{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:36:49Z","timestamp":1760150209708,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T00:00:00Z","timestamp":1696464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"],"award-info":[{"award-number":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"]}]},{"name":"Guangxi Natural Science Foundation of China","award":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"],"award-info":[{"award-number":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"]}]},{"name":"Foundation of Guilin University of Technology","award":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"],"award-info":[{"award-number":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"]}]},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics","award":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"],"award-info":[{"award-number":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"]}]},{"name":"National Natural Science Foundation of China","award":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"],"award-info":[{"award-number":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"]}]},{"name":"Innovation Project of Guangxi Graduate Education","award":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"],"award-info":[{"award-number":["42304018","2020GXNSFBA297145","GuikeAD23026177","GUTQDJJ6616032","21-238-21-05","42330105","42064002","42074035","YCSW2023341"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An accurate estimation of zenith wet delay (ZWD) is crucial for global navigation satellite system (GNSS) positioning and GNSS-based precipitable water vapor (PWV) inversion. The forecast Vienna Mapping Function 3 (VMF3-FC) is a forecast product provided by the Vienna Mapping Functions (VMF) data server based on the European Centre for Medium-Range Weather Forecasts (ECMWF)-based numerical weather prediction (NWP) model. The VMF3-FC can provide ZWD at any time and for any location worldwide; however, it has an uneven accuracy distribution and fails to match the application requirements in certain areas. To address this issue, in this study, a calibrated model for VMF3-FC ZWD, named the XZWD model, was developed by utilizing observation data from 492 radiosonde sites globally from 2019\u20132021 and the eXtreme Gradient Boosting (XGBoost) algorithm. The performance of the XZWD model was validated using 2022 observation data from the 492 radiosonde sites. The XZWD model yields a mean bias of \u22120.03 cm and a root-mean-square error (RMSE) of 1.64 cm. The XZWD model outperforms the global pressure and temperature 3 (GPT3) model, reducing the bias and RMSE by 94.64% and 58.90%, respectively. Meanwhile, the XZWD model outperforms VMF3-FC, with a reduction of 92.68% and 6.29% in bias and RMSE, respectively. Furthermore, the XZWD model reduces the impact of ZWD accuracy by latitude, height, and seasonal variations more effectively than the GPT3 model and VMF3-FC. Therefore, the XZWD model yields higher stability and accuracy in global ZWD forecasting.<\/jats:p>","DOI":"10.3390\/rs15194824","type":"journal-article","created":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T03:05:19Z","timestamp":1696475119000},"page":"4824","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning-Based Calibrated Model for Forecast Vienna Mapping Function 3 Zenith Wet Delay"],"prefix":"10.3390","volume":"15","author":[{"given":"Feijuan","family":"Li","sequence":"first","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7523-2672","authenticated-orcid":false,"given":"Junyu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006, China"}]},{"given":"Lilong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4241-3730","authenticated-orcid":false,"given":"Liangke","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9349-6265","authenticated-orcid":false,"given":"Lv","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006, China"}]},{"given":"Hongchang","family":"He","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1007\/s10291-021-01187-y","article-title":"Establishment and Assessment of a Zenith Wet Delay (ZWD) Augmentation Model","volume":"25","author":"Yang","year":"2021","journal-title":"GPS Solut."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s10291-022-01338-9","article-title":"Establishing a High-Precision Real-Time ZTD Model of China with GPS and ERA5 Historical Data and Its Application in PPP","volume":"27","author":"Xia","year":"2022","journal-title":"GPS Solut."