ABSTRACT

Fog is a phenomenon that occurs very close to the ground or sea level, and when detected by satellite, it is difficult to distinguish it from the low-level cloud. Logistic regression can help identify the false detection of the low-level cloud as fog and improve the accuracy of fog detection. In this study, a Korean fog detection algorithm was developed by using a machine learning-based logistic regression model (LRM) at three time points throughout the day (daytime, nighttime, and dawn/dusk) according to the solar zenith angle. The visible reflectance (Ref) and infrared brightness temperature (BT) of Himawari-8, solar zenith angle, land/sea mask, digital elevation angle, clear-sky Ref, and clear-sky BT excluding cloud pixels, from 2017 to 2018, were used as training data. The model was constructed by selecting variables with high correlation with the target data through a stepwise elimination method among input data having independent relationships between variables. Cross-validation using test data (20% of training data) contributed to the optimization of LRM. The fog detection performance of LRM confirmed by cross-validation has a stability of 83%–94% with high accuracy. For quantitative validation in 2019 using a 3 × 3-pixel validation method, the average probability of detection (POD) in the spring was 0.89–0.92, while the false alarm rate (FAR) was 0.39–0.41; in autumn, the POD was 0.9–0.97 and FAR was 0.29–0.4. The sophistication of the threshold between fog and non-fog can affect the performance improvement of the model. Further evaluation of the fog detection accuracy confirmed the reliability of the fog probability based on the stepwise probability. Satellite images enabled quantitative comparisons and validation of the proposed method; the results indicate that the approach is stable, reliable, and accurate. LRM fog detection will contribute to the Korean fog detection forecast with high performance, while the machine learning method used to build LRM can improve the performance of other meteorological forecast systems.

摘要

雾是一种发生在非常接近地面或海平面的现象，当被卫星探测到时，很难与低层云区分开来。Logistic 回归有助于识别低层云的误检测为雾，提高雾检测的准确性。在这项研究中，根据太阳天顶角，在全天的三个时间点（白天、夜间和黎明/黄昏）使用基于机器学习的逻辑回归模型 (LRM) 开发了一种韩国雾检测算法。2017-2018年Himawari-8的可见光反射率（Ref）和红外亮温（BT）、太阳天顶角、陆地/海洋掩膜、数字仰角、晴天Ref和晴天BT（不包括云像素），被用作训练数据。该模型是通过在变量之间具有独立关系的输入数据中，通过逐步剔除的方法，选取与目标数据相关性高的变量来构建的。使用测试数据（20% 的训练数据）的交叉验证有助于优化 LRM。经交叉验证证实的 LRM 的雾检测性能具有 83%–94% 的稳定性，且准确度很高。对于2019年使用3×3像素验证方法的定量验证，春季的平均检测概率（POD）为0.89-0.92，而误报率（FAR）为0.39-0.41；秋季，POD 为 0.9-0.97，FAR 为 0.29-0.4。雾和非雾之间阈值的复杂程度会影响模型的性能提升。对雾检测精度的进一步评估证实了基于逐步概率的雾概率的可靠性。卫星图像能够对所提出的方法进行定量比较和验证；结果表明，该方法稳定、可靠、准确。LRM 雾检测将有助于韩国高性能的雾检测预测，而用于构建 LRM 的机器学习方法可以提高其他气象预报系统的性能。