Predicting the intensity of blocking events using machine and deep learning

Abstract

Blocking events are high pressure systems that occur in the middle to upper latitudes, diverting the flow of the jet stream and preventing the regular progression of the weather.  Blocking events can persist for days to weeks, potentially causing extreme weather events such as droughts or heatwaves. Due to the harmful effects associated with the weather conditions that blocking events can cause, there have been ongoing efforts to forecast them. One such effort includes the Global Ensemble Forecast System (GEFS), which has been reported to underestimate the intensity of stronger blocking events by approximately ten percent. In the current study, one deep learning and three machine learning models were developed to predict the intensity of newly formed blocking events at onset. It was hypothesized that the models would have comparable percent error to the GEFS while using less time and computational resources, that there would be a strong correlation between predicted and actual blocking intensity, and that the deep learning model would have lower error than the machine learning models. The results showed that the models did indeed have comparable error to the GEFS and that there was a statistically significant correlation between predicted and actual blocking intensity for all four models, thus supporting the first two parts of the hypothesis. The third part was not supported since there was no statistically significant difference in error between any of the models, however the deep learning model was noted for not overfitting unlike the machine learning models.