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, H., Yao, Y., Hu, M., Xu, C., Su, X., Che, D., and Peng, W. (2022). A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning. Remote Sens., 14.","DOI":"10.3390\/rs14235921"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1007\/s10291-023-01513-6","article-title":"A Refined Zenith Tropospheric Delay Model for Mainland China Based on the Global Pressure and Temperature 3 (GPT3) Model and Random Forest","volume":"27","author":"Li","year":"2023","journal-title":"GPS Solut."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/JSTARS.2022.3228917","article-title":"High-Precision ZTD Model of Altitude-Related Correction","volume":"16","author":"Zhao","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s10291-023-01503-8","article-title":"A Global Zenith Tropospheric Delay Model with ERA5 and GNSS-Based ZTD Difference Correction","volume":"27","author":"Li","year":"2023","journal-title":"GPS Solut."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s00190-021-01535-3","article-title":"An Analysis of Multisource Tropospheric Hydrostatic Delays and Their Implications for GPS\/GLONASS PPP-Based Zenith Tropospheric Delay and Height Estimations","volume":"95","author":"Zhang","year":"2021","journal-title":"J. Geod."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1007\/s00190-022-01630-z","article-title":"Real-Time Wide-Area Precise Tropospheric Corrections (WAPTCs) Jointly Using GNSS and NWP Forecasts for China","volume":"96","author":"Zhang","year":"2022","journal-title":"J. Geod."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, L., Xu, Y., Yan, L., Wang, S., Liu, G., and Liu, F. (2020). A Regional NWP Tropospheric Delay Inversion Method Based on a General Regression Neural Network Model. Sensors, 20.","DOI":"10.3390\/s20113167"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1029\/RS020i006p01593","article-title":"Geodesy by Radio Interferometry: Effects of Atmospheric Modeling Errors on Estimates of Baseline Length","volume":"20","author":"Davis","year":"1985","journal-title":"Radio Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5804017","DOI":"10.1109\/TGRS.2022.3201146","article-title":"Flash Floods Prediction Using Precipitable Water Vapor Derived from GPS Tropospheric Path Delays over the Eastern Mediterranean","volume":"60","author":"Ziv","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, H., Liu, Y., Liu, Y., Cao, Y., Liang, H., Hu, H., Liang, J., and Tu, M. (2022). Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon. Remote Sens., 14.","DOI":"10.3390\/rs14010178"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"162225","DOI":"10.1016\/j.scitotenv.2023.162225","article-title":"Analyzing Correlations between GNSS Retrieved Precipitable Water Vapor and Land Surface Temperature after Earthquakes Occurrence","volume":"872","author":"Guo","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kawo, A., Van Schaeybroeck, B., Van Malderen, R., and Pottiaux, E. (2023). Precipitable Water Vapor in Regional Climate Models over Ethiopia: Model Evaluation and Climate Projections. Clim. Dyn., 1\u201321.","DOI":"10.1007\/s00382-023-06855-y"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105912","DOI":"10.1016\/j.atmosres.2021.105912","article-title":"Detecting Heavy Rainfall Using Anomaly-Based Percentile Thresholds of Predictors Derived from GNSS-PWV","volume":"265","author":"Li","year":"2022","journal-title":"Atmos. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15787","DOI":"10.1029\/92JD01517","article-title":"GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System","volume":"97","author":"Bevis","year":"1992","journal-title":"J. Geophys. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Graffigna, V., Hern\u00e1ndez-Pajares, M., Azpilicueta, F., and Gende, M. (2022). Comprehensive Study on the Tropospheric Wet Delay and Horizontal Gradients during a Severe Weather Event. Remote Sens., 14.","DOI":"10.3390\/rs14040888"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1109\/TGRS.2015.2456099","article-title":"A Comprehensive Evaluation and Analysis of the Performance of Multiple Tropospheric Models in China Region","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9644","DOI":"10.1080\/10106049.2021.2022015","article-title":"Accuracy Assessment of Reanalysis Datasets for GPS-PWV Estimation Using Indian IGS Stations Observations","volume":"37","author":"Srivastava","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s00190-007-0135-3","article-title":"Short Note: A Global Model of Pressure and Temperature for Geodetic Applications","volume":"81","author":"Boehm","year":"2007","journal-title":"J. Geod."