在辅助ERA5变量和基于机器学习的空隙填补上开发了Titicaca湖的每小时分辨率空气温度
Jimmy W Sirpa-Poma1,2, Juan Calle2, Elvis Uscamayta-Ferrano3
1Instituto de Hidráulica e Hidrología, Universidad Mayor de San Andrés, La Paz, Bolivia.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究引入了一种使用质量控制和机器学习来改善气象数据的新方法. 这种方法成功地填补了蒂蒂卡卡湖空气温度记录中的空白和纠正错误.
科学领域:
- 气象学 天气学
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 气象数据经常存在差距,不一致性和异常值.
- 高分辨率,可靠的数据对于环境监测和气候研究至关重要.
- 自动气象站提供有价值但有时不完美的数据集.
研究的目的:
- 开发和评估一个完整的程序,以提高气象数据质量.
- 结合先进的质量控制 (QC) 和机器学习 (ML) 来获得可靠的数据.
- 为了解决蒂蒂卡卡湖地区空气温度记录中的数据缺口和不一致.
主要方法:
- 应用先进的质量控制统计 (四分位数范围,双重量,局部异常因素) 来清理原始数据.
- 采用附近站数据和ERA5-Land再分析,实施了空间和时间填补技术.
- 使用的ML模型包括随机森林 (RF),支持矢量机 (SVM),堆叠 (STACK) 和AdaBoost (ADA).
主要成果:
- 随机森林 (RF) 模型表现出卓越的性能,达到高达0.9的R2值和1.5°C以下的RMSE值.
- 空间填补差距对于高度相关的站点数据是有效的,而时间方法适用于相关性较低的区域.
- 综合程序显著提高了气象数据的可靠性和完整性.
结论:
- 结合的QC和ML方法有效地产生可靠的,连续的,高分辨率的气象数据.
- 该方法可以适应不同的地理条件,并且可以扩展到其他气象变量.
- 该程序为改善数据稀缺地区的环境数据质量提供了可靠的解决方案.
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