A Recombination Clustering Technique for Forecasting of Tropical Cyclone Tracks Based on the CMA-TRAMS Ensemble Prediction System

Despite marked improvements in tropical cyclone (TC) track ensemble forecasting, forecasters still have difficulty in making quick decisions when facing multiple potential predictions, so it is demanding to develop post-processing techniques reducing the uncertainty in TC track forecasts, and one of such techniques is the cluster-based methods. To improve the effect and efficiency of the previous cluster-based methods, this study adopts recombination clustering (RC) by optimizing the use of limited TC variables and constructing better features that can accurately capture the good TC track forecasts from the ensemble prediction system (EPS) of the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS). The RC technique is further optimized by constraining the number of clusters using the absolute track bias between the ensemble mean (EM) and ensemble spread (ES). Finally, the RC-based deterministic and weighted probabilistic forecasts are compared with the TC track forecasts from traditional methods. It is found that (1) for deterministic TC track forecasts, the RC-based TC track forecasts outperform all other methods at 12–72-h lead times; compared with the skillful EM (118.6 km), the improvements introduced by the use of RC reach up to 10.8% (8.1 km), 10.2% (13.7 km), and 8.7% (20.5 km) at forecast times of 24, 48, and 72 h, respectively. (2) For probabilistic TC track forecasts, RC yields significantly more accurate and discriminative forecasts than traditional equal-weight track forecasts, by increasing the weight of the best cluster, with a decrease of 4.1% in brier score (BS) and an increase of 1.4% in area under the relative operating characteristic curve (AUC). (3) In particular, for cases with recurved tracks, such as typhoons Saudel (2017) and Bavi (2008), RC significantly reduces track errors relative to EM by 56.0% (125.5 km) and 77.7% (192.2 km), respectively. Our results demonstrate that the RC technique not only improves TC track forecasts but also helps to unravel skillful ensemble members, and is likely useful for feature construction in machine learning.