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

CHEN Ke; ZHANG Lan; ZHANG Youming; DONG Shanbin; LIU Yan; WU Qiong; SHANG Jian

doi:10.11834/jrs.20210110

Remote Sensing Intelligent Interpretation | Views : 0 下载量: 211 CSCD: 0 更多指标

R-PDF
PDF
Export
Share
Collection
Album

Research on retrieval algorithm of terahertz ice cloud sounding based on neural network
Vol. 26, Issue 10, Pages: 2043-2059(2022)
Published： 07 October 2022 ，
DOI： 10.11834/jrs.20210110

扫描看全文

陈柯，张兰，张幼明，董杉彬，刘艳，吴琼，商建.2022.基于神经网络的太赫兹冰云探测反演算法研究.遥感学报，26（10）： 2043-2059

Chen K，Zhang L，Zhang Y M，Dong S B，Liu Y，WU Q and Shang J. 2022. Research on retrieval algorithm of terahertz ice cloud sounding based on neural network. National Remote Sensing Bulletin， 26（10）：2043-2059
陈柯，张兰，张幼明，董杉彬，刘艳，吴琼，商建.2022.基于神经网络的太赫兹冰云探测反演算法研究.遥感学报，26（10）： 2043-2059 DOI： 10.11834/jrs.20210110.

Chen K，Zhang L，Zhang Y M，Dong S B，Liu Y，WU Q and Shang J. 2022. Research on retrieval algorithm of terahertz ice cloud sounding based on neural network. National Remote Sensing Bulletin， 26（10）：2043-2059 DOI： 10.11834/jrs.20210110.

摘要

太赫兹频段在冰云探测上具有独特优势，但是目前的太赫兹冰云反演算法将不同种类冰相粒子（主要是冰和霰）视为冰粒子统一计算。本文根据冰云太赫兹辐射特性实现了一种预分类的神经网络算法，能够从太赫兹亮温中分别反演得到冰、霰两种粒子的统计参数和廓线分布。首先，基于WRF数值模式和ATMS载荷真实观测的冰云霰数据构建了包含冰、霰粒子密度廓线的混合冰云数据库，然后，使用DOTLRT辐射传输模式模拟183 GHz、243 GHz、325 GHz、448 GHz、664 GHz和874 GHz这6个频段的星载太赫兹冰云探测亮温，最后，开展冰云参数探测仿真试验，验证反演算法性能。仿真试验中反演得到的冰和霰的路径总量均方根误差分别为8.97 g/m

和10.90 g/m

，等效粒径均方根误差分别为7.54 μm和25.38 μm，反演的冰、霰密度廓线也具有较高的精度。研究结果表明本文算法能够以较好的精度从多频太赫兹冰云探测亮温数据中分别反演得到冰、霰两种粒子的路径总量、等效粒径、等效云高和密度廓线，突破现有研究仅仅计算单一冰粒子的局限，更加符合冰云真实情况。

Abstract

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/m

and 10.90 g/m

respectively

and the average RMSE of the retrieved I_D

and G_D

are 7.54 μm and 25.38 μm respectively

and the average RMSE of the retrieved I_Z

and G_Z

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.

关键词

太赫兹冰云探测神经网络冰和霰粒子冰云参数反演

Keywords

terahertzice cloud soundingneural networkice and graupel particlesretrieval of ice cloud parameter

references

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. [DOI: 10.1109/TGRS.2011.2158438http://dx.doi.org/10.1109/TGRS.2011.2158438]

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/10.5194/amt-11-611-2018http://dx.doi.org/10.5194/amt-11-611-2018]

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 [DOI: 10.1002/qj.143http://dx.doi.org/10.1002/qj.143]

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 [DOI: 10.1029/2003JD003469http://dx.doi.org/10.1029/2003JD003469]

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 [DOI: 10.1175/JAM2250.1http://dx.doi.org/10.1175/JAM2250.1]

