计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 11-17.doi: 10.11896/jsjkx.201000038
韩瑞1,2, 顾春利3, 李哲4,5, 伍康4, 高峰1, 沈文海1
HAN Rui1,2, GU Chun-li3, LI Zhe4,5, WU Kang4, GAO Feng1, SHEN Wen-hai1
摘要： 台风是一种高影响的强对流天气系统。台风报文资料作为提供台风初值的最初来源,对改进台风预报准确性有一定的帮助,因此做好全球台风的报文快速识别收集工作至关重要。针对现有全球台风报文实时性差、延迟高、报文被动接收的问题,本研究利用MSG,Meteosat-5,MTSAT,GOES-W,GOES-E卫星图像数据,通过训练2006年1月－2020年8月的1 351次全球热带气旋过程,共计8 983张红外卫星图像,基于深度学习算法,提出了一种CNN-typhoon模型,可以对无台风、台风生成、台风最强等3种图像进行识别分类。实验证明:CNN-typhoon模型训练集的识别精度可接近100%,验证集精度高于88.1%;同时将模型代入模拟业务,在一定时段内增加了接近31.0%的报文收集种类,报文收集时效提高了23.5倍。
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| [1] DONG L,XU Y L,LYU X Y,et al.Analysis of forecast focuses of binary typhoons Nesata and Haitang [J].Meteor Mon,2020,46(1):29-36. [2] ZHENG Z S,HU C Y,HUANG D M,et al.Research on Transfer Learning Methods for Classification of Typhoon Cloud Image [J].Remote Sensing Technology and Application,2020,35(1):202-210. [3] SHA T Y,YANG G J,CHENG Z Q.A Brief Account of theForecast Products Based on the Forecast Texts of Ensemble Prediction from ECMWF [J].Guangdong Meteorology,2015,37(1):4-9. [4] CUN Y L,DENKER J S,SOLLA S A.Optimal brain damage [C]//International Conference on Neural Information Proces-sing Systems.1989. [5] KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImageNetClassification with Deep Convolutional Neural Networks[C]//Proceedings of NIPS 2012.2012. [6] ZHOU F Y,JIN L P,DONG J,et al.Review of convolutional neual network[J].Chinese Journal of Computers,2017,40(6):1229-1251. [7] SZELISKI R.Computer vision:algorithms and applications[M].New York:Springer,2010:83-132. [8] ZHENG Y Q,YANG X F,LI Z W.Detection of severe convective cloud over sea surface from geostationary meteorological satellite images based on deep learning[J].Journal of Remote Sensing(Chinese),2020,24(1):97-106. [9] ZOU G L,HOU Q,ZHENG Z S,et al.Classification of Typhoon Grade Based on Satellite Cloud Image and Deep Learning [J].Remote Sensing Information,2019,34(3):1-6. [10] SU H,WU L,JIANG J H,et al.Applying satellite observations of tropical cyclone internal structures to rapid intensication forecast with machine learning[J].Geophysical Research Letters,2020,47:e2020GL089102. [11] REICHSTEIN M,CAMPS-VALLS G,STEVENS B,et al.Deep learning and process understanding for data-driven Earth system science[J].Nature,2019,566:195-204. [12] WMO 2019:Typhoon Committee Operational Manual - Meteorological Component[R].World Meteorological Organization,Tropical Cyclone Programme Report No.TCP-23,WMO/TD-No.196. [13] WMO 2008:Guide to meteorological instruments and methods of observation[OL].http://www.seedmech.com/documents_folder/wmo_no_8.pdf. [14] WMO 2003a:Regional association IV (North and Central America and the Caribbean) hurricane operational plan[R].World Meteorological Organization,Tropical Cyclone Programme Report No.TCP-30,WMO/TD-No.494. [15] WMO 2003b:Typhoon committee operational manual meteorological component[R].World Meteorological Organization,Tropical Cyclone Programme Report No.TCP-23,WMO/TD-No.196. [16] WMO 2002b:Tropical cyclone operational plan for the Bay ofBengal and the Arabian Sea[R].World Meteorological Organization,Tropical Cyclone Programme Report No.TCP-21,WMO/TD-No.84. [17] WMO 2002c:Tropical cyclone operational plan for the South-West Indian Ocean[R].World Meteorological Organization,Tropical Cyclone Programme Report No.TCP-12,WMO/TD-No.577. [18] WMO 2002a:Tropical cyclone operational plan for the SouthPacific and South-East Indian Ocean[R].World Meteorological Organization,Tropical Cyclone Programme Report No.TCP-24,WMO/TDNo.292. [19] China Meteorological Administration.Typhoon business andservice regulations[M].Beijing:Meteorological Press,2012. [20] HWANG J,ORENSTEIN P,COHEN J,et al.Improving Subseasonal Forecasting in the Western U.S.with Machine Lear-ning[C]//The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19).ACM,New York,2019:14. [21] IGLESIAS G,KALE D C,LIU Y.An examination of deeplearning for extreme climate pattern analysis.In The 5th International Workshop on Climate Informatics[C]//Boulder,CO,USA,2015. [22] PAOLETTI M E,HAUT J M,PLAZA J,et al.A new deep convolutional neural network for fast hyperspectral image classification[J].ISPRS Journal of Photogrammetry and Remote Sen-sing,2017,S0924271617303660. [23] HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [24] KNAFF J A,LONGMORE S P,DEARIA R T,et al.Improved tropical-cyclone flight-level wind estimates using routine infrared satellite reconnaissance[J].J.Appl.Meteor.Climat.,2015,54(2):463-478. [25] MUELLER K J,DEMARIA M,KNAFF J,et al.Objective Estimation of Tropical Cyclone Wind Structure from Infrared Satellite Data [J].Weather & Forecasting,2006,21(6):990-1005. [26] ZEHR R M.Tropical cyclone research using large infrared data sets[C]//Preprints,24th Conf.on Hurricanes and Tropical Meteorology.2000:486-487. |
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