{"title":"基于随机森林算法的EOS-07 MHS卫星观测评估和特定湿度剖面的检索","authors":"Manoj Kumar Mishra, Rishi Kumar Gangwar, Munn Vinayak Shukla, Prashant Kumar, Pradeep Kumar Thapliyal","doi":"10.1016/j.rse.2025.115066","DOIUrl":null,"url":null,"abstract":"<div><div>An in-house-developed millimeter-wave humidity sounder onboard EOS-07 (EOS-07 MHS), launched in February 2023, operates at six frequencies around the 183.3 GHz water vapor absorption band. This study presents a preliminary performance assessment of EOS-07 MHS, including brightness temperature validation, humidity profile retrieval methodology and its validation.</div><div>Under clear-sky conditions, the biases in brightness temperature measured by EOS-07 MHS, relative to RTTOV simulations were within ±1 K, except for channels 1 and 6. Similarly, intercomparisons with ATMS observations showed biases within ±1 K and a standard deviation of 2–3 K.</div><div>A random forest-based method was employed to retrieve specific humidity profiles from EOS-07 MHS observations demonstrated agreement with ERA5 reanalysis and radiosonde observations. Compared with radiosonde data, the mean bias and standard deviation of retrieved specific humidity were approximately 0.78 g/kg and 2.3 g/kg, respectively. The mean percentage bias was within ±20 % below the 800 hPa pressure level, and ranged between ±20 % and ± 40 % above the 800 hPa pressure level. Relative to ERA5, the mean bias and root-mean-square deviation (RMSD) were under 30 % and 50 %, respectively. The estimated total precipitable water vapor showed a mean bias of 1.7–3.1 mm and a standard deviation of 5.2–5.7 mm compared to ERA5. Additionally, the EOS-07 MHS data were assimilated into the WRF model, resulting in improved atmospheric analyses and forecasts. A month-long cyclic assimilation experiment demonstrated consistent enhancements in moisture representation across the lower and middle atmosphere.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115066"},"PeriodicalIF":11.4000,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessment of EOS-07 MHS satellite observations and retrieval of specific humidity profiles using a random forest-based algorithm\",\"authors\":\"Manoj Kumar Mishra, Rishi Kumar Gangwar, Munn Vinayak Shukla, Prashant Kumar, Pradeep Kumar Thapliyal\",\"doi\":\"10.1016/j.rse.2025.115066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>An in-house-developed millimeter-wave humidity sounder onboard EOS-07 (EOS-07 MHS), launched in February 2023, operates at six frequencies around the 183.3 GHz water vapor absorption band. This study presents a preliminary performance assessment of EOS-07 MHS, including brightness temperature validation, humidity profile retrieval methodology and its validation.</div><div>Under clear-sky conditions, the biases in brightness temperature measured by EOS-07 MHS, relative to RTTOV simulations were within ±1 K, except for channels 1 and 6. Similarly, intercomparisons with ATMS observations showed biases within ±1 K and a standard deviation of 2–3 K.</div><div>A random forest-based method was employed to retrieve specific humidity profiles from EOS-07 MHS observations demonstrated agreement with ERA5 reanalysis and radiosonde observations. Compared with radiosonde data, the mean bias and standard deviation of retrieved specific humidity were approximately 0.78 g/kg and 2.3 g/kg, respectively. The mean percentage bias was within ±20 % below the 800 hPa pressure level, and ranged between ±20 % and ± 40 % above the 800 hPa pressure level. Relative to ERA5, the mean bias and root-mean-square deviation (RMSD) were under 30 % and 50 %, respectively. The estimated total precipitable water vapor showed a mean bias of 1.7–3.1 mm and a standard deviation of 5.2–5.7 mm compared to ERA5. Additionally, the EOS-07 MHS data were assimilated into the WRF model, resulting in improved atmospheric analyses and forecasts. A month-long cyclic assimilation experiment demonstrated consistent enhancements in moisture representation across the lower and middle atmosphere.</div></div>\",\"PeriodicalId\":417,\"journal\":{\"name\":\"Remote Sensing of Environment\",\"volume\":\"332 \",\"pages\":\"Article 115066\"},\"PeriodicalIF\":11.4000,\"publicationDate\":\"2025-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing of Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0034425725004705\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425725004705","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Assessment of EOS-07 MHS satellite observations and retrieval of specific humidity profiles using a random forest-based algorithm
An in-house-developed millimeter-wave humidity sounder onboard EOS-07 (EOS-07 MHS), launched in February 2023, operates at six frequencies around the 183.3 GHz water vapor absorption band. This study presents a preliminary performance assessment of EOS-07 MHS, including brightness temperature validation, humidity profile retrieval methodology and its validation.
Under clear-sky conditions, the biases in brightness temperature measured by EOS-07 MHS, relative to RTTOV simulations were within ±1 K, except for channels 1 and 6. Similarly, intercomparisons with ATMS observations showed biases within ±1 K and a standard deviation of 2–3 K.
A random forest-based method was employed to retrieve specific humidity profiles from EOS-07 MHS observations demonstrated agreement with ERA5 reanalysis and radiosonde observations. Compared with radiosonde data, the mean bias and standard deviation of retrieved specific humidity were approximately 0.78 g/kg and 2.3 g/kg, respectively. The mean percentage bias was within ±20 % below the 800 hPa pressure level, and ranged between ±20 % and ± 40 % above the 800 hPa pressure level. Relative to ERA5, the mean bias and root-mean-square deviation (RMSD) were under 30 % and 50 %, respectively. The estimated total precipitable water vapor showed a mean bias of 1.7–3.1 mm and a standard deviation of 5.2–5.7 mm compared to ERA5. Additionally, the EOS-07 MHS data were assimilated into the WRF model, resulting in improved atmospheric analyses and forecasts. A month-long cyclic assimilation experiment demonstrated consistent enhancements in moisture representation across the lower and middle atmosphere.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.