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 [DOI: 10.5194/amt-5-2277-2012http://dx.doi.org/10.5194/amt-5-2277-2012]

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 [DOI: 10.1175/JAS3495.1http://dx.doi.org/10.1175/JAS3495.1]

Heymsfield A J and Iaquinta J. 2000. Cirrus crystal terminal velocities. Journal of the Atmospheric Sciences, 57(7): 916-938 [DOI: 10.1175/1520-0469(2000)057<0916:CCTV>2.0.CO;2http://dx.doi.org/10.1175/1520-0469(2000)057<0916:CCTV>2.0.CO;2]

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 [DOI: 10.1002/qj.134http://dx.doi.org/10.1002/qj.134]

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 [DOI: 10.1109/MicroRad.2014.6878946http://dx.doi.org/10.1109/MicroRad.2014.6878946]

Liou K N. 1986. Influence of cirrus clouds on ceather and climate processes: a global perspective. Monthly Weather Review, 114(6): 1167-1199 [DOI: 10.1175/1520-0493(1986)114<1167:IOCCOW>2.0.CO;2http://dx.doi.org/10.1175/1520-0493(1986)114<1167:IOCCOW>2.0.CO;2]

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

刘磊, 翁陈思, 李书磊, 胡帅, 叶进, 窦芳丽，商建. 2017. 太赫兹波被动遥感冰云研究现状及进展. 地球科学进展, 35(12): 1211-1221 [DOI: 10.11867/j.issn.1001-8166.2020.103http://dx.doi.org/10.11867/j.issn.1001-8166.2020.103]

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 [DOI: 10.1117/12.830704http://dx.doi.org/10.1117/12.830704]

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/10.1016/j.asr.2017.01.022http://dx.doi.org/10.1016/j.asr.2017.01.022]

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 [DOI: 10.1109/TGRS.2004.830637http://dx.doi.org/10.1109/TGRS.2004.830637]

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 [DOI: 10.1029/2008JD010015http://dx.doi.org/10.1029/2008JD010015]

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 [doi: 10.1109/ACCESS.2016.2625742http://dx.doi.org/10.1109/ACCESS.2016.2625742]

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

王虎, 段崇棣, 吕容川, 雷红文, 朱忠博, 陈刚. 2017. 星载太赫兹冰云探测技术发展和面临问题. 太赫兹科学与电子信息学报, 15(5): 722-727 [DOI: 10.11805/TKYDA201705.0722http://dx.doi.org/10.11805/TKYDA201705.0722]

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 [https://doi.org/10.1029/2012JD018144https://doi.org/10.1029/2012JD018144]

Zhang R, Wang Y B. 2016. Research on machine learning and its algorithm and development, 23(2): 10-24

张润, 王永滨. 2016. 机器学习及其算法和发展研究. 中国传媒大学学报自然科学版, 23(2): 10-24 [DOI:10.16196/j.cnki.issn.1673-4793.2016.02.002http://dx.doi.org/10.16196/j.cnki.issn.1673-4793.2016.02.002]

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

张欣, 胡伟东, 刘瑞婷, 司炜康, 李雅德, 刘芫喽, Ligthart L P. 2018. 基于Cubesat平台的874 GHz冰云探测仪设计. 上海航天, 35(2): 144-150 [DOI: 10.19328/j.cnki.1006-1630.2018.02.018http://dx.doi.org/10.19328/j.cnki.1006-1630.2018.02.018]

Alert me when the article has been cited

提交

FAPAR retrieval from GF-5 hyperspectral images based on unified BRDF model

Spectral unmixing method considering endmember variability of vegetation

Cloud detection for FY-3D MERSI Ⅱ images combine radiative transfer simulation and shallow neural network

Terahertz radiation and scattering characteristics of ice cloud and sounding parameter design

Surface microwave scattering model evaluation and soil moisture retrieval based on ground-based radar data