参考文献/References:
[1] 端义宏,陈联寿,梁建茵,等.台风登陆前后异常变化的研究进展[J]. 气象学报, 2014,72(5),969-986.
[2] 端义宏,陈联寿,许映龙,等.我国台风监测预报预警体系的现状及建议[J]. 中国工程科学, 2012,14(9):4-9.
[3] 周冠博,柳龙生,董林,等. 2020年西北太平洋台风活动特征和预报难点分析[J]. 气象, 2022,48(4):504-515.
[4] 吕心艳,许映龙,董林,等. 2018年西北太平洋台风活动特征和预报难点分析[J]. 气象,2021,47(3):359-372.
[5] 钱传海,端义宏,麻素红,等. 我国台风业务现状及其关键技术[J].气象科技进展, 2012,2(5):36-43.
[6] Emanuel K,Zhang F Q.On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts[J]. Journal of the Atmospheric Sciences, 2016,73(9):3739-3747.
[7] 吴影,陈佩燕,雷小途. 登陆热带气旋路径和强度预报的效益评估初步研究[J]. 热带气象学报,2017,33(5):675-682.
[8] Bhatia K T,Nolan D S,Schumacher A B,et al. Improving Tropical Cyclone Intensity Forecasts with PRIME[J]. Weather and Forecasting,2017,32(4):1353-1377.
[9] Cecil D J, Zipser E J. Relationships between Tropical Cyclone Intensity and Satellite-Based Indicators of Inner Core Convection:85-GHz Ice-Scattering Signature and Lightning[J].American Meteorological Society,1999:103-123.
[10] Velden C S,Harper B,Wells F,et al. The Dvorak Tropical Cyclone Intensity Estimation Technique:A Satellite-Based Method that has Endured for over 30 Years[J]. Bulletin of the American Meteorological Society,2006,87(9):1195-1210.
[11] Olander T L,Velden C S. Tropical Cyclone Convection and Intensity Analysis Using Differenced Infrared and Water Vapor Imagery[J].Weather and Forecasting,2009,24(6):1558-1572.
[12] Ritchie L,Valliere K G,Pi?ros M F, et al. Tropical Cyclone Intensity Estimation in the North Atlantic Basin Using an Improved Deviation Angle Variance Technique[J]. Weather and Forecasting, 2012,27(5),1264-1277.
[13] Dvorak V F.Tropical Cyclone Intensity Analysis and Forecasting from Satellite Imagery[J]. American Meteorological Society, 1975:420-430.
[14] Dvorak V F. Tropical Cyclone Intensity Analysis Using Satellite Data[J]. National Oceanic and Atmospheric Administration, National Earth Satellite Service, 1984.
[15] Velden C S,Olander T L,Zehr R M.Development of an Objective Scheme to Estimate Tropical Cyclone Intensity from Digital Geostationary Satellite Infrared Imagery[J]. American Meteorological Society,1998:172-186.
[16] Olander T L,Velden C S.The Advanced Dvorak Technique:Continued Development of an Objective Scheme to Estimate Tropical Cyclone Intensity Using Geostationary Infrared Satellite Imagery[J]. Weather and Forecasting, 2007,22(2):287-298.
[17] 许映龙,张玲,向纯怡.台风定强技术及业务应用-以Dvorak技术为例[J]. 气象科技进展,2015,5(4):22-34.
[18] Krizhevsky A, Hinton G. Learning Multiple Layers of Features from Tiny Images[J].Handbook of Systemic Autoimmune Diseases,2009,1(4):1-10.
[19] Krizhevsky A,Sutskever I,Hinton G.ImageNet Classification with Deep Convolutional Neural Networks[J].Advances in Neural Information Processing Systems,2012:1097-1105.
[20] He K M, Zhang X Y,Ren S Q, et al. Deep Residual Learning for Image Recognition[C]//. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016:770-778.
[21] 崔林丽,郭巍,葛伟强,等. FY-4A卫星云顶参数精度检验及台风应用研究[J].高原气象,2020,39(1):196-203.
[22] Zhang C J,Wang X J, Ma L M, et al. Tropical Cyclone Intensity Classification and Estimation Using Infrared Satellite Images with Deep Learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021.
[23] Zhuo J, Tan Z M. Physics-Augmented Deep Learning to Improve Tropical Cyclone Intensity and Size Estimation from Satellite Imagery[J].Monthly Weather Review, 2021, 149(7):2097-2113.
[24] 严军, 刘健文.基于神经网络奇异谱分析的ENSO指数预测[J]. 大气科学,2005, 29(4):620-626.
[25] 陈刚毅, 丁旭羲, 赵丽妍.用模糊神经网络自动识别云的技术研究[J]. 大气科学,2005, 29(5):837-844.
[26] 金龙, 秦伟良, 姚华栋.多步预测的小波神经网络预报模型[J]. 大气科学,2000, 24(1):79-86.
