{"title":"面向边界层分类的仪器组合","authors":"Thomas Rieutord, Pauline Martinet, Alexandre Paci","doi":"10.1002/asl.1144","DOIUrl":null,"url":null,"abstract":"<p>To handle the complexity of the atmospheric boundary layer (ABL) and make accurate feature detection (top height, low-level jets, inversions, etc.), a prior necessary step is to identify the type of boundary layer. This study proposes a new method to identify the boundary layer type through unsupervised classification and the synergistic use of ground-based remote sensing. Unsupervised classification is used to lighten the human supervision. The new classification was applied to a 1-day case study collected during wintertime in the Arve River valley near Chamonix–Mont-Blanc during the Passy-2015 field experiment. The ABL classification obtained from microwave radiometer and ceilometer observations (ground-based remote sensors [GBReS]) combination is compared with high-frequency radiosoundings (RS) data and the French convective scale AROME model outputs. Classifications from RS and GBReS broadly agree, demonstrating the good behavior of the method, AROME leading to different results at night. The difference of AROME is likely due to the different nature of the data (model fields are smoother and include forecasting errors). The results show the ability of unsupervised classification to segment relevant objects in the boundary layer and the benefit to use a combination of GBReS.</p>","PeriodicalId":50734,"journal":{"name":"Atmospheric Science Letters","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asl.1144","citationCount":"0","resultStr":"{\"title\":\"Toward instrument combination for boundary layer classification\",\"authors\":\"Thomas Rieutord, Pauline Martinet, Alexandre Paci\",\"doi\":\"10.1002/asl.1144\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To handle the complexity of the atmospheric boundary layer (ABL) and make accurate feature detection (top height, low-level jets, inversions, etc.), a prior necessary step is to identify the type of boundary layer. This study proposes a new method to identify the boundary layer type through unsupervised classification and the synergistic use of ground-based remote sensing. Unsupervised classification is used to lighten the human supervision. The new classification was applied to a 1-day case study collected during wintertime in the Arve River valley near Chamonix–Mont-Blanc during the Passy-2015 field experiment. The ABL classification obtained from microwave radiometer and ceilometer observations (ground-based remote sensors [GBReS]) combination is compared with high-frequency radiosoundings (RS) data and the French convective scale AROME model outputs. Classifications from RS and GBReS broadly agree, demonstrating the good behavior of the method, AROME leading to different results at night. The difference of AROME is likely due to the different nature of the data (model fields are smoother and include forecasting errors). The results show the ability of unsupervised classification to segment relevant objects in the boundary layer and the benefit to use a combination of GBReS.</p>\",\"PeriodicalId\":50734,\"journal\":{\"name\":\"Atmospheric Science Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2022-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asl.1144\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Atmospheric Science Letters\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/asl.1144\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Science Letters","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/asl.1144","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Toward instrument combination for boundary layer classification
To handle the complexity of the atmospheric boundary layer (ABL) and make accurate feature detection (top height, low-level jets, inversions, etc.), a prior necessary step is to identify the type of boundary layer. This study proposes a new method to identify the boundary layer type through unsupervised classification and the synergistic use of ground-based remote sensing. Unsupervised classification is used to lighten the human supervision. The new classification was applied to a 1-day case study collected during wintertime in the Arve River valley near Chamonix–Mont-Blanc during the Passy-2015 field experiment. The ABL classification obtained from microwave radiometer and ceilometer observations (ground-based remote sensors [GBReS]) combination is compared with high-frequency radiosoundings (RS) data and the French convective scale AROME model outputs. Classifications from RS and GBReS broadly agree, demonstrating the good behavior of the method, AROME leading to different results at night. The difference of AROME is likely due to the different nature of the data (model fields are smoother and include forecasting errors). The results show the ability of unsupervised classification to segment relevant objects in the boundary layer and the benefit to use a combination of GBReS.
期刊介绍:
Atmospheric Science Letters (ASL) is a wholly Open Access electronic journal. Its aim is to provide a fully peer reviewed publication route for new shorter contributions in the field of atmospheric and closely related sciences. Through its ability to publish shorter contributions more rapidly than conventional journals, ASL offers a framework that promotes new understanding and creates scientific debate - providing a platform for discussing scientific issues and techniques.
We encourage the presentation of multi-disciplinary work and contributions that utilise ideas and techniques from parallel areas. We particularly welcome contributions that maximise the visualisation capabilities offered by a purely on-line journal. ASL welcomes papers in the fields of: Dynamical meteorology; Ocean-atmosphere systems; Climate change, variability and impacts; New or improved observations from instrumentation; Hydrometeorology; Numerical weather prediction; Data assimilation and ensemble forecasting; Physical processes of the atmosphere; Land surface-atmosphere systems.