用机器学习补全因卫星停用导致的二氧化碳再分析数据
Reconstructing Carbon Monoxide Reanalysis with Machine Learning
- 用机器学习预测模型与观测间的系统性偏差
- 可恢复2025年MOPITT卫星停用后的碳氧化物数据
- 适合大气监测与气候建模研究者参考
哥白尼大气监测服务通过结合模型模拟与卫星观测提供大气成分再分析产品。这些产品的质量高度依赖观测数据的可用性，而随着新卫星仪器上线或退役（如2025年初停用的测量对流层污染仪器，MOPITT），数据连续性面临挑战。本文研究机器学习方法，利用控制模型模拟数据预测月均总柱状碳氧化物（CO）再分析值，旨在弥补因观测缺失导致的数据断层，提升长期气候数据的完整性。
原文摘要 · Abstract (English)
The Copernicus Atmospheric Monitoring Service provides reanalysis products for atmospheric composition by combining model simulations with satellite observations. The quality of these products depends strongly on the availability of the observational data, which can vary over time as new satellite instruments become available or are discontinued, such as Carbon Monoxide (CO) observations of the Measurements Of Pollution In The Troposphere (MOPITT) satellite in early 2025. Machine learning offers a promising approach to compensate for such data losses by learning systematic discrepancies between model configurations. In this study, we investigate machine learning methods to predict monthly-mean total column of Carbon Monoxide re-analysis from a control model simulation.
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