Tropical Cyclone Characterization via Nocturnal Low-Light Visible Illumination

被引:6
|
作者
Hawkins, Jeffrey D. [1 ]
Solbrig, Jeremy E. [2 ]
Miller, Steven D. [2 ]
Surratt, Melinda [1 ]
Lee, Thomas F. [1 ]
Bankert, Richard L. [1 ]
Richardson, Kim [1 ]
机构
[1] Naval Res Lab, Marine Meteorol Div, Monterey, CA USA
[2] Cooperat Inst Res Atmosphere, Ft Collins, CO USA
关键词
VIIRS DAY/NIGHT BAND; SATELLITE IMAGERY; GLOBAL DISTRIBUTION; OBJECTIVE SCHEME; HOT TOWERS; INTENSITY; MODELS; CAPABILITIES; CALIBRATION; RADIOMETER;
D O I
10.1175/BAMS-D-16-0281.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Global monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day-night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible-near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program's (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2-3 times the moon's lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.
引用
收藏
页码:2351 / 2366
页数:16
相关论文
共 50 条
  • [1] LIME: Low-Light Image Enhancement via Illumination Map Estimation
    Guo, Xiaojie
    Li, Yu
    Ling, Haibin
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017, 26 (02) : 982 - 993
  • [2] Low-light image enhancement via illumination optimization and color correction
    Zhang, Wenbo
    Xu, Liang
    Wu, Jianjun
    Huang, Wei
    Shi, Xiaofan
    Li, Yanli
    COMPUTERS & GRAPHICS-UK, 2025, 126
  • [3] Low-Light Image Enhancement via Implicit Priors Regularized Illumination Optimization
    Ma, Qianting
    Wang, Yang
    Zeng, Tieyong
    IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, 2023, 9 : 944 - 953
  • [4] Unsupervised Illumination Adaptation for Low-Light Vision
    Wang, Wenjing
    Luo, Rundong
    Yang, Wenhan
    Liu, Jiaying
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2024, 46 (09) : 5951 - 5966
  • [5] Illumination enhancement discriminator and compensation attention based low-light visible and infrared image fusion
    Zhang, Xingfei
    Liu, Gang
    Wang, Gaoqiang
    Bavirisetti, Durga Prasad
    OPTICS AND LASERS IN ENGINEERING, 2025, 185
  • [6] Low-light image enhancement with a refined illumination map
    Shijie Hao
    Zhuang Feng
    Yanrong Guo
    Multimedia Tools and Applications, 2018, 77 : 29639 - 29650
  • [7] Illumination-Adaptive Unpaired Low-Light Enhancement
    Kandula, Praveen
    Suin, Maitreya
    Rajagopalan, A. N.
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 33 (08) : 3726 - 3736
  • [8] Low-light image enhancement with a refined illumination map
    Hao, Shijie
    Feng, Zhuang
    Guo, Yanrong
    MULTIMEDIA TOOLS AND APPLICATIONS, 2018, 77 (22) : 29639 - 29650
  • [9] Adaptive Illumination Estimation for Low-Light Image Enhancement
    Li, Lan
    Peng, Wen-Hao
    Duan, Zhao -Peng
    Pu, Sha-Sha
    ENGINEERING LETTERS, 2024, 32 (03) : 531 - 540
  • [10] Perceptive low-light image enhancement via multi-layer illumination decomposition model
    Yahong Wu
    Jieying Zheng
    Wanru Song
    Feng Liu
    Multimedia Tools and Applications, 2022, 81 : 40905 - 40929