Abstract A Feed Forward Neural Net (NN) approach to distinguish between clouds and the surface has been applied to the Sea and Land Surface Temperature Radiometer in polar regions. The masking algorithm covers the Arctic, Antarctic and regions typically classified as the cryosphere such as northern hemisphere permafrost. The mask has been trained using collocations with the CALIOP active lidar, which in narrow strips provide more accurate detection of cloud, and was subsequently evaluated as a function of cloud type and surface type. The mask was compared with the existing operational Bayesian and Empirical cloud masks by eye and also statistically using CALIOP data. It was found to perform exceptionally well in the polar regions. The Kuiper skill score improved from 0.28, for the operational Bayesian and 0.17 for the Empirical masks to 0.77 for the NN. The NN algorithm also has a much more homogeneous performance over all surface types. The key improvement came from better identification of clear scenes; for the NN mask, the same performance in terms of contamination of cloudy pixels in the sample of identified clear pixels can be achieved while retaining 40% of the clear pixels compared with 10% for the operational cloud identification. The algorithm performed with almost the same skill over sea and land. The best performance was achieved for opaque clouds while transparent and broken clouds showed slightly reduced accuracy.

中文翻译：

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极地SLSTR机器学习云识别算法评价与比较

摘要 一种区分云和地表的前馈神经网络（NN）方法已应用于极地地区的海陆表面温度辐射计。掩蔽算法涵盖北极、南极和通常被归类为冰冻圈的地区，例如北半球永久冻土。该面罩已使用 CALIOP 主动激光雷达的搭配进行训练，它在窄条带中提供更准确的云检测，随后被评估为云类型和表面类型的函数。通过肉眼和统计使用 CALIOP 数据将掩模与现有的操作贝叶斯和经验云掩模进行比较。它被发现在极地地区表现得非常好。柯伊伯技能得分从操作贝叶斯的 0.28 和经验面具的 0.17 提高到 0。77 为 NN。NN 算法在所有表面类型上也具有更加均匀的性能。关键改进来自更好地识别清晰场景；对于 NN 掩码，在识别清晰像素样本中的混浊像素污染方面可以实现相同的性能，同时保留 40% 的清晰像素，而操作云识别则保留 10%。该算法在海上和陆地上以几乎相同的技能执行。不透明的云获得了最佳性能，而透明和破碎的云的准确性略有降低。在识别的清晰像素样本中的混浊像素污染方面可以实现相同的性能，同时保留 40% 的清晰像素，而操作云识别则保留 10%。该算法在海上和陆地上以几乎相同的技能执行。不透明的云获得了最佳性能，而透明和破碎的云的准确性略有降低。在识别的清晰像素样本中的混浊像素污染方面可以实现相同的性能，同时保留 40% 的清晰像素，而操作云识别则保留 10%。该算法在海上和陆地上以几乎相同的技能执行。不透明的云获得了最佳性能，而透明和破碎的云的准确性略有降低。