{"title":"利用物理信息机器学习法对雨水污染的地基微波辐射计数据进行雨水检测","authors":"Wenyue Wang , Wenzhi Fan , Klemens Hocke","doi":"10.1016/j.jhydrol.2024.132365","DOIUrl":null,"url":null,"abstract":"<div><div>Because the radiation signal is strongly influenced by emission and scattering from rain, microwave radiometer data suffer from rain contamination. The traditional method of using rain gauges to detect rain for microwave radiometers has limitations. For example, it can only detect rain that reaches the ground and is ineffective for raindrops suspended in the atmosphere that can still contaminate remote sensing data. This article presents a rain detection method for microwave radiometer measurements, based on Gradient Boosted Decision Trees (GBDT). First, the characteristic that the increase in microwave radiometer brightness temperature when raindrops are present in the atmosphere, along with the seasonal dependency of rainfall patterns, is combined with meteorological variables to form feature vectors. Then, the GBDT is employed to classify data into rain-free and rain-contaminated categories. Microwave radiometer (MWR) measurements and simultaneous Micro Rain Radar (MRR) target classification collected from the Swiss Plateau in 2008 are utilized to train the model, which is subsequently tested using two testing schemes: ten-fold cross-validation technique and time series test sets. Compared with the detection accuracy of the integrated liquid water (ILW) threshold method (73.6% and 68.3%) in both testing schemes, our GBDT-based method achieved superior accuracy, recording approximately 100% and 98.4%, respectively. The proposed method exhibits strong generalization capabilities, allowing it to directly detect rain contamination in time series data and effectively overcome the time dependence of rainfall occurrence. In addition, compared with the ILW threshold method, the GBDT-based method considers various rainfall patterns contained in various seasons. Features selected for this method enable its direct application to other tropospheric microwave radiometer systems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132365"},"PeriodicalIF":5.9000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rain detection for rain-contaminated ground-based microwave radiometer data using physics-informed machine learning method\",\"authors\":\"Wenyue Wang , Wenzhi Fan , Klemens Hocke\",\"doi\":\"10.1016/j.jhydrol.2024.132365\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Because the radiation signal is strongly influenced by emission and scattering from rain, microwave radiometer data suffer from rain contamination. The traditional method of using rain gauges to detect rain for microwave radiometers has limitations. For example, it can only detect rain that reaches the ground and is ineffective for raindrops suspended in the atmosphere that can still contaminate remote sensing data. This article presents a rain detection method for microwave radiometer measurements, based on Gradient Boosted Decision Trees (GBDT). First, the characteristic that the increase in microwave radiometer brightness temperature when raindrops are present in the atmosphere, along with the seasonal dependency of rainfall patterns, is combined with meteorological variables to form feature vectors. Then, the GBDT is employed to classify data into rain-free and rain-contaminated categories. Microwave radiometer (MWR) measurements and simultaneous Micro Rain Radar (MRR) target classification collected from the Swiss Plateau in 2008 are utilized to train the model, which is subsequently tested using two testing schemes: ten-fold cross-validation technique and time series test sets. Compared with the detection accuracy of the integrated liquid water (ILW) threshold method (73.6% and 68.3%) in both testing schemes, our GBDT-based method achieved superior accuracy, recording approximately 100% and 98.4%, respectively. The proposed method exhibits strong generalization capabilities, allowing it to directly detect rain contamination in time series data and effectively overcome the time dependence of rainfall occurrence. In addition, compared with the ILW threshold method, the GBDT-based method considers various rainfall patterns contained in various seasons. Features selected for this method enable its direct application to other tropospheric microwave radiometer systems.</div></div>\",\"PeriodicalId\":362,\"journal\":{\"name\":\"Journal of Hydrology\",\"volume\":\"648 \",\"pages\":\"Article 132365\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hydrology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S002216942401761X\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrology","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S002216942401761X","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Rain detection for rain-contaminated ground-based microwave radiometer data using physics-informed machine learning method
Because the radiation signal is strongly influenced by emission and scattering from rain, microwave radiometer data suffer from rain contamination. The traditional method of using rain gauges to detect rain for microwave radiometers has limitations. For example, it can only detect rain that reaches the ground and is ineffective for raindrops suspended in the atmosphere that can still contaminate remote sensing data. This article presents a rain detection method for microwave radiometer measurements, based on Gradient Boosted Decision Trees (GBDT). First, the characteristic that the increase in microwave radiometer brightness temperature when raindrops are present in the atmosphere, along with the seasonal dependency of rainfall patterns, is combined with meteorological variables to form feature vectors. Then, the GBDT is employed to classify data into rain-free and rain-contaminated categories. Microwave radiometer (MWR) measurements and simultaneous Micro Rain Radar (MRR) target classification collected from the Swiss Plateau in 2008 are utilized to train the model, which is subsequently tested using two testing schemes: ten-fold cross-validation technique and time series test sets. Compared with the detection accuracy of the integrated liquid water (ILW) threshold method (73.6% and 68.3%) in both testing schemes, our GBDT-based method achieved superior accuracy, recording approximately 100% and 98.4%, respectively. The proposed method exhibits strong generalization capabilities, allowing it to directly detect rain contamination in time series data and effectively overcome the time dependence of rainfall occurrence. In addition, compared with the ILW threshold method, the GBDT-based method considers various rainfall patterns contained in various seasons. Features selected for this method enable its direct application to other tropospheric microwave radiometer systems.
期刊介绍:
The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.