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冰川冻土 ›› 2022, Vol. 44 ›› Issue (5): 1501-1512.doi: 10.7522/j.issn.1000-0240.2022.0133

• 冰工程专栏 • 上一篇    

基于NoSREx实验的积雪演化及微波辐射分析研究

周静恬1,2,3(), 邱玉宝1,2,4(), 黄琳1,2,3, LEMMETYINEN Juha4,5, 石利娟1,2,4, 李青寰1,2,4, 施建成6   

  1. 1.中国科学院 空天信息创新研究院 数字地球重点实验室,北京 100094
    2.可持续发展大数据国际研究中心,北京 100094
    3.中国 科学院大学,北京 100049
    4.中国科学院 空天信息创新研究院-芬兰气象研究所 北极观测联合研究中心(JRC-AO),芬兰 索丹屈莱 FI -99660
    5.芬兰气象研究所 北极空间中心,芬兰 赫尔辛基 FI -00560
    6.中国科学院 国家空间科学中心,北京 100190
  • 收稿日期:2021-02-09 修回日期:2021-09-01 出版日期:2022-10-25 发布日期:2022-11-05
  • 通讯作者: 邱玉宝 E-mail:zhoujt@aircas.ac.cn;qiuyb@aircas.ac.cn
  • 作者简介:周静恬,硕士研究生,主要从事微波遥感应用研究. E-mail: zhoujt@aircas.ac.cn
  • 基金资助:
    国家重点研发计划“政府间国际科技创新合作”重点专项(2017YFE0111700);中国科学院战略性先导科技专项(XDA19070201);中国科学院国际合作局对外合作重点项目(131211KYSB20170041);国家自然科学基金项目(41371351)

Analysis of the evolution of snow cover and microwave irradiation based on Nordic Snow Radar Experiment

Jingtian ZHOU1,2,3(), Yubao QIU1,2,4(), Lin HUANG1,2,3, Juha LEMMETYINEN4,5, Lijuan SHI1,2,4, Qinghuan LI1,2,4, Jiancheng SHI6   

  1. 1.Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
    2.International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,China
    3.University of Chinese Academy of Sciences,Beijing 100049,China
    4.Joint Research Center for Arctic Observations,Aerospace Information Research Institute,Chinese Academy of Sciences and Arctic Space Center,Finnish Meteorological Institute (JRC-AO),Chinese Academy of Sciences,Sodankyl? FI -99660,Finland
    5.Finnish Meteorological Institute,Helsinki FI -00560,Finland
    6.National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2021-02-09 Revised:2021-09-01 Online:2022-10-25 Published:2022-11-05
  • Contact: Yubao QIU E-mail:zhoujt@aircas.ac.cn;qiuyb@aircas.ac.cn

摘要:

在被动微波雪水当量反演中,积雪物理参数随时间的变化特征影响着反演精度,为理解积雪随时间演化的特征及其对微波辐射亮温的影响,本研究选用2009—2013年北欧积雪实验(Nordic Snow Radar Experiment, NoSREx)积雪地面观测和微波辐射测量数据,通过雪深和温度把雪期分为积累期(10月—次年2月)、稳定期(2—4月)和消融期(4—5月),发现各个雪期的积雪演化特征为:雪颗粒形状在积累期前期以融态颗粒(Melt Forms, MF)为主,积累期后期和稳定期以圆形颗粒、片状颗粒、深霜为主,消融期以MF为主;整个雪季底层雪粒径从小变大再变小的过程,粒径最大值出现在稳定期的2至3月,约为2.5~4.0 mm,均出现在近地表雪层,而表层粒径较小且较为稳定。通过雪深和微波亮度差(18~37 GHz)的关系分析,表明亮温差在不同雪期对于雪深的依赖关系不同,在积累期和稳定期,雪深变化与亮温差变化具有明显的正相关;在消融期由于积雪融化的影响,其相关性较差;基于多层积雪微波辐射模型(MEMLS)构建了一维微波辐射模拟环境,模拟表明MEMLS模型在3个雪期的垂直极化10.65 GHz和18.7 GHz模拟结果较37 GHz和90 GHz更好;10.65 GHz V极化在入射角为50°且稳定期时,微波亮温模拟均方根误差(RMSE结果最小,为2.49 K。3个雪期90 GHz模拟结果水平极化优于垂直极化,由于受表层积雪变化影响,90 GHz模拟结果较不稳定,尤其是消融期时,RMSE最小也达到了42.7 K。本研究有助于理解积雪随时间演化的特征及其对微波辐射模拟的影响,表明在被动微波雪水当量反演算法中,针对不同积雪期需要考虑积雪演化动态过程。

关键词: 积雪, 微波辐射亮温, NoSREx实验, MEMLS模型

Abstract:

In passive microwave snow water equivalent retrieval algorithms, the change characteristics of snow cover physical parameters over time affect the inversion accuracy. This paper uses Nordic Snow Radar Experiment (NoSREx) datasets from 2009 to 2013 to study the snow cover evolution characteristics over time and its influence on the microwave brightness temperature. Based on the change of snow depth and temperature, the snow period in the northern arctic inland regions is divided into the snow accumulation period (October to February), the snow stable period (February to April) and the snow melting period (April to May). Firstly, characteristics of the snow evolution process in different periods are analyzed. The shape of snow particles is mainly melting forms (MF) in the early accumulation period, and rounded grains (RG), faceted crystals (FC), and depth hoar (DH) in the late accumulation period and stable period, the snow melting period is dominated by MF; from the accumulation period to the snow melting period, the snow particles in the bottom layer will grow from small to large and then small. The maximum particle size appears in the annual stable period (February to March), the value is about 2.5~4.0 mm, all appear in the layer near the ground surface, the surface particle size is always small and relatively stable. Secondly, through the analysis of the relationship between snow depth and microwave brightness difference (18 and 37 GHz), the brightness temperature difference has different dependence on snow depth in different snow accumulation periods. During the accumulation period and the stable period, the changes of snow depth and the brightness temperature difference are positively similar; during the melting period, the correlation is not obvious due to the influence of snow melting. Thirdly, combined with the simultaneous observation of ground-based radiometers and the Microwave Emission Model of Layered Snowpacks (MEMLS), a forward one-dimensional microwave simulation environment was constructed, the results showed that three periods of 10.65 GHz and 18.7 GHz and the simulation results under vertical polarization are better at 37 GHz and 90 GHz; under 10.65 GHz, at the stable period,vertical polarization and an incident angle of 50°, the microwave brightness temperature simulation results are the best, RMSE is 2.49 K; compared with vertical polarization, the simulation results under three periods of 90 GHz are better under horizontal polarization; due to changes in the surface snow, the 90 GHz simulation results are unstable, especially during the snow melting period, the minimum RMSE reached 42.7 K. This research is helpful to understand the characteristics of snow cover evolution over time and its influence on microwave radiation simulation. It shows that in the passive microwave snow water equivalent retrieval algorithm, the dynamic process of snow cover evolution needs to be considered in different snow periods.

Key words: snow, microwave brightness temperature, NoSREx, MEMLS

中图分类号: 

  • P436.63+5