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1002\/grl.50288","article-title":"GPT2: Empirical Slant Delay Model for Radio Space Geodetic Techniques","volume":"40","author":"Lagler","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s10291-014-0403-7","article-title":"Development of an Improved Empirical Model for Slant Delays in the Troposphere (GPT2w)","volume":"19","author":"Schindelegger","year":"2015","journal-title":"GPS Solut."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s00190-017-1066-2","article-title":"VMF3\/GPT3: Refined Discrete and Empirical Troposphere Mapping Functions","volume":"92","author":"Landskron","year":"2018","journal-title":"J. Geod."},{"key":"ref_24","unstructured":"Collins, J.P., and Langley, R.B. (1997). A Tropospheric Delay Model for the User of the Wide Area Augmentation System, University of New Brunswick."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s10291-007-0077-5","article-title":"UNB3m_pack: A Neutral Atmosphere Delay Package for Radiometric Space Techniques","volume":"12","author":"Leandro","year":"2008","journal-title":"GPS Solut."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1007\/s00190-019-01263-9","article-title":"Assessment of Forecast Vienna Mapping Function 1 for Real-Time Tropospheric Delay Modeling in GNSS","volume":"93","author":"Yuan","year":"2019","journal-title":"J. Geod."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1007\/s00190-019-01290-6","article-title":"On the Suitability of ERA5 in Hourly GPS Precipitable Water Vapor Retrieval over China","volume":"93","author":"Zhang","year":"2019","journal-title":"J. Geod."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, K., Wu, S., Wan, M., and Lin, Y. (2021). Retrieving Precipitable Water Vapor from Real-Time Precise Point Positioning Using VMF1\/VMF3 Forecasting Products. Remote Sens., 13.","DOI":"10.3390\/rs13163245"},{"key":"ref_29","first-page":"967","article-title":"Prediction of Tropospheric Wet Delay by an Artificial Neural Network Model Based on Meteorological and GNSS Data","volume":"23","author":"Selbesoglu","year":"2020","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2671","DOI":"10.1016\/j.asr.2022.01.003","article-title":"Modeling of Precipitable Water Vapor from GPS Observations Using Machine Learning and Tomography Methods","volume":"69","author":"Voosoghi","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1007\/s10291-022-01254-y","article-title":"A Coalescent Grid Model of Weighted Mean Temperature for China Region Based on Feedforward Neural Network Algorithm","volume":"26","author":"Zhu","year":"2022","journal-title":"GPS Solut."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e2021GL096408","DOI":"10.1029\/2021GL096408","article-title":"Machine Learning-Based Model for Real-Time GNSS Precipitable Water Vapor Sensing","volume":"49","author":"Zheng","year":"2022","journal-title":"Geophys. Res. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gu, Z., Cao, M., Wang, C., Yu, N., and Qing, H. (2022). Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model. Sustainability, 14.","DOI":"10.3390\/su141610421"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sridharan, M. (2023). Performance Augmentation Study on a Solar Flat Plate Water Collector System with Modified Absorber Flow Design and Its Performance Prediction Using the XGBoost Algorithm: A Machine Learning Approach. Iran. J. Sci. Technol. Trans. Mech. Eng., 1\u201312.","DOI":"10.1007\/s40997-023-00648-8"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xu, B., Tan, Y., Sun, W., Ma, T., Liu, H., and Wang, D. (2023). Study on the Prediction of the Uniaxial Compressive Strength of Rock Based on the SSA-XGBoost Model. Sustainability, 15.","DOI":"10.3390\/su15065201"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s10291-023-01436-2","article-title":"Near-Real-Time GNSS Tropospheric IWV Monitoring System for South America","volume":"27","author":"Mendoza","year":"2023","journal-title":"GPS Solut."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s00190-022-01691-0","article-title":"Parameterisation of the GNSS Troposphere Tomography Domain with Optimisation of the Nodes\u2019 Distribution","volume":"97","author":"Trzcina","year":"2022","journal-title":"J. Geod."