对晴天条件的大气排放率模型的评估,并重新分析数据
Luis Morales-Salinas1, Samuel Ortega-Farias2, Camilo Riveros-Burgos3,4
1Laboratory for Research in Environmental Sciences (LARES), Faculty of Agricultural Sciences, University of Chile, Santiago, Chile. lmorales@uchile.cl.
Scientific reports
|September 2, 2023
概括
通过使用全球气候数据校准布鲁萨尔特方程,提高了大气长波向下辐射 (Ld) 估计. 这种方法显著减少了全球净辐射 (Rn) 计算中的错误.
科学领域:
- 地球和大气科学 地球和大气科学
- 环境科学 环境科学
- 气候科学 气候科学
背景情况:
- 大气长波向下辐射 (Ld) 对生态系统过程和净辐射 (Rn) 计算至关重要.
- 使用大气发射率 (εa) 亚模型的经验方法是估计Ld的常用方法.
- 对于 εa 的全球模型需要评估和校准才能准确地估计 Ld.
研究的目的:
- 评估和校准全球大气发射率 (εa) 模型,以改进Ld估计.
- 通过使用NCEP/NCAR再分析数据,评估Brutsaert方程对Ld估计的性能.
- 为了确定Brutsaert方程的系数是否在不同地区和季节是不变的.
主要方法:
- 评估了八个全球大气发射率 (εa) 模型.
- 使用参数不稳定性分析对表现最好的模型 (Brutsaert方程) 进行校准.
- 使用NCEP/NCAR再分析 (NNR) 数据进行模型评估和校准.
- 应用K-媒介分析以确定同质的气候区域.
主要成果:
- 布鲁萨尔特方程在评估的模型中表现最好.
- 校准的Brutsaert方程系数显示出1.2至1.4之间的全球季节性趋势.
- 在K-means分析中,发现了五个不同的气候区域,它们的Eaa行为类似.
- 校准的Brutsaert方程将全球Rn估计误差降低了64%,区域误差降低了18-77%.
结论:
- 在校准时,Brutsaert方程显著提高了Ld和Rn估计的准确性.
- 布鲁萨尔特方程的系数不是一个普遍的常数,并且根据地理位置和季节而异.
- 区域校准对于使用Brutsaert方程优化Ld和Rn估计至关重要.
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