Multisensor Agile Adaptive Sampling (MAAS): a methodology to collect radar observations of convective cell lifecycle

IF 1.9 4区 地球科学 Q2 ENGINEERING, OCEAN Journal of Atmospheric and Oceanic Technology Pub Date : 2023-09-29 DOI:10.1175/jtech-d-23-0043.1
Katia Lamer, Pavlos Kollias, Edward P. Luke, Bernat P. Treserras, Mariko Oue, Brenda Dolan
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引用次数: 1

Abstract

Abstract Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at sub-km scale), and temporal evolution (at ~2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (the CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect 3 sector Plan Position Indicator (PPI) scans towards the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of 3-6 Range Height Indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a pre-determined set of criteria. Between 01 June and 30 September 2022 over 315,000 vertical cross-section observations were collected by the C-band radars through ~1,300 unique isolated convective cells, most of which were observed for over 15-min of their lifecycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.
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多传感器敏捷自适应采样(MAAS):一种收集对流细胞生命周期雷达观测数据的方法
摘要多传感器敏捷自适应采样(MAAS)是一种智能传感框架,可提高观测对流单体垂直结构(几乎没有间隙)、空间变异(亚公里尺度)和时间演化(~2分钟分辨率)的可能性。通过自动分析最新的NEXRAD数据来识别、表征、跟踪和临近预报在休斯顿区域形成的所有对流单元的位置,MAAS的改进引导了两台机械扫描c波段雷达(CSAPR2和CHIVO)。MAAS使用预定规则列表或实时用户输入来选择要由c波段雷达跟踪和采样的对流单元。CSAPR2跟踪雷达首先负责收集3扇区计划位置指示(PPI)扫描到选定的单元。使用边缘计算机处理PPI扫描来识别选定细胞内的其他目标。在不到2分钟的时间内,CSAPR2和CHIVO雷达都能够收集到3-6束距离高度指示(RHI)扫描,指向选定单元内的不同目标。只要细胞符合预先确定的一组标准,就沿着细胞平流路径依次收集束。2022年6月1日至9月30日期间,c波段雷达通过约1300个独特的孤立对流单体收集了超过315,000个垂直截面观测数据,其中大多数观测时间超过15分钟。据我们所知,这个主要通过自动方式收集的数据集构成了同类数据集中最大的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.50
自引率
9.10%
发文量
135
审稿时长
3 months
期刊介绍: The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.
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