[27] 刘妹琴.离散时滞标准神经网络模型及其应用[J]. 中国科学E辑:信息科学, 2005,35(10):1031-1048.
[28] 曹祥村,邵利民.一种利用BP网络预报台风路径的新方法[J]. 海洋预报,2007(3):75-82.
[29] 洪梅, 张韧, 吴国雄, 等.用遗传算法重构副热带高压特征指数的非线性动力模型[J]. 大气科学,2007,31(2):346-352.
[30] 胡娅敏, 丁一汇, 沈桐立.基于遗传算法的四维变分资料同化技术的研究[J]. 大气科学,2006,30(2):248-256.
[31] Zheng D, Liang R, Zhou Y, et al. A Chaos Genetic Algorithm for Optimizing an Artificial Neural Network of Prediction Silicon Content in Hot Metal[J].International Journal of Minerals Metallurgy and Materials, 2003.
[32] 郭章林, 刘明广, 解德才.震灾经济损失评估的遗传神经网络模型[J]. 自然灾害学报, 2004,13(6):92-96.
[33] Huang X Y,Jin L,Shi X M. A Nonlinear Artificial Intelligence Ensemble Prediction Model Based on EOF for Typhoon Track[C]//.Fourth International Joint Conference on Computational Sciences and Optimization,2011.
[34] Jin L, Yao C, Huang X Y. A Nonlinear Artificial Intelligence Ensemble Prediction Model for Typhoon Intensity[J]. Monthly Weather Review,2008, 136:4541-4554.
[35] 黄小燕,金龙.基于主成分分析的人工智能台风路径预报模型[J]. 大气科学,2013,37(5):1154-1164.
[36] 王瀚.基于深度学习的台风路径预测多模型算法研究[D].成都:电子科技大学, 2020.
[37] 邵利民,傅刚,曹祥村,等. BP神经网络在台风路径预报中的应用[J]. 自然灾害学报,2009,18(6):104-111.
[38] 吕庆平,罗坚,朱坤,等.基于SVM的气候持续法在热带气旋路径预报中的应用试验[J]. 海洋预报,2009,26(1):76-83.
[39] 朱雷.基于神经网络委员会机器的南中国海台风路径预报模型研究[D].上海:华东师范大学,2017.
[40] 周笑天, 张丰, 杜震洪, 等.基于神经网络集合预报的台风路径预报优化[J].浙江大学学报(理学版),2020,47(2):196-204,217.
[41] Rüttgers M, Lee S,Jeon S, et al. Prediction of a Typhoon Track Using a Generative Adversarial Network and Satellite Images[J]. Scientific Reports, 2019,9(1).
[42] Pradhan R, Aygun R S, Maskey M, et al. Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network[J]. IEEE Transactions on Image Processing,2018,27(2).
[43] Zahera H M, Sherif M A, Ngonga A C N. Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction[C]//. Proceeding of the 10th International Conference on Knowledge Capture,2019:231-234.
[44] Chen B,Chen B F,Lin H T.Rotation-Blended CNNs on a New Open Dataset for Tropical Cyclone Image-to-Intensity Regression[C]//. Zn Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery&Data Mining,2018:90-99.
[45] Wei T,Wei H W,Xu X L,et al.Tropical Cyclone Maximum Wind Estimation from Infrared Satellite Data with Integrated Convolutional Neural Networks[C]//. 2019 International Conference on Internet of Things(iThings) and IEEE Green Computing and Communications(GreenCom) and IEEE Cyber,Physical and Social Computing(CPSCom) and IEEE Smart Data(Smart-Data),2019:575-580.
[46] 张淼,覃丹宇,邱红.基于FY-3C/MWTS-Ⅱ数据估计西北太平洋热带气旋强度[J]. 气象,2017,43(5):573-580.
[47] Combinido J S, Mendoza J R,Aborot J.A Convolutional Neural Network Approach for Estimating Tropical Cyclone Intensity Using Satellite-based Infrared Images[C]//. 201824th International Conference on Pattern Recognition (ICPR),2018.
[48] 邹国良,侯倩,郑宗生,等.面向卫星云图及深度学习的台风等级分类[J]. 遥感信息,2019,34(3):1-6.
[49] Chen B F,Davis C A,Kuo Y H.Effects of Low-Level Flow Orientation and Vertical Shear on the Structure and Intensity of Tropical Cyclones[J]. Monthly Weather Review, 2018,146:2447-2467.
[50] Chen B F, Chen B, Lin H T, et al. Estimating Tropical Cyclone Intensity by Satellite Imagery Utilizing Convolutional Neural Networks[J].Weather and Forecasting,2019, 34(2):447-465.
[51] Xu Y J,Yang H T,Cheng M F,et al.Cyclone Intensity Estimate with Context-Aware Cyclegan[C]//. ICIP 2019,2019:3417-3421.
[52] Chen B, Chen B F, Chen Y N. Real-Time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data[J].2020.