论文标题
飓风轨道适合改善飓风预测的共识模型
The Hurricane Track Fit Consensus Model for Improving Hurricane Forecasting
论文作者
论文摘要
我们提出了一种新方法，以建立模型共识来改善实时飓风轨道预测。该方法基于历史数值模型预测到观察到的风暴位置的统计拟合，并从其历史错误和偏见中学习。我们的方法最接近HFIP校正的共识方法（HCCA）方法，同时使用替代模型公式。我们的方法为每个预测小时创建一个单独的共识模型，使得可以在该特定小时内独立纠正每个输入模型的偏差。我们称这种方法称为飓风轨道拟合（HFIT）模型，在计算上是有效的，可扩展到其他数值模型作为输入，并且会产生可解释的系数权衡模型贡献。 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数值模型轨道共识（TVCA）。 2014 - 2021年飓风轨道数据集的交叉验证结果表明，与输入模型和官方NHC预测（OFCL）相比，HFIT共识模型始终减少轨道预测错误。例如，在24小时内，HFIT轨道预测误差分别比AVNI和EMXI分别小于18.5％和15.6％，在72H时为23％和15％。 HFIT预测显示出错误的降低，而OFCL则在24H时为8.1％，在72h时为7.5％。我们还讨论了2022年飓风季节HFIT的成功实时运营性能。
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.
