A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations
A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations
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DOI：
10.5194/amt-2019-409
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发表时间：
2019-11
影响因子：
3.8
通讯作者：
Chenxi Wang;S. Platnick;K. Meyer;Zhibo Zhang;Yaping Zhou
中科院分区：
文献类型：
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作者：
Chenxi Wang;S. Platnick;K. Meyer;Zhibo Zhang;Yaping Zhou
摘要。我们使用Suomi国家极轨伙伴关系（SNPP）上的可见光红外成像辐射计套件（VIIRS）的光谱观测数据训练了两个随机森林（RF）机器学习模型，用于云掩膜和云热力相位检测。选取正交偏振云气溶胶激光雷达（CALIOP）观测数据作为参考标签。使用2013年至2016年4年的VIIRS和CALIOP数据集，对两种RF模型进行了全天和日间条件下的训练。由于轨道的差异，CALIOP和SNPP的VIIRS组合训练样本覆盖了较宽的观测天顶角范围，这对模型的整体性能有很大的好处。全天模式使用3个红外波段（8.6、11和12µm），白天模式使用5个近红外（NIR）和短波红外（SWIR）波段（0.86、1.24、1.38、1.64和2.25µm）以及3个红外波段探测透明、液态水和冰云像素点。为了提高两种模型的性能，我们分别考虑了多达七种表面类型，即海水、森林、农田、草地、冰雪、贫瘠沙漠和灌木。将两种RF模型对浑浊像素和热力学相位的检测与2017年的CALIOP产品进行比较。研究表明，当使用保守筛选过程排除最具挑战性的被动遥感云像素时，与CALIOP参考相比，这两种RF模型在云检测和热力学相位方面都具有较高的准确率。对现有的其他SNPP VIIRS和Aqua MODIS云掩模和相位产品进行了评价，结果表明，两种RF模型和MODIS MYD06光学特性相位产品是白天激光雷达观测的前三种算法。在夜间，RF全天模型最适合云检测和相位，特别是对于冰雪表面上的像素。如果可以从CALIOP或其他激光雷达上收集训练样本，则可以将现有的射频模型扩展到其他类似的无源仪器。然而，参考标签的质量和可能影响模型性能的潜在采样问题需要进一步关注。
Abstract. We trained two Random Forest (RF) machine learning models for cloud mask and cloud thermodynamic-phase detection using spectral observations from Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (SNPP). Observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS and CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad-viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses three VIIRS infrared (IR) bands (8.6, 11, and 12 µm), and the daytime model uses five Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64, and 2.25 µm) together with the three IR bands to detect clear, liquid water, and ice cloud pixels. Up to seven surface types, i.e., ocean water, forest, cropland, grassland, snow and ice, barren desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models was compared against collocated CALIOP products from 2017. It is shown that, when using a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison to the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top three algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, particularly for pixels over snow and ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.