使用XCODEX提取XCO2-NASA数据:一个用于数据提取和结构化的Python包.
Henrique Fontellas Laurito1, Thaís Rayane Gomes da Silva2, Newton La Scala2
1Department of Exact Science (FCAV/Unesp), Faculty of Agricultural and Veterinary Sciences, São Paulo State University, Via de Acesso Prof. Paulo Donato Castellane S/N, Jaboticabal, São Paulo, 14884-900, Brazil. henrique.f.laurito@unesp.br.
Environmental monitoring and assessment
|June 2, 2025
概括
一个新的Python包,XCODEX,简化了访问NASA OCO-2卫星数据,用于大气中的二氧化碳 (XCO2) 监测. 该工具通过自动化数据提取和确保全球碳循环研究的高准确性来增强气候研究.
科学领域:
- 地球和环境科学 地球和环境科学
- 大气科学 大气科学
- 气候科学 气候科学
背景情况:
- 准确的大气二氧化碳 (XCO2) 监测对于气候变化研究至关重要.
- 美国宇航局的OCO-2卫星提供了重要的XCO2数据,但其netCDF4格式阻碍了有效的数据利用.
- 研究人员在提取和组织OCO-2数据进行分析时面临着挑战.
研究的目的:
- 开发一个用户友好的Python包 (XCODEX),用于自动检索和结构化OCO-2 XCO2测量.
- 为了简化多个地理位置的数据处理,并最大限度地减少丢失的数据.
- 通过减少数据预处理负担,促进对分析见解的专注研究.
主要方法:
- 开发了XCODEX,这是一个Python包,用于自动化每天从OCO-2中检索XCO2数据.
- 实施的数据处理步骤,包括变量定义,日期匹配和有针对性的数据提取.
- 与地面TCCON和Mauna Loa观测结果相对应的验证的XCODEX结果.
主要成果:
- 与验证数据相比,XCODEX表现出高准确度和可靠性,调整后的R2>0.97和RMSE<1ppm.
- 区域分析显示了全球10个地点的显著XCO2差异.
- 大气二氧化碳的持续上升趋势约为2.4 ppm/年,与全球趋势保持一致.
结论:
- XCODEX有效地自动化了OCO2数据处理,使其可以在Pandas数据框架中访问.
- 该方案通过简化全球碳循环研究的数据处理来赋予研究人员权力.
- XCODEX有助于改善环境监测和气候建模工作.
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