Maximizing the volume of collocated data from two coordinated suborbital platforms

J. Schlosser, Ryan Bennett, Brian Cairns, Gao Chen, B. Collister, J. Hair, Michael Jones, M. Shook, A. Sorooshian, K. Thornhill, L. Ziemba, S. Stamnes
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Abstract

Suborbital (e.g., airborne) campaigns that carry advanced remote sensing and in situ payloads provide detailed observations of atmospheric processes, but can be challenging to use when it is necessary to geographically collocate data from multiple platforms that make repeated observations of a given geographic location at different altitudes. This study reports on a data collocation algorithm that maximizes the volume of collocated data from two coordinated suborbital platforms and demonstrates its value using data from the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) suborbital mission. A robust data collocation algorithm is critical for the success of the ACTIVATE mission goal to develop new and improved remote sensing algorithms, and quantify their performance. We demonstrate the value of these collocated data to quantify the performance of a recently developed vertically-resolved lidar + polarimeter-derived aerosol particle number concentration (Na) product, resulting in a range-normalized mean absolute deviation (NMAD) of 9% compared to in situ measurements. We also show that this collocation algorithm increases the volume of collocated ACTIVATE data by 21% compared to using only nearest neighbor finding algorithms alone. Additional to the benefits demonstrated within this study, the data files and routines produced by this algorithm have solved both the critical collocation and the collocation application steps for researchers who require collocated data for their own studies. This freely available and open source collocation algorithm can be applied to future suborbital campaigns that, like ACTIVATE, use multiple platforms to conduct coordinated observations, e.g., a remote sensing aircraft together with in situ data collected from suborbital platforms.
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最大限度地提高两个协调亚轨道平台的共用数据量
搭载先进遥感和原位有效载荷的亚轨道(如机载)活动提供了对大气过程的详细观测,但当需要在不同高度对特定地理位置进行重复观测的多个平台的数据进行地理定位时,使用亚轨道活动可能具有挑战性。本研究报告介绍了一种数据搭配算法,该算法可最大限度地利用来自两个协调亚轨道平台的搭配数据量,并利用来自美国国家航空航天局西大西洋实验(ACTIVATE)亚轨道任务的气溶胶云气象学相互作用数据证明了该算法的价值。ACTIVATE 任务的目标是开发新的和改进的遥感算法,并对其性能进行量化。我们展示了这些配准数据的价值,以量化最近开发的垂直分辨激光雷达+偏振计衍生气溶胶粒子数浓度(Na)产品的性能,结果与现场测量相比,范围归一化平均绝对偏差(NMAD)为9%。我们还表明,与单独使用近邻搜索算法相比,这种配准算法使配准的 ACTIVATE 数据量增加了 21%。除了本研究中展示的好处之外,该算法生成的数据文件和例程还为需要配准数据进行研究的研究人员解决了关键配准和配准应用步骤。这种免费提供的开放源码配准算法可应用于未来的亚轨道活动,如 ACTIVATE,使用多个平台进行协调观测,如遥感飞机与亚轨道平台收集的现场数据。
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