The Dvorak tropical cyclone intensity estimation technique

被引:312
|
作者
Velden, Christopher
Harper, Bruce
Wells, Frank
Beven, John L., II
ehr, Ray
Olander, Timothy
Mayfield, Max
Guard, Charles Chip
Lander, Mark
Edson, Roger
Avila, Lixion
Burton, Andrew
Turk, Mike
Caroff, Akihiro
Christian, Adam
Caroff, Philippe
McCrone, Paul
机构
[1] Univ Wisconsin, CIMSS, Madison, WI 53706 USA
[2] Syst Engn Australia, Brisbane, Qld, Australia
[3] NOAA, Trop Predict Ctr, Miami, FL 33149 USA
[4] NOAA, NWSFO, Ft Collins, CO USA
[5] Univ Guam, Mangilao, GU 96923 USA
[6] Bur Meteorol, Perth, WA, Australia
[7] NOAA, NESDIS, Satellite Anal Branch, Washington, DC 20233 USA
[8] Meteo France, RSMC La Reunion, La Reunion, France
[9] Japan Meteorol Agcy, Typhoon Ctr, RMSC Tokyo, Tokyo, Japan
[10] Joint Typhoon Warning Ctr, Honolulu, HI USA
[11] USAF, Weather Agcy, Omaha, NE USA
关键词
D O I
10.1175/BAMS-87-9-1195
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The history of meteorology has taught us that weather analysis and prediction usually advances by a series of small, progressive studies. Occasionally, however, a special body of work can accelerate this process. When that work pertains to high-impact weather events that can affect large populations, it is especially notable. In this paper we review the contributions by Vernon F. Dvorak, whose innovations using satellite observations of cloud patterns fundamentally enhanced the ability to monitor tropical cyclones on a global scale. We discuss how this original technique has progressed, and the ways in which new spaceborne instruments are being employed to complement Dvorak's original vision. © 2006 American Meteorological Society.
引用
收藏
页码:1195 / 1210
页数:16
相关论文
共 50 条
  • [21] TROPICAL CYCLONE INTENSITY CHANGE
    NUNEZ, E
    [J]. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 1980, 61 (09) : 1125 - 1125
  • [22] Tropical cyclone intensity prediction
    不详
    [J]. WEATHER, 2009, 64 (02) : 30 - 30
  • [23] Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network
    Pradhan, Ritesh
    Aygun, Ramazan S.
    Maskey, Manil
    Ramachandran, Rahul
    Cecil, Daniel J.
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2018, 27 (02) : 692 - 702
  • [24] Comparative Analysis of Machine Learning Models for Tropical Cyclone Intensity Estimation
    Liou, Yuei-An
    Le, Truong-Vinh
    [J]. REMOTE SENSING, 2024, 16 (17)
  • [25] The tropical cyclone intensity estimation based on MODIS Data: a case study
    Hao Zengzhou
    Gong Fang
    Tu Qianguang
    Pan Delu
    Wang Difeng
    [J]. REMOTE SENSING OF CLOUDS AND THE ATMOSPHERE XVI, 2011, 8177
  • [26] An Improved Conversion Relationship between Tropical Cyclone Intensity Index and Maximum Wind Speed for the Advanced Dvorak Technique in the Northwestern Pacific Ocean Using SMAP Data
    Ryu, Sumin
    Hong, Sung-Eun
    Park, Jun-Dong
    Hong, Sungwook
    [J]. REMOTE SENSING, 2020, 12 (16)
  • [27] Tropical Cyclone Intensity Estimation by Feature Extraction Techniques Using Satellite Imagery
    Kar, Chinmoy
    [J]. Informatica (Slovenia), 2024, 48 (03): : 497 - 498
  • [28] Estimation of Tropical Cyclone Intensity Using Infrared Data from a Geostationary Satellite
    Liu, Jia
    Xu, Xiaofeng
    Luo, Xiangyang
    [J]. SOLA, 2019, 15 : 189 - 192
  • [29] A Consensus Technique for Tropical Cyclone Intensity Prediction over the Western North Pacific
    Oh, Youjung
    Moon, Il-Ju
    Lee, Woojeong
    [J]. ATMOSPHERE-KOREA, 2018, 28 (03): : 291 - 303
  • [30] A PROPOSED TECHNIQUE FOR FORECASTING TROPICAL CYCLONE INTENSITY CHANGE FROM SATELLITE SOUNDINGS
    KIDDER, SQ
    [J]. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 1980, 61 (09) : 1122 - 1122