Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms
Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms
复制标题
使用机器学习算法反演火山灰云底高
DOI：
10.3390/atmos14020228
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Dexin Zhao
中科院分区：
文献类型：
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作者：
Fenghua Zhao;Jiawei Xia;Lin Zhu;Hongfu Sun;Dexin Zhao
火山灰与气象云的辐射特征存在明显差异，常规的气象云云底高（CBH）反演方法在未经大量参数化和模式修正的情况下难以应用于火山灰。此外，现有的CBH反演方法有局限性，包括许多经验公式的参与和上游云产品的准确性的依赖。为减少火山灰云底高物理反演方法的不确定性，提出了一种用于火山灰云底高反演的机器学习方法。这一新方法利用了来自正交偏振云-气溶胶激光雷达的极轨道主动遥感数据、垂直剖面信息以及分别来自第二代气象卫星和风云-4B号卫星上的旋转增强可见光和红外成像仪和高级地球静止辐射成像仪的地球静止被动遥感测量数据。该方法涉及一种基于概率论的算法，混合使用主成分分析（PCA）和四种ML算法之一，包括k-最近邻（KNN），极端梯度提升（XGBoost），随机森林（RF）和梯度提升决策树（GBDT）方法。选择2010年4月至5月埃亚菲亚德拉冰盖火山（冰岛）、2011年6月普耶韦-科东考勒火山群（智利安第斯山脉）和2022年1月洪加汤加-洪加哈派火山（汤加）的喷发作为构建训练和验证样本集的典型案例。我们证明，PCA和GBDT的组合比其他组合更准确地执行，平均绝对误差（MAE）为1.152公里，均方根误差（RMSE）为1.529公里，皮尔逊相关系数（r）为0.724。在训练之前使用PCA作为额外的过程，降低了输入预测器之间的特征相关性，并提高了算法的准确性。虽然ML算法在相对简单的单层火山灰云条件下表现良好，但它往往高估多层条件下的VBH，这是气象CBH反演中尚未解决的问题。
There are distinct differences between radiation characteristics of volcanic ash and meteorological clouds, and conventional retrieval methods for cloud base height (CBH) of the latter are difficult to apply to volcanic ash without substantial parameterisation and model correction. Furthermore, existing CBH inversion methods have limitations, including the involvement of many empirical formulae and a dependence on the accuracy of upstream cloud products. A machine learning (ML) method was developed for the retrieval of volcanic ash cloud base height (VBH) to reduce uncertainties in physical CBH retrieval methods. This new methodology takes advantage of polar-orbit active remote-sensing data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), from vertical profile information and from geostationary passive remote-sensing measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Advanced Geostationary Radiation Imager (AGRI) aboard the Meteosat Second Generation (MSG) and FengYun-4B (FY-4B) satellites, respectively. The methodology involves a statistics-based algorithm with hybrid use of principal component analysis (PCA) and one of four ML algorithms including the k-nearest neighbour (KNN), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting decision tree (GBDT) methods. Eruptions of the Eyjafjallajökull volcano (Iceland) during April-May 2010, the Puyehue-Cordón Caulle volcanic complex (Chilean Andes) in June 2011, and the Hunga Tonga-Hunga Ha’apai volcano (Tonga) in January 2022 were selected as typical cases for the construction of the training and validation sample sets. We demonstrate that a combination of PCA and GBDT performs more accurately than other combinations, with a mean absolute error (MAE) of 1.152 km, a root mean square error (RMSE) of 1.529 km, and a Pearson’s correlation coefficient (r) of 0.724. Use of PCA as an additional process before training reduces feature relevance between input predictors and improves algorithm accuracy. Although the ML algorithm performs well under relatively simple single-layer volcanic ash cloud conditions, it tends to overestimate VBH in multi-layer conditions, which is an unresolved problem in meteorological CBH retrieval.