一种新的方法来预测从平流层极地的动态来预测北极平流层臭氧,使用可解释的机器学习
Anish Kumar1, Joyjit Mandal2, Sina Mehrdad3
1Institute for Meteorology, Leipzig University, Leipzig, Germany. anish.kumar@uni-leipzig.de.
Scientific reports
|October 20, 2025
概括
北极臭氧水平正在下降,这促使需要预测算法. 这项研究使用机器学习对平流层极地的动态来预测臭氧,显示了未来气候建模的有希望的准确性.
科学领域:
- 大气化学 大气化学
- 气候科学 气候科学
- 机器学习 机器学习
背景情况:
- 自2019年以来,北极平流层臭氧消耗的趋势一直在下降.
- 2020年,在平流层极地 (SPV) 中报告了北极臭氧洞,引起了人们的关注.
研究的目的:
- 开发一个高效的算法来预测北极臭氧水平.
- 为了利用SPV的形态和动态特性进行臭氧预测.
主要方法:
- 使用XGBoost.一个可解释的机器学习方法.
- 基于SPV的基于物理的特性特征工程.
主要成果:
- XGBoost的R平方得分为0.80,与观察到的臭氧水平的相关性为0.91.
- 该算法准确预测了每日和季节性臭氧变化,包括2020年的低值,高估了约20个多布森单位.
- 该模型与某些年份的观察结果有很强的对齐.
结论:
- SPV的动态参数对于预测化学臭氧损失是有效的.
- 开发的算法可以用气候模型数据作为预测未来北极臭氧变化的工具.
- 这种方法提供了一种化学独立的方法来预测臭氧变异性.
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