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Full-Text Articles in Physical Sciences and Mathematics
On The Use Of Machine Learning For Causal Inference In Extreme Weather Events, Yuzhe Wang
On The Use Of Machine Learning For Causal Inference In Extreme Weather Events, Yuzhe Wang
Discovery Undergraduate Interdisciplinary Research Internship
Machine learning has become a helpful tool for analyzing data, and causal Inference is a powerful method in machine learning that can be used to determine the causal relationship in data. In atmospheric and climate science, this technology can also be applied to predicting extreme weather events. One of the causal inference models is Granger causality, which is used in this project. Granger causality is a statistical test for identifying whether one time series is helpful in forecasting the other time series. In granger causality, if a variable X granger-causes Y: it means that by using all information without …
Comparison Of The Performance Of The Observation-Based Hybrid Edmf And Edmf-Tke Pbl Schemes In 2020 Tropical Cyclone Forecasts From The Globalnested Hurricane Analysis And Forecast System, Andrew Hazelton, Jun A. Zhang, Sundararaman Gopalakrishnan
Comparison Of The Performance Of The Observation-Based Hybrid Edmf And Edmf-Tke Pbl Schemes In 2020 Tropical Cyclone Forecasts From The Globalnested Hurricane Analysis And Forecast System, Andrew Hazelton, Jun A. Zhang, Sundararaman Gopalakrishnan
Department of Earth, Atmospheric, and Planetary Sciences Faculty Publications
Better representation of the planetary boundary layer (PBL) in numerical models is one of the keys to improving forecasts of TC structure and intensity, including rapid intensification. To meet this goal, our recent work has used observations to improve the eddy-diffusivity mass flux with prognostic turbulent kinetic energy (EDMF-TKE) PBL scheme in the Hurricane Analysis and Forecast System (HAFS). This study builds on that work by comparing a modified version of EDMF-TKE (MEDMF-TKE) with the hybrid EDMF scheme based on a K-profile method (HEDMF-KP) in the 2020 HAFS-globalnest model. Verification statistics based on 101 cases in the 2020 season demonstrate …