Früher|Nächster

Research on retrieval algorithm of terahertz ice cloud sounding based on neural network

  • role： First author第一作者
  • Affiliation：

    Huazhong University of Science and Technology, Wuhan 430074, China

  • Email：chenke@hust.edu.cn
  • Introduction：陈柯，研究方向为微波大气遥感、微波辐射计研制。E-mail： chenke@hust.edu.cn
CHEN Ke1,  
  • Affiliation：

    Huazhong University of Science and Technology, Wuhan 430074, China

ZHANG Lan1,  
  • Affiliation：

    Wuhan Maritime Communication Research Institute, Wuhan 430079, China

ZHANG Youming2,  
  • Affiliation：

    Huazhong University of Science and Technology, Wuhan 430074, China

DONG Shanbin1,  
  • Affiliation：

    Huazhong University of Science and Technology, Wuhan 430074, China

LIU Yan1,  
  • Affiliation：

    Innovation Center for FengYun Meteorological Satellite (FYSIC), Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China

WU Qiong3,  
  • role： Corresponding author通信作者
  • Affiliation：

    Innovation Center for FengYun Meteorological Satellite (FYSIC), Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China

  • Email：shangjian@cma.gov.cn
  • Introduction：商建，研究方向为微波遥感探测及卫星图像定位。E-mail： shangjian@cma.gov.cn
SHANG Jian3*

Resümee

Terahertz band has a number of potential advantages that complement existing visible and infrared techniques in ice cloud sounding application, but treating various phase ice particles (mainly ice and graupel) as single ice particles is a major limitation of current terahertz ice cloud retrieval algorithms. In this paper, a pre-classified neural network algorithm based on the terahertz radiation characteristics of ice cloud is proposed, which is able to retrieve the physical parameters of ice and graupel particles, respectively. The algorithm first uses a pre-classified neural network to retrieve the density profiles of graupel particles separately from the 183 GHz band brightness temperature data. The retrieved graupel profiles are then used as a priori constraint to calculate the higher frequency band brightness temperature difference due to ice particles only. Finally, another pre-classified neural network is used to retrieve the density profiles of ice particles separately from the preceding terahertz brightness temperature difference data. The proposed algorithm are evaluated through the end to end simulation experiments. Firstly, a hybrid ice cloud dataset including ice and graupel particle parameters is built based on the numerical weather prediction (NWP) model and the actual observation data. Then the synthetic ice cloud brightness temperature data from 183—874 GHz (i.e. 183 GHz, 243 GHz, 325 GHz, 448 GHz, 664 GHz and 874 GHz) are generated through Discrete-Ordinate Tangent Linear Radiative Transfer (DOTLRT) radiative transfer model with the hybrid ice cloud dataset. Finally, the parameters of ice and graupel are retrieved by the proposed algorithm from the simulated brightness temperature data, and compared with the input parameters to assess the retrieval accuracy. The simulation experiments show that the average Root Mean Square Errors (RMSE) of the retrieved IWP and GWP are 8.97 g/m2 and 10.90 g/m2 respectively, and the average RMSE of the retrieved I_Dme and G_Dme are 7.54 μm and 25.38 μm respectively, and the average RMSE of the retrieved I_Zme and G_Zme are 309.21 m and 513.62 m respectively, and the retrieved density profiles of ice and graupel particles also have high accuracy. The results indicate that the proposed algorithm can retrieve the total path amount, equivalent ice particle size, and equivalent ice cloud height and density profile of ice and graupel particles respectively with high accuracy, which is more consist with the real condition of ice cloud than the current retrieval algorithm.

Schlüsselwort

Terahertz;ice cloud sounding;neural network;ice and graupel particles;retrieval of ice cloud parameter

