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Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution / Estera Trzcina in Journal of geodesy, vol 97 n° 1 (January 2023)  

Public [article]  inJournal of geodesy > vol 97 n° 1 (January 2023) . - n° 2 Titre : Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution Type de document : Article/Communication Auteurs : Estera Trzcina, Auteur ; Witold Rohm, Auteur ; Kamil Smolak, Auteur Année de publication : 2023 Article en page(s) : n° 2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] interpolation bilinéaire [Termes IGN] modèle météorologique [Termes IGN] optimisation (mathématiques) [Termes IGN] radiosondage [Termes IGN] récepteur GNSS [Termes IGN] retard troposphérique [Termes IGN] retard troposphérique zénithal [Termes IGN] système de grille globale discrète [Termes IGN] teneur en vapeur d'eau [Termes IGN] tomographie [Termes IGN] troposphère Résumé : (auteur) Water vapour is a highly variable constituent of the troposphere; thus, its high-resolution measurements are of great importance to weather prediction systems. The Global Navigation Satellite Systems (GNSS) are operationally used in the estimation of the tropospheric state and assimilation of the results into the weather models. One of the GNSS techniques of troposphere sensing is tomography which provides 3-D fields of wet refractivity. The tomographic results have been successfully assimilated into the numerical weather models, showing the great potential of this technique. The GNSS tomography can be based on two different approaches to the parameterisation of the model’s domain, i.e. block (voxel-based) or grid (node-based) approach. Regardless of the parameterisation approach, the tomographic domain should be discretised, which is usually performed in a regular manner, with a grid resolution depending on the mean distance between the GNSS receivers. In this work, we propose a new parameterisation approach based on the optimisation of the tomographic nodes’ location, taking into account the non-uniform distribution of the GNSS information in the troposphere. The experiment was performed using a dense network of 16 low-cost multi-GNSS receivers located in Wrocław and its suburbs, with a mean distance of 3 km. Cross-validation of four different parameterisation approaches is presented. The validation is performed based on the Weather Research and Forecasting model as well as radiosonde observations. The new approach improves the results of wet refractivity estimation by 0.5–2 ppm in terms of RMSE, especially for altitudes of 0.5–2.0 km. Numéro de notice : A2023-044 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1007/s00190-022-01691-0 Date de publication en ligne : 30/12/2022 En ligne : https://doi.org/10.1007/s00190-022-01691-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102343 [article]

Assessing ZWD models in delay and height domains using data from stations in different climate regions / Thainara Munhoz Alexandre de Lima in Applied geomatics, vol 14 n° 1 (March 2022)  

Public [article]  inApplied geomatics > vol 14 n° 1 (March 2022) . - pp 93 - 103 Titre : Assessing ZWD models in delay and height domains using data from stations in different climate regions Type de document : Article/Communication Auteurs : Thainara Munhoz Alexandre de Lima, Auteur ; Marcelo Santos, Auteur ; Daniele Barroca Marra Alves, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 93 - 103 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] climat [Termes IGN] correction du signal [Termes IGN] données GNSS [Termes IGN] modèle atmosphérique [Termes IGN] modèle empirique [Termes IGN] modèle météorologique [Termes IGN] positionnement ponctuel précis [Termes IGN] prévision météorologique [Termes IGN] radiosondage [Termes IGN] retard troposphérique zénithal Résumé : (auteur) Global Navigation Satellite System (GNSS) has revolutionized activities involving geodetic positioning. To achieve a desired accuracy, it is essential to model the atmosphere in an appropriate way. With respect to the neutral atmosphere, the signal sent by the satellite suffers a delay when crossing this layer during its travel to the receiver on the surface, the so-called neutral atmospheric delay. Although empirical models exist, they may not be suitable to represent microclimatic variations in different regions of the globe due to peculiarities that exist in diverse areas. To minimize this limitation, correction models based on numerical weather prediction (NWP) emerge. They allow the assessment of the delay from local atmospheric parameters and the evaluation of atmospheric particularities of each region. In addition, another way to obtain neutral atmosphere delay is by making use of data from radiosondes, which measure atmospheric data at various altitude levels. The main objective of this article is to investigate the performance of different models using GNSS data collected in countries with different climatic conditions. Assessment is performed on the positioning domain using the precise point positioning (PPP) technique. The results show that the proximity between the NWP-based models and radiosondes was approximately 3 cm, and that between empirical models was 5 cm, with variations that depended on the model and the region. Regarding the impact on the height component, the difference between the accuracy of the empirical and NWP models was approximately 16 cm. Numéro de notice : A2022-219 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s12518-021-00414-y En ligne : https://doi.org/10.1007/s12518-021-00414-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100088 [article]

Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods / Mir Reza Ghaffari Razin in Advances in space research, vol 69 n° 7 (April 2022)  

