遥感采集云顶高度使用神经网络和数据从云-气溶激光雷达与直角偏振
Yinhe Cheng1, Hongjian He1, Qiangyu Xue1
1School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
神经网络模型通过使用Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 数据从MODIS数据中改进了云顶高度 (CTH) 的检索. 综合输入模型显示出最佳性能,特定参数对准确性至关重要.
科学领域:
- 大气科学 大气科学
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 准确检索云顶高度 (CTH) 对于气候建模和天气预报至关重要.
- MODIS数据提供了广泛的覆盖范围,但需要改进的方法来精确确定CTH.
- CALIOP数据提供了高精度的垂直云配置文件,作为一个有价值的参考.
研究的目的:
- 为了提高云顶高度 (CTH) 从中等分辨率成像光谱辐射计 (MODIS) 数据的检索精度.
- 评估不同神经网络输入策略对CTH检索的有效性.
- 为了确定影响CTH准确性的关键大气参数.
主要方法:
- 使用MODIS数据开发神经网络模型,使用三个输入类型:云参数,校准辐射和两者的组合.
- 模型性能与有直角偏振 (CALIOP) 的云气溶激光雷达数据的验证.
- 统计分析用于比较不同输入策略的准确性和适用性.
主要成果:
- 使用MODIS云参数和校准辐射输入组合的模型在CTH检索中表现出卓越的性能.
- 云顶压力 (CTP) 和云顶温度被确定为准确CTH的关键因素.
- 一个使用较少输入 (云参数和校准辐射) 的推模型在案例研究中显示出高可信度和适用性,与CALIPSO CTHs非常相匹配.
结论:
- 神经网络模型显著提高了MODIS数据中的CTH检索精度.
- 输入参数的选择,特别是云参数和校准辐射,对于模型性能至关重要.
- 推的简化模型为CTH确定提供了可靠和高效的方法,有利于气候和天气研究.
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