Detection of convective cells using multi-channel satellite data from FY-2

Jia LIU; Xiangyang LUO

doi:10.11834/jrs.20209018

Technology and Methodology | Views : 0 下载量: 668 CSCD: 1

PDF
Export
Share
Collection
Album

Detection of convective cells using multi-channel satellite data from FY-2
Vol. 24, Issue 8, Pages: 1023-1031(2020)
Received：29 January 2019，

Published：07 August 2020
DOI： 10.11834/jrs.20209018
稿件说明：

移动端阅览

刘佳,罗向阳.2020.风云二号多通道卫星数据对流单体检测.遥感学报,24(8): 1023-1031 DOI： 10.11834/jrs.20209018.

Liu J and Luo X Y. 2020. Detection of convective cells using multi-channel satellite data from FY-2. Journal of Remote Sensing(Chinese)，24(8): 1023-1031[DOI：10.11834/jrs.20209018] DOI：

摘要

在对流单体检测中，相邻的对流云核很难区分。为了解决这个问题，基于红外和水汽通道卫星数据，提出一种新的检测对流单体方法。首先，针对卫星数据的特点进行数据处理，去除亮温大于241 K（对流系统的基准阈值）的像元，并将其归一化到[0

1]。其次，利用H极大值变换技术提取对流核种子点，设计一种八连通域准则使相邻种子点形成聚类并按顺序进行标记，有效地区分了相邻的种子簇。然后

设计一个新的准则判断相邻的种子簇是否需要合并。最后，根据多个阈值产生的种子点设计了种子簇累加合并方法使种子点生长或者与其他相邻的种子点合并，形成最终的对流核。选取红外和水汽通道的多通道数据开展验证实验，并和雷达资料和其他方法进行对比实验，结果表明该方法可以有效地区分相邻的对流单体，在初生、成熟和消散等中尺度对流系统生命周期均准确。此外，所提方法不仅对单个对流核有效，而且对多个对流核也能准确地检测。

Abstract

Mesoscale Convective System (MCS) is the main reason of formation strong convective weather. Therefore

it is quite necessary for us to study the evolution and characteristic of MCS. Adjacent convective cells cannot be well distinguished is the main problem in convective cell detection. To address this issue

a new method is proposed based on H-maxima transform using infrared and water vapor channel data from FY-2F meteorological satellite.

Firstly

Pixels with bright temperature greater than 241K (Baseline threshold of convection system) were removed and normalized to [0

1]. Secondly

H-maxima transform technology was used to extract seed points of convective cells

and a new criterion for connected domain was designed to cluster adjacent seed points and mark them in order. Finally

a new merging method was developed to make the seed points grow or merge with adjacent seed points.

Experimental results on satellite images from infrared channel and water vapor channel show that the proposed HTC method efficient and accurate

including initial

mature and dissipation stages of Mesoscale Convective Systems lifecycle. This study showed that the proposed method has a considerable application prospect for detecting convective cells in the field of meteorology. In addition

the proposed method is not only suitable for detecting single convective cells

but also capable of multiple convective cells detection.

关键词

Keywords

references

Behrendt A , Pal S , Aoshima F , Bender M , Blyth A , Corsmeier U , Cuesta J , Dick G , Dorninger M , Flamant C , Di Girolamo P , Gorgas T , Huang Y , Kalthoff N , Khodayar S , Mannstein H , Träumner K , Wieser A and Wulfmeyer V . 2011 . Observation of convection initiation processes with a suite of state-of-the-art research instruments during COPS IOP 8b. Quarterly Journal of the Royal Meteorological Society, 137 (S 1 ): 81 – 100 [ DOI: 10.1002/qj.758 http://dx.doi.org/10.1002/qj.758 ].

