Annual Frequency of Tropical Cyclones Directly Affecting Guangdong Province:Prediction Based on LSTM-FC

作     者：HU Ya-min CHEN Yun-zhu HE Jian LIU Sheng-jun YAN Wen-jie ZHAO Liang WANG Ming-sheng LI Zhi-hui WANG Juan-huai DONG Shao-rou LIU Xin-ru 胡娅敏;陈韵竹;何健;刘圣军;闫文杰;赵亮;汪明圣;李芷卉;王娟怀;董少柔;刘新儒

作者机构：Guangdong Climate CenterGuangzhou 510641 China School of Mathematics and StatisticsCentral South UniversityChangsha 410083 China State Key Laboratory of Numerical Modeling for Atmosphere Sciences and Geophysical Fluid Dynamics(LASG)Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 China 

出 版 物：《Journal of Tropical Meteorology》 (热带气象学报（英文版）)

年 卷 期：2022年第28卷第1期

页      面：45-56页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：National Key R&D Program of China(2017YFA0605004) Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004) National Basic R&D Program of China(2018YFA0606203) Special Fund of China Meteorological Administration for Innovation and Development(CXFZ2021J026) Special Fund for Forecasters of China Meteorological Administration(CMAYBY2020-094) Graduate Independent Exploration and Innovation Project of Central South University(2021zzts0477) Science and Technology Planning Program of Guangdong Province(20180207) 

主　　题：tropical cyclone frequency long short-term memory network fully connected layers Gaussian process regression multiple linear regression 

摘      要：Tropical cyclone(TC)annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong *** on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature(SST)V5 data in winter,the TC frequency climatic features and prediction models have been *** 1951-2019,353 TCs directly affected Guangdong with an annual average of about *** have experienced an abrupt change from abundance to deficiency in the mid to late 1980 with a slightly decreasing trend and a normal distribution.338 primary precursors are obtained from statistically significant correlation regions of SST,sea level pressure,1000hPa air temperature,850hPa specific humidity,500hPa geopotential height and zonal wind shear in *** those 338 primary factors are reduced into 19 independent predictors by principal component analysis(PCA).Furthermore,the Multiple Linear Regression(MLR),the Gaussian Process Regression(GPR)and the Long Short-term Memory Networks and Fully Connected Layers(LSTM-FC)models are constructed relying on the above 19 *** three different kinds of test sets from 2010 to 2019,2011 to 2019 and 2010 to 2019,the root mean square errors(RMSEs)of MLR,GPR and LSTM-FC between prediction and observations fluctuate within the range of 1.05-2.45,1.00-1.93 and 0.71-0.95 as well as the average absolute errors(AAEs)0.88-1.0,0.75-1.36 and 0.50-0.70,*** for the 2010-2019 experiment,the mean deviations of the three model outputs from the observation are 0.89,0.78 and 0.56,together with the average evaluation scores 82.22,84.44 and 88.89,*** prediction skill comparisons unveil that LSTM-FC model has a better performance than MLR and *** conclusion,the deep learning model of LSTM-FC may shed light on improving the accuracy of short-term climate prediction about TC *** current research can provide experience on the development of deep learning in this field and help to achieve furthe