极地研究

极地研究 ›› 2026, Vol. 38 ›› Issue (3): 400-412.DOI: 10.13679/j.jdyj.20240085

• 研究论文 • 上一篇    

综合GNSS-R和ERA5的雪水当量估计方法研究

陈芳霖1  常亮1  黄婷2   

  1. 1上海海洋大学海洋科学与生态环境学院, 上海 201306;
    2上海海洋大学研究生院, 上海 201306
  • 收稿日期:2024-09-24 修回日期:2025-02-07 出版日期:2026-09-30 发布日期:2026-10-22
  • 通讯作者: 黄婷
  • 基金资助:
    国家自然科学基金(42174016)资助

Snow water equivalent estimation method based on the GNSS-R and ERA5

CHEN Fanglin1, CHANG Liang1, HUANG Ting2   

  1. 1College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;
    2Graduate School of Shanghai Ocean University, Shanghai 201306, China
  • Received:2024-09-24 Revised:2025-02-07 Online:2026-09-30 Published:2026-10-22

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摘要： 全球导航卫星系统反射测量(GNSS-R)技术已成为雪水当量估计的重要手段之一, 但基于积雪密度模型方法进行GNSS-R雪水当量反演时, 通常会受到全球导航卫星系统(GNSS)测站周围未配置气象观测仪器的影响。本文提出综合使用第五代大气再分析数据集(ERA5)资料建立的雪密度模型与GNSS-R反演的雪深序列开展雪水当量估计方法, 旨在有效突破气象参数分辨率不足对传统GNSS-R雪水当量估算的限制, 并选取AB33和SG27两个GPS测站开展实验研究。结果显示, 当GPS测站周边的SNOwpack TEL metry (SNOTEL)站点数量较少时, 与传统基于SNOTEL站点的GNSS-R雪水当量反演方法相比, 本文方法估计的雪水当量的精度更高, 积雪密度相关性提高了约11%, 均方根误差(RMSE)降低了约12%, 雪水当量RMSE降低了约24%, 有效减弱了传统方法建立雪密度模型时所受到的气象参数空间分辨率限制。此外, 本文方法估计的雪水当量精度高于ERA5资料结果, 相关性提升了约9%, RMSE降低了约20%, 也进一步证实了本文方法的有效性, 同时验证了其在GNSS-R领域的应用潜力。

关键词: GNSS-R, ERA5, 雪密度模型, 雪水当量

Abstract:

The Global Navigation Satellite System Reflectometry (GNSS-R) technology has become an important method for estimating the snow water equivalent (SWE). However, when using a snow density model-based method to invert the GNSS-R SWE, the accuracy is often affected by the lack of meteorological observation instruments around the GNSS stations. To address this issue, this study proposed a method that integrated a snow density model established using European Centre for Medium-Range Weather Forecasts Re-Analysis version 5 (ERA5) data and snow depth series derived from GNSS-R to estimate the SWE. This approach aimed to overcome the limitations of insufficient meteorological parameter resolution in traditional GNSS-R SWE estimation. To verify the applicability and effectiveness of the proposed method, experimental studies were conducted at two GPS stations, AB33 and SG27. The accuracy of SWE estimates obtained by constructing snow density models using both SNOwpack TELemetry (SNOTEL) observational data and ERA5 reanalysis data was analyzed. In the region near the GNSS station with fewer SNOTEL stations, the proposed method achieved a higher level of accuracy in SWE estimation compared with traditional GNSS-R SWE inversion methods based on SNOTEL data. Specifically, the snow density correlation improved by approximately 11%, root mean square error (RMSE) decreased by approximately 12%, and SWE RMSE reduced by approximately 24%, effectively mitigating the limitations of traditional methods caused by the spatial resolution of meteorological parameters. Furthermore, the SWE estimates obtained using the proposed method demonstrated a higher level of accuracy than those based on ERA5 reanalysis data, with a correlation increase of approximately 9% and an RMSE reduction of approximately 20%. This further confirms the effectiveness of the proposed method and validates its application potential in the GNSS-R field.


Key words: GNSS-R, ERA5, snow density model, snow water equivalent