LESO: 10年间的卫星导出的洲际每小时表面臭氧度
Songyan Zhu1,2, Jian Xu3, Jingya Zeng4
1National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China. szhu4@ed.ac.uk.
Scientific data
|October 25, 2023
概括
一个新的框架,即学习表面臭氧 (LESO) 组合,为中国,欧洲和美国提供准确的每小时表面臭氧度. 这一进步有助于大气和气候科学研究,通过详细介绍臭氧.
科学领域:
- 大气化学和物理大气化学和物理
- 气候科学 气候科学
- 环境监测 环境监测
背景情况:
- 表面臭氧 (O3) 是一个关键的空气污染物和温室气体.
- 表面O3的精确,高分辨率的时空数据对于环境和气候研究至关重要.
- 从卫星信号中获取较低大气层的O3存在重大挑战.
研究的目的:
- 引入和验证由LEarning表面臭氧 (LESO) 框架产生的表面臭氧 (O3) 度的新组合.
- 调查中国,欧洲和美国O3表面的空间和时间变化.
- 为10年 (2012-2021) 提供准确的,高分辨率的每小时表面O3数据.
主要方法:
- 开发了学习表面臭氧 (LESO) 框架,以生成一组表面O3数据.
- 建立了表面O3和来自卫星的O3总列之间的关系.
- 将高分辨率气象再分析数据集成到LESO框架中.
主要成果:
- 莱索集体提供每小时表面O3度0.1° × 0.1°空间分辨率.
- 验证显示,LESO数据集解释了大约80%的每小时O3变化,RMSE为19.63μg/m3.
- 这些数据集准确地捕捉了表面O3.3的日间周期,周末效应,季节性和跨年变化.
结论:
- 莱索框架为克服从卫星数据中获取低大气层O3的挑战提供了突破性进展.
- 生成的O3数据集非常准确,可以捕捉关键的时间和空间变化.
- 这些数据集对于大气和气候科学中的各种研究和应用非常有价值.
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