基于集合学习的热红外卫星数据 XCO2 浓度估算

Atmosphere Pub Date : 2024-01-19 DOI:10.3390/atmos15010118
Xiaoyong Gong, Ying Zhang, Meng Fan, Xinxin Zhang, Shipeng Song, Zhongbin Li
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引用次数: 0

摘要

随着大气中二氧化碳(CO2)浓度的增加,全球气温持续上升,气候变暖已成为全球可持续发展面临的重大挑战。交叉轨道红外探测仪(CrIS)是一种傅立叶变换光谱仪,光谱分辨率为 0.625 cm-1,覆盖 15 μm 二氧化碳吸收波段,提供了一种每天两次大规模监测二氧化碳的方法。本文提出了一种利用集合学习从热红外卫星数据预测柱平均二氧化碳(XCO2)浓度的方法,以避免优化估计(OE)所需的辐射传递模型迭代计算。训练数据集由 CrIS 卫星数据、欧洲中期天气预报中心 (ECMWF) Reanalysis v5 (ERA5) 气象参数和地面观测数据组成。训练集采用两种方法处理:相关显著性分析(简称 CSA)和主成分分析(PCA)。使用极端梯度提升器(XGBoost)、极端随机树(ERT)和梯度提升回归树(GBRT)进行训练和学习,以开发新的检索模型。结果表明,利用 PCA 数据集建立的 XCO2 预测模型的 R2 比利用 CSA 数据集建立的预测模型的 R2 大。这三种学习模型都经过了验证集的验证,其中 ERT 模型的预测结果与真实值的一致性最好(R2 = 0.9006,RMSE = 0.7994 ppmv,MAE = 0.5804 ppmv)。最终选择 ERT 模型来估算 XCO2 的浓度。2019 年 12 个 TCCON 站点的 XCO2 预测值偏差在 ±1 ppm 范围内。与 TCCON 地面观测数据接近的 XCO2 浓度月平均值被划分为四个区域:亚洲(R2 = 0.9671,RMSE = 0.7072 ppmv)、欧洲(R2 = 0.9703,RMSE = 0.8733 ppmv)、北美洲(R2 = 0.9800,RMSE = 0.6187 ppmv)和大洋洲(R2 = 0.9558,RMSE = 0.4614 ppmv)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of the Concentration of XCO2 from Thermal Infrared Satellite Data Based on Ensemble Learning
Global temperatures are continuing to rise as atmospheric carbon dioxide (CO2) concentrations increase, and climate warming has become a major challenge to global sustainable development. The Cross-Track Infrared Sounder (CrIS) instrument is a Fourier transform spectrometer with 0.625 cm−1 spectral resolution covering a 15 μm CO2-absorbing band, providing a way of monitoring CO2 with on a large scale twice a day. This paper proposes a method to predict the concentration of column-averaged CO2 (XCO2) from thermal infrared satellite data using ensemble learning to avoid the iterative computations of radiative transfer models, which are necessary for optimization estimation (OE). The training data set is constructed with CrIS satellite data, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) meteorological parameters, and ground-based observations. The training set was processed using two methods: correlation significance analysis (abbreviated as CSA) and principal component analysis (PCA). Extreme Gradient Boosters (XGBoost), Extreme Random Trees (ERT), and Gradient Boost Regression Tree (GBRT) are used for training and learning to develop the new retrieval model. The results showed that the R2 of XCO2 prediction built from the PCA dataset was bigger than that from the CSA dataset. These three learning models were verified by validation sets, and the ERT model showed the best agreement between model predictions and the truth (R2 = 0.9006, RMSE = 0.7994 ppmv, MAE = 0.5804 ppmv). The ERT model was finally selected to estimate the concentrations of XCO2. The deviation of XCO2 predictions of 12 TCCON sites in 2019 was within ±1 ppm. The monthly averages of XCO2 concentrations in close agreement with TCCON ground observations were grouped into four regions: Asia (R2 = 0.9671, RMSE = 0.7072 ppmv), Europe (R2 = 0.9703, RMSE = 0.8733 ppmv), North America (R2 = 0.9800, RMSE = 0.6187 ppmv), and Oceania (R2 = 0.9558, RMSE = 0.4614 ppmv).
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