极地研究

极地研究 ›› 2024, Vol. 36 ›› Issue (4): 607-624.DOI: 10.13679/j.jdyj.20230038

• 研究论文 • 上一篇    下一篇

北极多源海冰厚度数据产品的定量分析与综合评估

李彤彤1,汪杨骏2,吴鸿乾3,刘科峰4,陈希4,李明4,李洪臣4   

  1. 1江苏海洋大学海洋技术与测绘学院, 江苏 连云港 222000; 
    2国防科技大学前沿交叉学科学院, 江苏 南京 410000; 
    3国防科技大学系统工程学院, 江苏 南京 410000; 
    4国防科技大学气象海洋学院, 江苏 南京 410000

  • 收稿日期:2023-07-03 修回日期:2023-09-20 出版日期:2024-12-31 发布日期:2025-01-15
  • 通讯作者: 汪杨骏
  • 作者简介:李彤彤, 女, 1998年生。硕士, 从事北极海冰资料评估与融合研究。Email:sunning_tt@163.com
  • 基金资助:
    湖南省自然科学基金

Quantitative analysis and comprehensive evaluation of multi-source Arctic sea ice thickness data products

LI Tongtong1, WANG Yangjun2, WU Hongqian3, LIU Kefeng2, CHEN Xi2, LI Ming2, LI Hongchen4   

  1. 1College of Marine Technology and Surveying, Jiangsu Ocean University, Lianyungang 222000, China;
    2Academy of Frontier Interdisciplinary Studies, National University of Defense Technology, Nanjing 410000, China; 
    3College of Systems Engineering, National University of Defense Technology, Nanjing 410000, China;
    4College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 410000, China
  • Received:2023-07-03 Revised:2023-09-20 Online:2024-12-31 Published:2025-01-15
  • Contact: 杨骏 汪

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摘要： 北极海冰厚度的研究对探究全球气候变化和开辟北极航道具有重要意义。虽然卫星遥感和数值模拟技术已广泛应用于海冰厚度的研究, 但相比于海冰密集度的研究, 不同海冰厚度数据产品在时空上存在着较大差异。因此, 本文为了客观定量地衡量不同海冰厚度数据产品的准确性和适用性, 提出1种海冰厚度数据产品的综合质量评估框架。该框架提取了2010—2020年不同海冰厚度产品的数字统计特征、局地空间分布和时间变化规律, 构建9类评估指标, 通过与实测数据的对比分析, 实现海冰厚度产品的多维度定量评估。结果表明, (1) CryoSat-2和SMOS(CS2SMOS)产品在统计特征相关性、空间结构相似度、年际变化偏差、逐月变化相关性和逐月变化偏差等 5 个指标中表现突出; (2)PIOMAS 产品最能反映冬半年海冰厚度随时间的变化特征和最优的年际变化相关性; (3) CPOM产品在特征统计偏差、空间分布相关性和空间分布偏差等3个指标表现最优。以上研究结果可用于海冰厚度数据产品融合, 可为不同海冰厚度数据产品在不同时空进行赋权, 因而提高海冰厚度数据产品融合的客观性和可靠性。

关键词: 多源数据, 海冰厚度, 评估, 北极

Abstract: The study of Arctic sea ice thickness is of significant importance for understanding global climate change and exploring Arctic shipping routes. While satellite remote sensing and numerical simulation techniques have been widely employed in sea ice thickness studies, there are significant spatiotemporal discrepancies among various sea ice thickness data products, unlike the research on sea ice concentration. Therefore, this paper proposes a comprehensive quality assessment framework for sea ice thickness data products to objectively and quantitatively evaluate their accuracy and applicability. The framework extracts digital statistical features, local spatial distributions, and temporal variation pattern of different sea ice thickness products from 2010 to 2020, constructing nine evaluation indicators. Through comparative analysis with observed data, multidimensional quantitative evaluation of sea ice thickness products is achieved. The results indicate that: (1) CryoSat-2 and SMOS (CS2SMOS) products excel in five indicators, including statistical feature correlation, spatial structure similarity, interannual variation deviation, monthly change correlation, and monthly change deviation; (2) PIOMAS product best reflects the temporal characteristics of sea ice thickness during the winter half-year and exhibits optimal interannual variation correlation; (3) CPOM product performs best in three indicators, including feature statistical deviation, spatial distribution correlation, and spatial distribution deviation. The research findings can be used for the fusion of sea ice thickness data products, enabling the objective and reliable weighting of different sea ice thickness data products in different spatiotemporal contexts, thereby enhancing the objectivity and reliability of sea ice thickness data product fusion.

Key words: multi-source data, sea ice thickness, assessment, Arctic