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4165","DOI":"10.1002\/2016JD026000","article-title":"GPS PPP-Derived Precipitable Water Vapor Retrieval Based on Tm\/Ps from Multiple Sources of Meteorological Data Sets in China: GPS-PWV Retrieval on Multiple Data Sets","volume":"122","author":"Zhang","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1007\/s10291-023-01506-5","article-title":"A New Model for Vertical Adjustment of Precipitable Water Vapor with Consideration of the Time-Varying Lapse Rate","volume":"27","author":"Huang","year":"2023","journal-title":"GPS Solut."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s10291-022-01354-9","article-title":"An Improved Global Grid Model for Calibrating Zenith Tropospheric Delay for GNSS Applications","volume":"27","author":"Huang","year":"2022","journal-title":"GPS Solut."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lemoine, F., Kenyon, S.C., Factor, J., Trimmer, R., Pavlis, N., Chinn, D., Cox, C., Klosko, S., Luthcke, S., and Torrence, M. (1998). The Development of the Joint NASA GSFC and the National Imagery and Mapping Agency (NIMA) Geopotential Model EGM96.","DOI":"10.1007\/978-3-662-03482-8_62"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"111695","DOI":"10.1016\/j.measurement.2022.111695","article-title":"A Quality Control Method Based on Improved IQR for Estimating Multi-GNSS Real-Time Satellite Clock Offset","volume":"201","author":"Xie","year":"2022","journal-title":"Measurement"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"10727","DOI":"10.1175\/JCLI-D-19-0971.1","article-title":"A Drought Monitoring Method Based on Precipitable Water Vapor and Precipitation","volume":"33","author":"Zhao","year":"2020","journal-title":"J. Clim."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s00190-007-0170-0","article-title":"Implementation and Testing of the Gridded Vienna Mapping Function 1 (VMF1)","volume":"82","author":"Kouba","year":"2008","journal-title":"J. Geod."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e2021EA001815","DOI":"10.1029\/2021EA001815","article-title":"Assessment of the Troposphere Products Derived from VMF Data Server with ERA5 and IGS Data Over China","volume":"8","author":"Yang","year":"2021","journal-title":"Earth Space Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic Gradient Boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.asr.2022.04.043","article-title":"Developing a New Combined Model of Zenith Wet Delay by Using Neural Network","volume":"70","author":"Ding","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10291-023-01507-4","article-title":"Modeling Tropospheric Zenith Wet Delays in the Chinese Mainland Based on Machine Learning","volume":"27","author":"Li","year":"2023","journal-title":"GPS Solut."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"75257","DOI":"10.1109\/ACCESS.2022.3192011","article-title":"Short-Term Load Forecasting Method Based on Feature Preference Strategy and LightGBM-XGboost","volume":"10","author":"Yao","year":"2022","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e19281","DOI":"10.1016\/j.heliyon.2023.e19281","article-title":"Prediction and Analysis of Atmospheric Visibility in Five Terrain Types with Artificial Intelligence","volume":"9","author":"Liang","year":"2023","journal-title":"Heliyon"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s00190-023-01722-4","article-title":"TropNet: A Deep Spatiotemporal Neural Network for Tropospheric Delay Modeling and Forecasting","volume":"97","author":"Lu","year":"2023","journal-title":"J. Geod."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s00190-023-01719-z","article-title":"Tibetan Zenith Wet Delay Model with Refined Vertical Correction","volume":"97","author":"Xu","year":"2023","journal-title":"J. Geod."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Xia, P., Xia, J., Ye, S., and Xu, C. (2020). A New Method for Estimating Tropospheric Zenith Wet-Component Delay of GNSS Signals from Surface Meteorology Data. Remote Sens., 12.","DOI":"10.3390\/rs12213497"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4824\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:05:28Z","timestamp":1760130328000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4824"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,5]]},"references-count":54,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194824"],"URL":"https:\/\/doi.org\/10.3390\/rs15194824","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,10,5]]}}}