Biggerstaff M I , Zounes Z , Addison Alford A , Carrie G D , Pilkey J T , Uman M A and Jordan D M . 2017 . Flash propagation and inferred charge structure relative to radar‐observed ice alignment signatures in a small Florida mesoscale convective system . Geophysical Research Letters , 44 ( 15 ): 8027 - 8036 [ DOI: 10.1002/2017GL074610 http://dx.doi.org/10.1002/2017GL074610 ]

Böing S J , Jonker H J , Siebesma A P , and Grabowski W W . 2012 . Influence of the subcloud layer on the development of a deep convective ensemble . Journal of the Atmospheric Sciences , 69 ( 9 ): 2682 – 2698 [ DOI: 10.1175/JAS-D-11-0317.1 http://dx.doi.org/10.1175/JAS-D-11-0317.1 ].

Chasteen M B , Koch S E and Parsons D B . 2019 . Multiscale Processes Enabling the Longevity and Daytime Persistence of a Nocturnal Mesoscale Convective System . Monthly Weather Review , 147 ( 2 ): 733 - 761 [ DOI: 10.1175/MWR-D-18-0233.1 http://dx.doi.org/10.1175/MWR-D-18-0233.1 ]

Cintineo J L , Pavolonis M J , Sieglaff J M and Heidinger A K . 2013 . Evolution of severe and nonsevere convection inferred from GOES-derived cloud properties . Journal of Applied Meteorology and Climatology , 52 ( 9 ): 2009 – 2023 [ DOI: 10.1175/JAMC-D-12-0330.1 http://dx.doi.org/10.1175/JAMC-D-12-0330.1 ]

Clavner M , Cotton W R , van den Heever S C , Saleeby S M and Pierce J R . 2018 . The response of a simulated mesoscale convective system to increased aerosol pollution: Part I: Precipitation intensity, distribution, and efficiency . Atmospheric Research , 199 : 193 - 208 [ DOI: 10.1016/j.atmosres.2017.08.010 http://dx.doi.org/10.1016/j.atmosres.2017.08.010 ]

Crosier J , Bower K , Choularton T , Westbrook C D , Connolly P J , Cui Z Q , Crawford I P , Capes G L , Coe H , Dorsey J R , Williams P I , Illingworth A J , Gallagher M W and Blyth A M . 2011 . Observations of ice multiplication in a weakly convective cell embedded in supercooled mid-level stratus . Atmospheric Chemistry and Physics , 11 ( 1 ): 257 – 273 [ DOI: 10.5194/acp-11-257-2011 http://dx.doi.org/10.5194/acp-11-257-2011 ]

Fiolleau T and Roca R . 2013 . An algorithm for the detection and tracking of tropical mesoscale convective systems using infrared images from geostationary satellite . IEEE Transactions on Geoscience and Remote Sensing , 51 ( 7 ): 4302 – 4315 [ DOI: 10.1109/TGRS.2012.2227762 http://dx.doi.org/10.1109/TGRS.2012.2227762 ]

Gentile S , Ferretti R and Marzano F S . 2014 . Investigating Hector Convective Development and Microphysical Structure Using High-Resolution Model Simulations, Ground-Based Radar Data, and TRMM Satellite Data . Journal of the Atmospheric Sciences , 71 ( 4 ): 1353 – 1370 [ DOI: 10.1175/JAS-D-13-0107.1 http://dx.doi.org/10.1175/JAS-D-13-0107.1 ]

Han L , Fu S , Zhao L , Zheng Y G , Wang H Q and Lin Y J . 2009 . 3D convective storm identification, tracking, and forecasting-An enhanced TITAN algorithm . Journal of atmospheric and oceanic technology , 26 ( 4 ): 719 – 732 [ DOI: 10.1175/2008JTECHA1084.1 http://dx.doi.org/10.1175/2008JTECHA1084.1 ]

Huang Y , Liu H J , Zhai J and Feng Y . 2014 . FY- 2 E Convective cloud detection method based on multi-thresholds. Remote Sensing Technology and Application , 29 ( 06 ): 915 - 922

黄勇，刘慧娟，翟菁，冯妍 . 2014 . 基于多阈值的FY2E对流云识别方法 . 遥感技术与应用， 29 ( 06 ): 915 - 922 [ DOI: 10.11873/j.issn. 1004-0323.2014.6.0915 http://dx.doi.org/10.11873/j.issn.1004-0323.2014.6.0915 ]

Lv Q Y . 2017 . Satellite detection of multi-layered clouds and associated radiative effects . Lanzhou ： Lanzhou University

吕巧谊 . 2017 . 多层云的卫星检测及其辐射效应 . 兰州：兰州大学

Météo F . 2013 . Algorithm Theoretical Basis Document for “Rapid Development Thunderstorms” (RDT-PGE11 v3.0). SAF/NWC/CDOP2/MFT/SCI/ATBD/11; Nowcasting Satellite Application Facility: Madrid, Spain .

