Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 11-17.doi: 10.11896/jsjkx.201000038

• Artificial Intelligence • Previous Articles     Next Articles

Global Typhoon Message Collection Method Based on CNN-typhoon Model

HAN Rui1,2, GU Chun-li3, LI Zhe4,5, WU Kang4, GAO Feng1, SHEN Wen-hai1   

  1. 1 National Meteorological Information Center,China Meteorological Administration,Beijing 100081,China
    2 School of Environment,Tsinghua University,Beijing 100084,China
    3 Beijing Institute of Applied Meteorology,Beijing 100029,China
    4 Department of Precision Instruments,Tsinghua University,Beijing 100084,China
    5 Beijing Xiaopeng Automobile Co.,Ltd.,Beijing 100084,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:HAN Rui,born in 1986,master,senior engineer.Her main research interests include meteorological data processing and so on.
  • Supported by:
    This work was supported by the National Key R&D Program of China(2016YFA0600301),Shanghai Typhoon Research Foundation (TFJJ201904) and National Natural Science Foundation of China(91744209).

Abstract: Typhoon is a highly convective weather system with high impact.As the source of typhoon initial values,typhoon message data is helpful to improve the accuracy of typhoon forecasts.Therefore,it is very important to do a good job in the rapid identification and collection of global typhoon messages.Aiming at the problems of poor real-time performance,high latency,and passive message reception of global typhoon messages,this study uses MSG,Meteosat5,MTSAT,GOES-W,and GOES-E satellite 8 983 infrared satellite images of 1 351 typhoon processes from January 2006 to August 2020.Based on the deep learning algorithm,a CNN-typhoon model is proposed,which can identify and classify three types of images:no typhoon,typhoon generation,and strongest typhoon.Experiments have proved that the recognition accuracy of the CNN-typhoon model training set can be close to 100%,and the verification set accuracy is higher than 88.1%.At the same time,the model is substituted into the simulation service,and within a certain period of time,nearly 31.0% of the message collection types are increased,saving the message collection timeliness is improved by 23.5 times.

Key words: Classification, Collection, Convolutional Neural Networks, Identification, Typhoon message

CLC Number: 

  • TP183
[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.
[1] HU An-xiang, YIN Xiao-kang, ZHU Xiao-ya, LIU Sheng-li. Strcmp-like Function Identification Method Based on Data Flow Feature Matching [J]. Computer Science, 2022, 49(9): 326-332.
[2] CHEN Zhi-qiang, HAN Meng, LI Mu-hang, WU Hong-xin, ZHANG Xi-long. Survey of Concept Drift Handling Methods in Data Streams [J]. Computer Science, 2022, 49(9): 14-32.
[3] ZHOU Xu, QIAN Sheng-sheng, LI Zhang-ming, FANG Quan, XU Chang-sheng. Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification [J]. Computer Science, 2022, 49(9): 132-138.
[4] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[5] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[6] CHEN Kun-feng, PAN Zhi-song, WANG Jia-bao, SHI Lei, ZHANG Jin. Moderate Clothes-Changing Person Re-identification Based on Bionics of Binocular Summation [J]. Computer Science, 2022, 49(8): 165-171.
[7] TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo. Review of Text Classification Methods Based on Graph Convolutional Network [J]. Computer Science, 2022, 49(8): 205-216.
[8] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[9] WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25.
[10] SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235.
[11] GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang. Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features [J]. Computer Science, 2022, 49(7): 40-49.
[12] YANG Bing-xin, GUO Yan-rong, HAO Shi-jie, Hong Ri-chang. Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition [J]. Computer Science, 2022, 49(7): 57-63.
[13] ZHANG Hong-bo, DONG Li-jia, PAN Yu-biao, HSIAO Tsung-chih, ZHANG Hui-zhen, DU Ji-xiang. Survey on Action Quality Assessment Methods in Video Understanding [J]. Computer Science, 2022, 49(7): 79-88.
[14] MENG Yue-bo, MU Si-rong, LIU Guang-hui, XU Sheng-jun, HAN Jiu-qiang. Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism [J]. Computer Science, 2022, 49(7): 142-147.
[15] DU Li-jun, TANG Xi-lu, ZHOU Jiao, CHEN Yu-lan, CHENG Jian. Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning [J]. Computer Science, 2022, 49(6A): 60-65.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!