References

  1. 1.
    Boukabara S A, Garrett K, Chen W C, Iturbide-Sanchez F, Grassotti C, Kongoli C, Chen R Y, Liu Q H, Yan B H, Weng F Z, Ferraro R, Kleespies T J and Meng H. 2011. MiRS: an all-weather 1DVAR satellite data assimilation and retrieval system. IEEE Transactions on Geoscience and Remote Sensing, 49(9): 3249-3272.
  2. 2.
    Brath M, Fox S, Eriksson P, Harlow R, Burgdorf M, Buehler S. 2018. Retrieval of an ice water path over the ocean from ISMAR and MARSS millimeter and submillimeter brightness temperatures. Atmospheric Measurement Techniques, 11(1): 611-632 [doi.org/]
  3. 3.
    Buehler S A, Jiménez C, Evans K F, Eriksson P, Rydberg B, Heymsfield A J, Stubenrauch C J, Lohmann U, Emde C, John V O, Sreerekha T R and Davis C P. 2007. A concept for a satellite mission to measure cloud ice water path, ice particle size, and cloud altitude. Quarterly Journal of the Royal Meteorological Society, 133(S2): 109-128
  4. 4.
    Donovan D P. 2003. Ice-cloud effective particle size parameterization based on combined lidar, radar reflectivity, and mean Doppler velocity measurements. Journal of Geophysical Research: Atmospheres, 108(D18): 4573
  5. 5.
    Evans K F, Wang J R, Racette P E, Heymsfield G and Li L H. 2005. Ice cloud retrievals and analysis with the compact scanning submillimeter imaging radiometer and the cloud radar system during CRYSTAL FACE. Journal of Applied Meteorology and Climatology, 44(6): 839-859
  6. 6.
    Evans K F, Wang J R, O’C Starr D, Heymsfield G, Li L, Tian L, Lawson R P, Heymsfield A J and Bansemer A. 2012. Ice hydrometeor profile retrieval algorithm for high-frequency microwave radiometers: application to the CoSSIR instrument during TC4. Atmospheric Measurement Techniques, 5(9): 2277-2306
  7. 7.
    Garrett T J, Navarro B C, Twohy C H, Jensen E J, Baumgardner D G, Bui P T, Gerber H, Herman R L, Heymsfield A J, Lawson P, Minnis P, Nguyen L, Poellot M, Pope S K, Valero F P J and Weinstock E M. 2005. Evolution of a Florida cirrus anvil. Journal of the Atmospheric Sciences, 62(7): 2352-2372
  8. 8.
    Heymsfield A J and Iaquinta J. 2000. Cirrus crystal terminal velocities. Journal of the Atmospheric Sciences, 57(7): 916-938
  9. 9.
    Jiménez C, Buehler S A, Rydberg B, Eriksson P and Evans K F. 2007. Performance simulations for a submillimetre-wave satellite instrument to measure cloud ice. Quarterly Journal of the Royal Meteorological Society, 133(S2): 129-149
  10. 10.
    Kangas V, D'Addio S, Klein U, Loiselet M, Mason G, Orlhac J C, Gonzalez R, Bergada M, Brandt M and Thomas B. 2014. Ice cloud imager instrument for MetOp Second Generation//2014 13th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment. Pasadena, CA: IEEE: 228-231
  11. 11.
    Liou K N. 1986. Influence of cirrus clouds on ceather and climate processes: a global perspective. Monthly Weather Review, 114(6): 1167-1199
  12. 12.
    Liu L, Weng C S, Li S L, Hu S, Ye J, Dou F L, Shang J. 2020. Research status and progress of terahertz wave passive remote sensing of ice clouds. Advances in Geoscience, 35(12): 1211-1221
  13. 13.
    Mendrok J, Wu D L, Buehler S A, Jimenez C and Kasai Y. 2009. Sub-millimeter wave radiometer for observation of cloud ice: a proposal for Japanese mission//Proceedings Volume 7474, Sensors, Systems, and Next-Generation Satellites XIII. Berlin: SPIE: 249-257
  14. 14.
    Piyush D N, Goyal J, Srinivasan J. 2017. Retrieval of cloud ice water path using SAPHIR on board Megha-Tropiques over the tropical ocean. Advances in Geoscience, 59(7): 1895-1906 [doi.org/]
  15. 15.
    Voronovich A G, Gasiewski A J and Weber B L. 2004. A fast multistream scattering-based Jacobian for microwave radiance assimilation. IEEE Transactions on Geoscience and Remote Sensing, 42(8): 1749-1761
  16. 16.
    Waliser D E, Li J L F, Woods C P, Austin R T, Bacmeister J, Chern J, Del Genio A, Jiang J H, Kuang Z M, Meng H, Minnis P, Platnick S, Rossow W B, Stephens G L, Sun-Mack S, Tao W K, Tompkins A M, Vane D G, Walker C and Wu D. 2009. Cloud ice: a climate model challenge with signs and expectations of progress. Journal of Geophysical Research: Atmospheres, 114(D8): D00A21
  17. 17.
    Wang D, Prigent C, Aires F, Jimenez C. 2016. A statistical retrieval of cloud parameters for the millimeter wave ice cloud imager on board MetOp-SG. IEEE Access, 5: 4057-4076
  18. 18.
    Wang H, Duan C D, Lü R C, Lei H W, Zhu Z B and Chen G. 2017. Development of space borne Terahertz ice clouds measurement technology and existing technical problems. Journal of Terahertz Science and Electronic Information Technology, 15(5): 722-727
  19. 19.
    Weng F, Zou X, Wang X, Yang S, Goldberg M. 2012. Introduction to Suomi national polar-orbiting partnership advanced technology microwave sounder for numerical weather prediction and tropical cyclone applications. Journal of Geophysical Research Atmospheres, 117(D19112): 1-14 []
  20. 20.
    Zhang R, Wang Y B. 2016. Research on machine learning and its algorithm and development, 23(2): 10-24
  21. 21.
    Zhang X, Hu W D, Liu R T, Si W K, Li Y D, Liu Y L and Ligthart L P. 2018. 874 GHz ice cloud detector design based on Cubesat platform. Aerospace Shanghai, 35(2): 144-150

Lesen Sie die ganze Passage

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