Najman L and Talbot H . 2013 . Mathematical Morphology: From Theory to Applications . New York : John Wiley & Sons .

Novo S , Martinez D and Puentes O . 2014 . Tracking, analysis, and nowcasting of Cuban convective cells as seen by radar . Meteorological Applications , 21 ( 3 ): 585 – 595 [ DOI: 10.1002/met.1380 http://dx.doi.org/10.1002/met.1380 ]

Protat A , Rauniyar S , Kumar V and Strapp J W . 2014 . Optimizing the probability of flying in high ice water content conditions in the tropics using a regional-scale climatology of convective cell properties . Journal of Applied Meteorology and Climatology , 53 ( 11 ): 2438 – 2456 ［ DOI: 10.1175/JAMC-D-14-0002.1 http://dx.doi.org/10.1175/JAMC-D-14-0002.1 ］

Serra J and Soille P . 1994 . Mathematical morphology and its applications to image processing . New York : Springer .

Shukla B P and Pal P K . 2012 . A source apportionment approach to study the evolution of convective cells: An application to the nowcasting of convective weather systems . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 5 ( 1 ): 242 – 247 [ DOI: 10.1109/JSTARS.2011.2170661 http://dx.doi.org/10.1109/JSTARS.2011.2170661 ]

Thomas C , Corpetti T and Mémin E . 2010 . Data assimilation for convective-cell tracking on meteorological image sequences . IEEE Transactions on Geoscience and Remote Sensing , 48 ( 8 ): 3162 – 3177 [ DOI: 10.1109/TGRS.2010.2045504 http://dx.doi.org/10.1109/TGRS.2010.2045504 ]

Wang B Y , Qin D Y and Liu C C ． 2015 . Detection of rapidly developing convection using FY-2F satellite data ． Journal of Remote Sensing , 19 ( 5 ): 836 - 843

汪柏阳，覃丹宇，刘传才 . 2015 . 利用FY-2F数据检测快速发展对流 . 遥感学报 , 19 ( 05 ): 836 - 843 ［ DOI: 10.11834 /jrs.20154218 http://dx.doi.org/10.11834/jrs.20154218 ］

Zhang C G , Zhou L Z and Lin B Q . 2017 . Identification and tracking of convective cloud clusters based on satellite and radar data . Meteorological science and technology , 45 ( 3 ): 485 - 491 .

张春桂，周乐照，林炳青 . 2017 . 基于卫星和雷达资料的对流云团识别跟踪 . 气象科技， 45 ( 03 ): 485 - 491 [ DOI: 10.19517/j.1671-6345.20160313 http://dx.doi.org/10.19517/j.1671-6345.20160313 ]

Zhao W H and Shan H B . 2018 . Study of convective cloud identification based on H 2 O/IRW oberservation . Meteorological Monthly , 44 ( 06 ): 814 - 824

赵文化，单海滨 . 2018 . 基于红外窗区与水汽通道对流云团识别方法研究 . 气象 , 44 ( 06 ): 814 - 824 [ DOI: 10.7519/j.issn.1000-0526.2018.06.009 http://dx.doi.org/10.7519/j.issn.1000-0526.2018.06.009 ]

Alert me when the article has been cited

提交

Landslide susceptibility assessment of the Luding 9.5 earthquake area by fusing LT-1 ascending and descending ground deformation observations

Small wetland extraction based on Sentinel-1 time-series data and MultiRocket-RF model

Identification and analysis of forest loss drivers in Africa based on multisource time-series remote sensing products

Hyperspectral image classification network based on multiscale supervised contrastive learning

Comparative verification of cloud particle effective radius at cloud top by satellite retrieval and airborne measurement