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
  • [31] Low-light image enhancement for infrared and visible image fusion
    Zhou, Yiqiao
    Xie, Lisiqi
    He, Kangjian
    Xu, Dan
    Tao, Dapeng
    Lin, Xu
    IET IMAGE PROCESSING, 2023, 17 (11) : 3216 - 3234
  • [32] Contrast Enhanced Low-light Visible and Infrared Image Fusion
    Teku, Sandhya Kumari
    Rao, S. Koteswara
    Prabha, I. Santhi
    DEFENCE SCIENCE JOURNAL, 2016, 66 (03) : 266 - 271
  • [33] SLAM in Low-Light Environments Based on Infrared-Visible Light Fusion
    Wang, Haiwei
    Gao, Chenqi
    Gao, Tianyu
    Hu, Jinwen
    Xu, Zhao
    Han, Junwei
    Zhu, Yan
    Wu, Yong
    2024 IEEE 18TH INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION, ICCA 2024, 2024, : 868 - 873
  • [34] Enabling on-aircraft visible-light communications in low-light conditions
    Tagliaferri, D.
    Capsoni, C.
    ELECTRONICS LETTERS, 2019, 55 (05) : 274 - 275
  • [35] ITRE: Low-light image enhancement based on illumination transmission ratio estimation
    Wang, Yu
    Wang, Yihong
    Liu, Tong
    Li, Jinyu
    Sui, Xiubao
    Chen, Qian
    KNOWLEDGE-BASED SYSTEMS, 2024, 303
  • [36] LOW-LIGHT IMAGE ENHANCEMENT VIA FEATURE RESTORATION
    Yang, Yang
    Zhang, Yonghua
    Guo, Xiaojie
    2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 2440 - 2444
  • [37] Low-light image enhancement with joint illumination and noise data distribution transformation
    Guo, Sheng
    Wang, Wei
    Wang, Xiao
    Xu, Xin
    VISUAL COMPUTER, 2023, 39 (04): : 1363 - 1374
  • [38] Illumination-Aware Image Quality Assessment for Enhanced Low-Light Image
    Yao, Sigan
    Zhu, Yiqin
    Liang, Lingyu
    Wang, Tao
    PATTERN RECOGNITION AND COMPUTER VISION,, PT III, 2021, 13021 : 226 - 237
  • [39] Low-Light Image Enhancement With Regularized Illumination Optimization and Deep Noise Suppression
    Guo, Yu
    Lu, Yuxu
    Liu, Ryan Wen
    Yang, Meifang
    Chui, Kwok Tai
    IEEE ACCESS, 2020, 8 (145297-145315): : 145297 - 145315
  • [40] Content-illumination coupling guided low-light image enhancement network
    Zhao, Ruini
    Xie, Meilin
    Feng, Xubin
    Su, Xiuqin
    Zhang, Huiming
    Yang, Wei
    SCIENTIFIC REPORTS, 2024, 14 (01)