriging are investigated and assessed. Universal Kriging of Kalman Filter corrections reduced all error statistics of the WRF model surface wind speed outputs used in this study. The spatial and seasonal variability of wind speed error reduction are also discussed as well as suggestions for future research directions in this topic.
Samalot, Alexander, "Combined Universal Kriging and Kalman Filter Techniques to Improve Wind Speed Prediction for Northeastern U.S." (2017). Master's Theses. 1131.