概括
这项研究引入了一种新的遗传BP (GA-BP) 神经网络,用于使用激光雷达精确的气溶光学深度 (AOD) 和气溶灭绝系数概况 (AECP) 检索. 该方法动态优化了气溶灭绝-反散比 (AE-BR),提高了反转的准确性.
科学领域:
- 大气科学 大气科学
- 遥感技术 遥感技术 遥感技术
- 计算方法 计算方法
背景情况:
- 使用激光雷达精确检索气溶光学厚度 (AOD) 和气溶灭绝系数概况 (AECP),对于了解大气性质至关重要.
- 选择合适的气溶灭绝-反向散射比 (AE-BR) 是激光雷达逆转精度的一个关键挑战.
研究的目的:
- 使用遗传BP (GA-BP) 神经网络开发和验证AOD和AECP的新型倒置算法.
- 为了提高基于激光雷达的大气气溶特性检索的准确性.
主要方法:
- 利用CE318太阳光计和激光雷达的同时测量.
- 开发了一个GA-BP神经网络,以建立激光雷达回声信号和AOD之间的关系.
- 基于GA-BP输出对AECP逆转的AE-BR实时动态校正.
主要成果:
- 实现了准确的AOD反转,平均绝对误差为0.0156.6.
- 确定最佳的AE-BR范围为20-50 sr.
- 证明了AECP的实时动态校正,显示出强大的概括能力.
结论:
- 该GA-BP神经网络方法提供准确的AOD和AECP检索.
- 动态调整AE-BR显著提高了AECP的反转精度.
- 拟议的方法适用于基于激光雷达的实际大气气溶研究,并且有效.
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