气溶类型从线性估计复杂气溶混合物的分析分离 (ATLAS) 和微物理参数从多波长激光雷达数据的改进识别,第二部分:案例研究
概括
我们开发了一种新的两阶段方法,将ATLAS和TiARA结合起来,从激光雷达数据中识别粒子微物理参数. 这种方法改善了气溶混合物分析,为大气粒子特性提供了详细的见解.
科学领域:
- 大气科学 大气科学
- 遥感 遥感 遥感 遥感
- 气溶科学 气溶科学
背景情况:
- 从多波长激光雷达测量中识别粒子微物理参数 (PMP) 是具有挑战性的,因为这个问题的性质不好.
- 大气气溶混合物 (AMs) 是复杂的,这使得很难确定单个成分的特性.
研究的目的:
- 开发和验证一种新的方法,以从激光雷达测量中更好地识别PMP.
- 调查了解气溶类型及其计数对PMP检索准确性的影响.
- 证明新方法在分析复杂气溶混合物的能力.
主要方法:
- 根据分析分离 (ATLAS) 的线性估计开发了气溶类型,以基于激光雷达光学数据将复杂的AM分解为单个气溶类型.
- 应用自动无监督数据反转方法TiARA (提可诺夫高级规范化算法) 来将单个气溶类型的光学数据反转为PMP.
- 实施了两个阶段的协同ATLAS-TiARA方法,应用于ORACLES-2016,SHADOW和里尔大学的激光雷达测量.
主要成果:
- 通过ATLAS-TiARA方法,成功地分解了两,三和四组合的AM.
- 获取的PMP显示与ORACLES-2016运动的实地测量有很好的一致性,数量度和单散射白度的偏差不超过25%和0.05,分别.
- 确定了细模式 (城市,烟雾/雾) 和粗模式 (尘埃,海洋,花粉) 颗粒.
结论:
- 新的ATLAS-TiARA方法提供了对微物理粒子属性的更详细的洞察.
- 协同方法提高了在复杂的大气气溶混合物中PMP识别的准确性.
- 这种方法在分析用于气溶表征的激光雷达数据方面取得了重大进展.
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