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Abstract：
In order to enhance the retrieval accuracy of cloud top height (CTH) from MODIS data, neural network models were employed based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Three types of methods were established using MODIS inputs: cloud parameters, calibrated radiance, and a combination of both. From a statistical standpoint, models with combination inputs demonstrated the best performance, followed by models with calibrated radiance inputs, while models relying solely on calibrated radiance had poorer applicability. This work found that cloud top pressure (CTP) and cloud top temperature played a crucial role in CTH retrieval from MODIS data. However, within the same type of models, there were slight differences in the retrieved results, and these differences were not dependent on the quantity of input parameters. Therefore, the model with fewer inputs using cloud parameters and calibrated radiance was recommended and employed for individual case studies. This model produced results closest to the actual cloud top structure of the typhoon and exhibited similar cloud distribution patterns when compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) CTHs from a climatic statistical perspective. This suggests that the recommended model has good applicability and credibility in CTH retrieval from MODIS images. This work provides a method to improve accurate CTHs from MODIS data for better utilization.
摘要：
为了提高MODIS数据中云顶高度(CTH)的检索精度,基于云-气溶胶激光雷达和正交极化（CALIOP）数据，采用神经网络模型。使用MODIS输入建立了三种类型的方法：云参数，校准的辐射度,以及两者的结合。从统计的角度来看，具有组合输入的模型表现出最佳性能，其次是具有校准辐射度输入的模型，而仅仅依靠校准后的辐射度的模型适用性较差。这项工作发现，云顶压力（CTP）和云顶温度在从MODIS数据中检索CTH中起着至关重要的作用。然而,在相同类型的模型中，检索结果略有差异，这些差异不取决于输入参数的数量。因此,推荐使用云参数和校准后的辐射度输入较少的模型,并将其用于个例研究.从气候统计的角度来看，与云-气溶胶激光雷达和红外探路者卫星观测（CALIPSO）CTH相比，该模型产生的结果最接近台风的实际云顶结构，并表现出相似的云分布模式。这表明推荐模型在MODIS图像的CTH检索中具有良好的适用性和可信度。这项工作提供了一种从MODIS数据中提高CTH准确性的方法，以便更好地利用。
