Katia Lamer, Pavlos Kollias, Edward P. Luke, Bernat P. Treserras, Mariko Oue, Brenda Dolan
{"title":"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","authors":"Katia Lamer, Pavlos Kollias, Edward P. Luke, Bernat P. Treserras, Mariko Oue, Brenda Dolan","doi":"10.1175/jtech-d-23-0043.1","DOIUrl":null,"url":null,"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.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":"41 1","pages":"0"},"PeriodicalIF":1.9000,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multisensor Agile Adaptive Sampling (MAAS): a methodology to collect radar observations of convective cell lifecycle\",\"authors\":\"Katia Lamer, Pavlos Kollias, Edward P. Luke, Bernat P. 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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.\",\"PeriodicalId\":15074,\"journal\":{\"name\":\"Journal of Atmospheric and Oceanic Technology\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Atmospheric and Oceanic Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1175/jtech-d-23-0043.1\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, OCEAN\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Atmospheric and Oceanic Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1175/jtech-d-23-0043.1","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, OCEAN","Score":null,"Total":0}
Multisensor Agile Adaptive Sampling (MAAS): a methodology to collect radar observations of convective cell lifecycle
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.
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.