卫星观测时代以后全球冰雪覆盖回顾制图

IF 2.9 3区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS
Kingsley K. Kumah, Omid Zandi, Ali Behrangi
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引用次数: 0

摘要

监测地球冰雪覆盖对于各种应用至关重要,包括气候研究、水文预报和降水制图。本研究开发和评估了扩展全球多传感器自动冰雪测绘系统(GMASI)在1987年7月开始之前的记录的方法,重建了高分辨率的全球冰雪覆盖数据。使用ERA5再分析变量,三种机器学习(ML)方法- ML- e(仅具有ERA5预测因子的ML), ML- ec(具有ERA5和基于气候学的预测因子的ML)和ML- ecc(具有ERA5预测因子的ML,基于气候学的预测因子和额外的一致性检查)-与气候学和基于分数覆盖的方法一起进行了测试。对GMASI(1988-1991)的验证表明,ML-EC和ML-ECC实现了优越的对准,后者提供了边际精度增益。两种方法都显示出稳定的日估算值,在验证期间,雪和冰盖的平均百分比偏差保持在3%以下。它们的高精度进一步反映在关键表面类型的检测概率(POD)超过97%。在所有的方法中,普遍倾向于低估北半球的无雪地区和高估积雪覆盖地区,而南半球的分类挑战在无雪陆地和南极海冰上更为明显。随后应用ML-EC方法将GMASI记录延长至1980年,捕获了与GMASI时代趋势一致的季节和年际变化。这些结果强调了机器学习技术的潜力,可以将积雪和冰盖记录延伸到再分析时代的开始(1940年至今),为气候分析和业务应用提供宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Retrospective Mapping of Global Snow and Ice Cover Beyond the Satellite Observational Era

Monitoring Earth's snow and ice cover is essential for diverse applications, including climate studies, hydrological forecasting, and precipitation mapping. This study develops and evaluates methodologies to extend the Global Multisensor Automated Snow and Ice Mapping System (GMASI) records prior to its July 1987 inception, reconstructing high-resolution global snow and ice cover data. Using ERA5 reanalysis variables, three machine learning (ML) approaches—ML-E (ML with ERA5 predictors only), ML-EC (ML with ERA5 and Climatology-based predictors), and ML-ECC (ML with ERA5 predictors, Climatology-based predictors, and additional Consistency Checks)—were tested alongside climatological and fractional cover-based methods. Validation against GMASI (1988–1991) shows that ML-EC and ML-ECC achieve superior alignment, with the latter offering marginal accuracy gains. Both methods demonstrated stable daily estimates, with mean percentage biases for snow and ice cover remaining below 3% during validation. Their high accuracy is further reflected in probabilities of detection (POD) exceeding 97% across key surface types. Across all methods, there was a general tendency to underestimate snow-free areas and overestimate snow-covered regions in the Northern Hemisphere, while classification challenges in the Southern Hemisphere were more pronounced over snow-free land and Antarctic sea ice. The ML-EC approach was subsequently applied to extend the GMASI record back to 1980, capturing seasonal and interannual variability consistent with GMASI-era trends. These results underscore the potential of ML techniques to extend snow and ice cover records as far back as the beginning of the reanalysis era (1940–present), providing invaluable insights for climate analysis and operational applications.

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来源期刊
Earth and Space Science
Earth and Space Science Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
5.50
自引率
3.20%
发文量
285
审稿时长
19 weeks
期刊介绍: Marking AGU’s second new open access journal in the last 12 months, Earth and Space Science is the only journal that reflects the expansive range of science represented by AGU’s 62,000 members, including all of the Earth, planetary, and space sciences, and related fields in environmental science, geoengineering, space engineering, and biogeochemistry.
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