A combined algorithm comprising multiple dust detection methods was developed using infrared (IR) channels onboard the GEOstationary Korea Multi-Purpose SATellite 2A equipped with the Advanced Meteorological Imager (GK2A/AMI). Six cloud tests using brightness temperature difference (BTD) were utilized to reduce errors caused by clouds. For detecting dust storms, three standard BTD tests (i.e., \({BT}_{12.3}-{BT}_{10.5}\), \({BT}_{8.7}-{BT}_{10.5}\), and \({BT}_{11.2}-{BT}_{10.5}\)) were combined with the polarized optical depth index (PODI). The combined algorithm normalizes the indices for cloud and dust detection, and adopts weighted combinations of dust tests depending on the observation time (day/night) and surface type (land/sea). The dust detection results were produced as quantitative confidence factors and displayed as false color imagery, applying a dynamic enhancement background reduction algorithm (DEBRA). The combined dust detection algorithm was qualitatively assessed by comparing it with dust RGB imageries and ground-based lidar data. The combined algorithm especially improved the discontinuity in weak dust advection to the sea and considerably reduced false alarms as compared to previous dust monitoring methods. For quantitative validation, we used aerosol optical thickness (AOT) and fine mode fraction (FMF) derived from low Earth orbit (LEO) satellites in daytime. For both severe and weakened dust cases, the probability of detection (POD) ranged from 0.667 to 0.850 and it indicated that the combined algorithm detects more potential dust pixels than other satellites. In particular, the combined algorithm was advantageous in detecting weak dust storms passing over the warm and humid Yellow Sea with low dust height and small AOT.

在配备有先进气象成像仪（GK2A / AMI）的GEOstationary Korea多功能SATellite 2A上，使用红外（IR）通道开发了一种包含多种灰尘检测方法的组合算法。利用亮度温差（BTD）进行了六次云测试，以减少由云引起的误差。为了检测沙尘暴，需要进行三个标准的BTD测试（即\（{BT} _ {12.3}-{BT} _ {10.5} \），\（{BT} _ {8.7}-{BT} _ {10.5} \ ）和\（{BT} _ {11.2}-{BT} _ {10.5} \））与偏振光学深度指数（PODI）组合在一起。组合算法对云和灰尘检测的指标进行了归一化，并根据观察时间（昼/夜）和地表类型（陆地/海洋）采用了灰尘测试的加权组合。应用动态增强背景减少算法（DEBRA），将灰尘检测结果作为定量置信因子生成并显示为假彩色图像。通过将其与粉尘RGB图像和基于地面的激光雷达数据进行比较，对组合式粉尘检测算法进行了定性评估。与以前的粉尘监测方法相比，该组合算法尤其改善了微弱的平流到海中的不连续性，并大大减少了误报。为了进行定量验证，我们在白天使用了来自低地球轨道（LEO）卫星的气溶胶光学厚度（AOT）和精细模式分数（FMF）。对于严重和较弱的尘埃情况，检测概率（POD）介于0.667至0.850之间，这表明组合算法比其他卫星检测到更多的潜在尘埃像素。特别是，组合算法在检测尘埃高度低且AOT较小的暖湿黄海上的微弱沙尘暴方面具有优势。