  We present a new method for creating a model consensus to improve real-time
hurricane track prediction. The method is based on the statistical fitting of
historic numerical model track forecasts to the observed storm positions and
learning from their historical errors and biases. Our method is closest to the
HFIP Corrected Consensus Approach (HCCA) methodology while using an alternative
model formulation. Our 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, and it produces interpretable coefficients weighing
model contributions.
  The new method is evaluated for the 2014-2021 hurricane seasons in the
Atlantic basin using the input from the best-performing operational track
forecast guidance at the National Hurricane Center (NHC): the U.S. National
Weather Service Global Forecast System deterministic and ensemble mean models,
European Centre for Medium-Range Weather Forecasts deterministic model, the NWS
Hurricane Weather Research and Forecasting model and the NHC equally weighted
numerical model track consensus (TVCA). The results of the cross-validation for
the 2014-2021 hurricane track dataset show that the HFIT consensus model
consistently reduces the track forecast errors compared to those from the input
models and the official NHC forecasts (OFCL). For example, at 24h the HFIT
track forecast errors are smaller by 18.5% and 15.6% than those in AVNI and
EMXI respectively, and 23% and 15% smaller at 72h. The HFIT forecasts show a
reduction of errors compared to OFCL by 8.1% at 24h and 7.5% at 72h. We also
discuss the successful real-time operational performance of HFIT during the
2022 hurricane season.
