Anterior|Siguiente

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

  • role： First author第一作者
  • Affiliation：

    School of Information Technology, Shangqiu Normal University, Shangqiu 476000, China

    Zhengzhou Information Science and Technology Institute, State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450000, China

  • Email：sqsylj@126.com
  • Introduction：刘佳，1985年生，男，博士，研究方向为遥感数据处理和模式识别。E-mail: sqsylj@126.com
LIU Jia12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Zhengzhou Information Science and Technology Institute, State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450000, China

  • Email：luoxy_ieu@sina.com
  • Introduction：罗向阳，1978年生，男，教授，研究方向为网络与信息安全。E-mail: luoxy_ieu@sina.com
LUO Xiangyang2*

resumen

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.

palabra clave

remote sensing;convective cell;convective core;H-maxima transform;clustering;FY-2;seed point

References

  1. 1.
    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(S1): 81–100.
  2. 2.
    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
  3. 3.
    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.
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    Huang Y, Liu H J, Zhai J and Feng Y. 2014. FY-2E Convective cloud detection method based on multi-thresholds. Remote Sensing Technology and Application, 29(06):915-922
  12. 12.
    Lv Q Y. 2017. Satellite detection of multi-layered clouds and associated radiative effects. Lanzhou：Lanzhou University
  13. 13.
    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.
  14. 14.
    Najman L and Talbot H. 2013. Mathematical Morphology: From Theory to Applications. New York: John Wiley & Sons.
  15. 15.
    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
  16. 16.
    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: ］
  17. 17.
    Serra J and Soille P. 1994. Mathematical morphology and its applications to image processing. New York: Springer.
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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.
  22. 22.
    Zhao W H and Shan H B. 2018. Study of convective cloud identification based on H2O/IRW oberservation. Meteorological Monthly, 44(06): 814-824

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website