Scholarlink

<p>In this study, we developed a machine learning-based global daily SWE product (SWEML) with 0.25° (~25 km) resolution for 1980–2020. Using k-means clustering, <i>in-situ</i> SWE measurements were grouped into 14 clusters, and a random forest model was trained on 11,687 grid points with meteorological forcing and terrain attributes. SWEML was compared with ten reference datasets representing diverse approaches, including land–atmosphere reanalysis without data assimilation (DA), systems incorporating DA, snow model simulations of varying complexity driven by reanalysis forcing with or without DA, and remote sensing products. The overall root mean square error (RMSE) and bias were 10.33 mm and −7.13 mm, respectively. Notably, SWEML achieved high accuracy in high-elevation regions such as the Rocky Mountains, with an RMSE of 7.30 mm and correlation coefficient of 0.98. It also agreed with the Gamma airborne SWE over North America and showed similar spatial patterns and peak SWE time series of the Andes Snow Reanalysis. These results highlight the robustness of SWEML in regions with and without training data.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Global 0.25-degree gridded Snow water equivalent data derived from machine learning using in-situ measurements

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月10日

查看全文
  • Jungho Seo,
  • Mahdi Panahi,
  • JiHyun Kim,
  • Sayed Bateni,
  • Yeonjoo Kim

摘要

In this study, we developed a machine learning-based global daily SWE product (SWEML) with 0.25° (~25 km) resolution for 1980–2020. Using k-means clustering, in-situ SWE measurements were grouped into 14 clusters, and a random forest model was trained on 11,687 grid points with meteorological forcing and terrain attributes. SWEML was compared with ten reference datasets representing diverse approaches, including land–atmosphere reanalysis without data assimilation (DA), systems incorporating DA, snow model simulations of varying complexity driven by reanalysis forcing with or without DA, and remote sensing products. The overall root mean square error (RMSE) and bias were 10.33 mm and −7.13 mm, respectively. Notably, SWEML achieved high accuracy in high-elevation regions such as the Rocky Mountains, with an RMSE of 7.30 mm and correlation coefficient of 0.98. It also agreed with the Gamma airborne SWE over North America and showed similar spatial patterns and peak SWE time series of the Andes Snow Reanalysis. These results highlight the robustness of SWEML in regions with and without training data.