基于先进的机器学习算法的孟加拉湾海面pCO2地图
A P Joshi1, Prasanna Kanti Ghoshal1,2, Kunal Chakraborty3
1Indian National Centre for Ocean Information Services, Ministry of Earth Sciences, Hyderabad, India.
Scientific data
|April 13, 2024
概括
这项研究通过开发高分辨率地图,提高了对孟加拉湾海面二氧化碳 (CO2) 的理解. 新的INCOIS-ReML产品与现有模型相比,提供了更高的准确性,改善了CO2流量估计.
科学领域:
- 海洋学 海洋学 海洋学
- 海洋生物地质化学海洋生物地质化学
- 气候科学 气候科学
背景情况:
- 有限的海面pCO2观测阻碍了对孟加拉湾 (BoB) 的空间和时间变异性的理解.
- 现有的机器学习 (ML) 产品对 BoB 存在高预测误差,原因是观测数据稀少.
研究的目的:
- 通过使用综合数据集,为孟加拉湾开发气候学海面pCO2地图.
- 创建一个高分辨率的pCO2产品,准确地表示空间变化和空海CO2流量.
- 评估和比较先进的ML算法在BoB中的pCO2预测的性能.
主要方法:
- 利用了大量的开放和沿海海洋观测pCO2和相关变量.
- 采用了四种先进的机器学习算法来预测海面pCO2.
- 选择了性能最好的ML模型来生成INCOIS-ReML气候产品.
主要成果:
- 与RAMA浮标观测相比,开发的INCOIS-ReML产品表现出令人满意的性能.
- 在捕获海面pCO2变异性方面,INCOIS-ReML显示优于现有的ML产品.
- 高分辨率的INCOIS-ReML产品有效捕捉pCO2和空海CO2流量的空间变化,特别是在沿海和北方BoB地区.
结论:
- 在孟加拉湾,INCOIS-ReML产品在绘制海面pCO2和空海CO2流量方面取得了重大进展.
- 这项研究解决了数据稀缺问题,为该地区的海洋生物地球化学研究提供了更可靠的工具.
- 改进的pCO2空间表示对于理解BoB中的碳动态和气候相互作用至关重要.
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