基于卫星遥感数据的全天云属性和发生概率数据集
Longfeng Nie1,2,3, Yuntian Chen4,5, Dongxiao Zhang6,7,8,9
1Pengcheng Laboratory, Shenzhen, 518000, P. R. China.
Scientific data
|March 5, 2025
概括
CldNet版本2.0 (CldNetV2) 增强了云的分类和属性预测,为气象研究提供了关键的全天数据集. 这一进步提高了对气候的理解,并填补了夜间数据缺口.
科学领域:
- 气象学和气候科学 气象学和气候科学
- 遥感 遥感 遥感 遥感
- 人工智能在地球观测中的作用
背景情况:
- 准确的云属性数据集对于气象研究,气候研究和应用至关重要.
- 现有的卫星云产品往往缺乏全面的夜间数据和多样化的云属性信息.
- CldNet版本2.0建立在之前的工作基础上,将云识别功能扩展到全日夜周期.
研究的目的:
- 引入CldNet版本2.0 (CldNetV2) 进行增强的云类型分类和属性预测.
- 为了生成全面的,全天云特性和发生概率的数据集.
- 为了解决当前希马瓦里云产品的局限性,特别是在夜间条件下.
主要方法:
- 从基础的CldNet中利用转移学习和模型参数共享技术.
- 开发一个深度学习模型来分类云类型和预测多个云属性.
- 在年度,季节性和月度时间尺度上统计分析云类型发生概率,区分全天,白天和夜间.
主要成果:
- CldNetV2成功地对云类型进行了分类,并预测了额外的云属性,从而创建了有价值的夜间数据集.
- 生成的数据集包括各种时间尺度和条件 (全天,白天,夜晚) 的云类型发生概率.
- 使用CALIPSO,ERA5和可视化的独立验证证实了CldNetV2云产品的可靠性.
结论:
- CldNetV2显著提升了全天云属性和事件概率评估的能力.
- 公开发布的数据集为气象环境评估和气候研究提供了宝贵的资源.
- 这项工作有助于通过全面的云数据更好地理解和建模大气过程.
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