一个基于深度学习的算法,用于识别使用Fengyun-4A卫星观测数据的降雨云
Guangyi Ma1, Jie Huang2, Yonghong Zhang2
1School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
一个新的卷积神经网络 (CNN) 算法PCINet使用Fengyun-4A卫星数据准确识别降雨云. 该方法增强了定量降水估计和现在预测,特别是在白天使用可见/近红外光谱数据.
科学领域:
- 气象学和大气科学 气象学和大气科学
- 遥感技术 遥感技术 遥感技术
- 人工智能在地球观测中的作用
背景情况:
- 从卫星数据准确识别降雨云对于定量降雨估计和现在预测至关重要.
- 现有的方法需要改进,以便在白天和夜晚的不同时间全面识别.
研究的目的:
- 开发和评估一种基于卷积神经网络 (CNN) 的新型算法 (PCINet) 来识别降水云.
- 评估可见/近红外光谱信息对白天降雨云识别的贡献.
- 将PCINet的性能与其他图像细分的深度学习模型进行比较.
主要方法:
- 利用了来自风云-4A (FY-4A) 卫星的高时空和多光谱数据.
- 开发了一个CNN架构 (PCINet),具有多个规模结构和跳过连接策略.
- 探索可见/近红外光谱波段对白天识别的影响.
- 与其他五种深度学习模型进行了比较分析,使用长时间序列数据和大量降雨事件的案例研究.
主要成果:
- 拟议的PCINet算法与基线深度学习模型相比,表现优越.
- 整合可见/近红外光谱信息显著提高了白天降雨云识别准确度.
- 该模型提供了准确和近乎实时的降雨云识别.
结论:
- PCINet提供了一种有效的解决方案,用于在白天,夜晚和夜间准确地识别降水云.
- 整合特定的光谱频段增强了该模型在白天分析的能力.
- 该算法在降水监测和预测中的操作应用具有显著的潜力.
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