IMPROVEMENT IN TRACK AND INTENSITY PREDICTION OF HURRICANE FLORENCE 2018 USING WRF AND HWRF MODELS WITH REGRESSION ANALYSIS

Hurricane Florence (2018) was one of the most destructive storms of the 2018 hurricane season. This storm produced a substantial amount of precipitation, which caused immense flooding along the coast. As a result, billions of dollars of damage were done to the coast. This study explored approaches to improve the prediction of the track and intensity of Hurricane Florence (2018) by utilizing the deterministic Numerical Weather Prediction (NWP) models and a statistical modeling-based ensemble technique. The Global Forecast System (GFS) data is employed to initialize the Weather Research and Forecasting (WRF) and Hurricane WRF (HWRF) models to produce numerous simulations with various scheme options and starting times. The simulation data from five different NWP (Numerical Weather Prediction) models including the HWRF, WRF, ECMWF (European Centre for Medium-Range Weather Forecasts), and GFS models, were then interpolated to prepare for the statistical models. With the interpolated data, a hybrid method with multiple linear regression (MLR), random forest, and simple ensemble (SE) was developed. This hybrid method used multiple linear regression and random forest to identify the significant factors for hurricane prediction in the training set, and an averaging ensemble was then applied to the significant factors’ data. As verified in the testing data sets, the errors from the hybrid method were reduced, indicating the improvement of the predictability. It is found that our numerical simulations using the HWRF model with a statistical modeling-based ensemble technique improved the accuracy of the track and intensity prediction of Hurricane Florence (2018). Overall, these tools and methods can greatly improve the accuracy of the track and intensity prediction of future hurricanes like Florence and can help ensure better civilian preparedness for a hazardous storm.