[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-127135-105":59,"doc-detail-127135-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","modeling-of-the-weighted-mean-temperature-based-on-the-random-forest-machine-learning-approach","Modeling of the weighted mean temperature based on the random forest machine learning approach","","This study presents a novel approach to modeling atmospheric weighted mean temperature (Tm) using the Random Forest (RF) machine learning algorithm. Tm is a critical parameter for atmospheric water vapor sounding with the Global Navigation Satellite System (GNSS) technique. The research incorporates spatial distribution, temporal variations, and surface meteorological variables to enhance the Tm model. Utilizing global atmospheric profiles from radiosonde (RS) data and GPS radio occultations (RO) measurements, the RF-based model demonstrates high accuracy. Comparisons with numerical integrations of atmospheric profiles in 2020 reveal that the RF model yields overall Root Mean Square (RMS) errors of 2.8 K when compared to RS data and 2.6 K when contrasted with GPS RO data. This work contributes significantly to the field of atmospheric remote sensing and GNSS meteorology by providing a robust and accurate method for Tm estimation.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/modeling-of-the-weighted-mean-temperature-based-on-the-random-forest-machine-learning-approach/127135/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/modeling-of-the-weighted-mean-temperature-based-on-the-random-forest-machine-learning-approach/127135.png","ImageObject",300,407,{"name":92,"@type":93},"Riley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is atmospheric weighted mean temperature (Tm)?","Question",{"text":113,"@type":114},"Atmospheric weighted mean temperature (Tm) is a significant parameter used in estimating atmospheric water vapor content through the Global Navigation Satellite System (GNSS) technique.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What methodology was used to model Tm?",{"text":118,"@type":114},"The Tm modeling was achieved using the Random Forest (RF) machine learning algorithm, integrating spatial distribution, temporal characteristics, and surface meteorological variables with global atmospheric profiles from radiosonde and GPS radio occultation data.",{"name":120,"@type":111,"acceptedAnswer":121},"How accurate is the RF-based Tm model?",{"text":122,"@type":114},"The RF-based Tm model demonstrates good accuracy, achieving overall RMS errors of 2.8 K compared to radiosonde data and 2.6 K compared to GPS RO data, based on comparisons with numerical integrations of atmospheric profiles in 2020.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},127135,1785937083,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":81},687207022233,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","EGU23-17204, updated on 26 Apr 2023  \n[https://doi.org/10.5194/egusphere-egu23-17204](https://doi.org/10.5194/egusphere-egu23-17204)[ ](https://doi.org/10.5194/egusphere-egu23-17204)EGU General Assembly 2023  \n© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.  \nModeling of the weighted mean temperature based on the random forest machine learning approach  \nQinzheng Li1,2, Johannes Böhm1, Linguo Yuan2, and Robert Weber1  \n1 Department of Geodesy and Geoinformation, Vienna University of Technology (TU Wien), Wien 1040, Austria 2 Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China  \nAtmospheric weighted mean temperature, Tm, is an important parameter in the Earth’s atmospheric water vapor sounding with the Global Navigation Satellite System (GNSS) technique. In this study, considering spatial distribution, time-varying characteristics, and the correlation with surface meteorological variables, Tm modeling is realized based on the random forest (RF) machine learning and global atmospheric profiles from radiosonde (RS) data and GPS radio occultations (RO) measurements. Comparisons of modeled results and numerical integrations of atmospheric profiles in 2020 show that the RF-based Tm model with surface meteorological parameters generally obtains a good accuracy with overall RMS errors of 2.8 K in comparison with RS data and 2.6 K in contrast to GPS RO data.","cbCaim60aActn62J","https://ap.wps.com/l/cbCaim60aActn62J","pdf",315881,"English","# Modeling of the weighted mean temperature based on the random forest machine learning approach\n## Introduction\n## Methodology\n## Results and Discussion","[{\"question\":\"What is atmospheric weighted mean temperature (Tm)?\",\"answer\":\"Atmospheric weighted mean temperature (Tm) is a significant parameter used in estimating atmospheric water vapor content through the Global Navigation Satellite System (GNSS) technique.\"},{\"question\":\"What methodology was used to model Tm?\",\"answer\":\"The Tm modeling was achieved using the Random Forest (RF) machine learning algorithm, integrating spatial distribution, temporal characteristics, and surface meteorological variables with global atmospheric profiles from radiosonde and GPS radio occultation data.\"},{\"question\":\"How accurate is the RF-based Tm model?\",\"answer\":\"The RF-based Tm model demonstrates good accuracy, achieving overall RMS errors of 2.8 K compared to radiosonde data and 2.6 K compared to GPS RO data, based on comparisons with numerical integrations of atmospheric profiles in 2020.\"}]","Modeling of the weighted mean temperature based on the random forest machine learning approach | PDF"]