A Computationally Efficient Consensus Model for Improving Hurricane Track Forecasting

A new method to create a model consensus forecast to improve real-time hurricane track prediction is presented. The method is based on the statistical fitting of historical numerical model track forecasts to the observed storm positions and adjusting for their historical errors and biases. The method is closest to the Hurricane Forecast Improvement Program (HFIP) Corrected Consensus Approach (HCCA) methodology, while using an alternative model formulation. The new method creates a separate consensus model for each forecast hour, making it possible to independently correct the bias of each input model for that specific hour. This approach, which we call the Hurricane Track Fit (HFIT) model, is computationally efficient and scalable to additional numerical models as input. The new method is developed using forecast data from the 2016 to 2022 hurricane seasons in the Atlantic basin with input from some of the top-performing operational track forecast guidance available to the National Hurricane Center. The results of a cross validation for the 2016-22 hurricane track dataset and of forecast tests using an independent dataset of storms from the 2023 to 2024 Atlantic hurricane seasons show that the HFIT consensus model produces statistically significant smaller track forecast errors compared to those from its individual input models. Tests with the independent dataset also yield results consistent with those from other operational consensus models and the official National Hurricane Center (NHC) forecasts. HFIT shows statistically significant improvements at 24 and 36 h over a version of the model that applies equal weights to the HFIT input models. SIGNIFICANCE STATEMENT: This work details the development of a hurricane track forecasting consensus model that is based on multiple linear regression techniques. It is similar to prior work performed on this topic; however, it utilizes a novel technique for formulating the model that can provide forecasters with an additional method of producing consensus hurricane track forecast guidance.