Estimation of Precipitation Induced by Tropical Cyclones Based on Machine‐learning‐enhanced Analogue Identification of Numerical Prediction

AbstractA tropical cyclone (TC) is an extremely hazardous weather event. These events include heavy rain as an important hazard factor, which poses a serious threat to the public safety of coastal cities. Presently, numerical weather prediction (NWP) is an important and commonly used method to support the forecasting of the impact of TCs. However, relatively high uncertainty still remains in quantitative precipitation predictions provided by NWP, which makes it difficult to meet the demands of public safety management in regions affected by TCs. To remediate this deficiency, the study combines machine learning (ML) techniques with NWP and proposes a new analogue identification method for TC precipitation estimation. The method consists of three parts. First, the output data of historical NWPs, including wind, temperature, humidity of 850 hPa and sea‐level pressure field, are deposited into a massive sample library. Second, the dimensionality of the sample library is reduced by using the locally linear embedding (LLE) method, and a characteristic subspace containing the features of the original sample library is formed. Finally, when a new NWP prediction appears, it can be projected into the characteristic subspace, and the historical NWP sample having the greatest similarity with the current NWP prediction can be identified. The observational precipitation corresponding to the most similar historical NWP sample can then be used to estimate the potential impact of current TCs. This method is verified using observation results from the coastal city of Shenzhen, China. The results show that the prediction of the method proposed in the current study exhibited significant improvement compared with prediction results provided by both the NWP's direct output and a traditional method used in the city.