Public [article]  inAdvances in space research > vol 69 n° 7 (April 2022) . - pp 2671 - 2681 Titre : Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods Type de document : Article/Communication Auteurs : Mir Reza Ghaffari Razin, Auteur ; Behzad Voosoghi, Auteur Année de publication : 2022 Article en page(s) : pp 2671 - 2681 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] algorithme génétique [Termes IGN] apprentissage automatique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] Inférence floue [Termes IGN] Iran [Termes IGN] précipitation [Termes IGN] radiosondage [Termes IGN] réseau neuronal artificiel [Termes IGN] retard hydrostatique [Termes IGN] retard troposphérique zénithal [Termes IGN] tomographie par GPS [Termes IGN] vapeur d'eau [Termes IGN] voxel Résumé : (auteur) This paper studies the application of two machine learning methods to model precipitable water vapor (PWV) using observations of 23 GPS stations from the local GPS network of north-west of Iran in 2011. In a first step, the zenith tropospheric delay (ZTD) and zenith hydrostatic delay (ZHD) is calculated with the Bernese GNSS software and Saastamoinen model as revised by Davis, respectively. Then, by subtracting the ZHD from the ZTD, the zenith wet delay (ZWD) is obtained at each GPS station, for all times. In a second step, ZWD is modeled by two different machine learning methods, based on the latitude, longitude, DOY, time, relative humidity, temperature and pressure. After training a Support Vector Machine (SVM) and an Artificial Neural Network (ANN), ZWD temporal and spatial variations are estimated. Using the formula by Bevis, the ZWD can be converted to PWV at any time and space, for each machine learning method. The accuracy of the two new models is evaluated using control stations, exterior and radiosonde station, whose observations were not used in the training step. Also, all the results of the SVM and ANN are compared with a voxel-based tomography (VBT) model. In the control and exterior stations, ZWD estimated by the SVM (ZWDSVM) and ANN (ZWDANN) is compared with the ZWD obtained from the GPS (ZWDGPS). Also, in the control and exterior stations, precise point positioning (PPP) is used to evaluate the accuracy of the new models. In the radiosonde station, the PWV of the new models (PWVSVM, PWVANN) is compared with the radiosonde PWV (PWVradiosonde) and voxel-based PWV (PWVVBT). The averaged relative error of the SVM, ANN and VBT models in the control stations is 10.50%, 12.71% and 12.91%, respectively. For SVM, ANN and VBT models, the averaged RMSE at the control stations is 1.87 (mm), 2.22 (mm) and 2.29 (mm), respectively. Analysis of the results of PWV estimated by the SVM, ANN and VBT, as well as the surface precipitation obtained from meteorological stations, indicate the high accuracy of the SVM in comparison with the ANN and VBT model. In the results shown in this paper, the SVM has the best ability to accurately estimate ZWD and PWV, using local GPS network observations. Numéro de notice : A2022-446 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.asr.2022.01.003 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1016/j.asr.2022.01.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100106 [article]

Spatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde / Mir Reza Ghaffari Razin in GPS solutions, vol 26 n° 1 (January 2022)  

Public [article]  inGPS solutions > vol 26 n° 1 (January 2022) . - n° 1 Titre : Spatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde Type de document : Article/Communication Auteurs : Mir Reza Ghaffari Razin, Auteur ; Samed Inyurt, Auteur Année de publication : 2022 Article en page(s) : n° 1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] Inférence floue [Termes IGN] modélisation spatio-temporelle [Termes IGN] positionnement ponctuel précis [Termes IGN] précipitation [Termes IGN] radiosondage [Termes IGN] retard troposphérique zénithal [Termes IGN] station GPS [Termes IGN] vapeur d'eau [Termes IGN] voxel Résumé : (auteur) Water vapor (WV) is one of the most important parameters in meteorological studies. Using an adaptive neuro-fuzzy inference system (ANFIS), a new method has been proposed for spatiotemporal modeling of precipitable WV (PWV). In a first step, the tropospheric zenith wet delay (ZWD) is calculated using the observations of 23 GPS stations in the northwest of Iran. Out of these 23 stations, 21 stations for training and 2 stations for testing and validating were selected. The observations are for 15 days, ranging from day of year (DOY) 300 to 314 in 2011. The reason for choosing this area and time interval is the availability of a complete set of data. Then, the values of ZWD are converted to PWV. The PWV values obtained from this step are considered as the output of the ANFIS. Also, the latitude and longitude values of the GPS stations, the DOY, observational time (min), temperature (T), pressure (P), and relative humidity (RH) are considered input to ANFIS. The ANFIS network is trained using the back-propagation algorithm. After the training step, the PWV values are evaluated at 2 test stations, KLBR and GGSH, and at Tabriz radiosonde station (38.08° N, 46.28°E). For a more accurate evaluation, all the results of the new method are compared with the voxel-based tomography model. The evaluation of the results is performed using the relative error, standard deviation, correlation coefficient, and root-mean-square error (RMSE). Also, precise point positioning (PPP) is used to better evaluate the proposed model at test stations. The value of the correlation coefficient at the radiosonde station for the ANFIS and voxel is 0.90 and 0.87, respectively. Also, the minimum RMSE calculated for the ANFIS and voxel are 1.02 and 1.06 mm, respectively. In the PPP analysis, an improvement of about 4 mm is observed in the coordinates of the test stations using ANFIS. The results confirm the capability and high accuracy of the proposed model in determining the temporal and spatial variations of PWV. Numéro de notice : A2022-003 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01184-1 Date de publication en ligne : 19/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01184-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98828 [article]

Intercomparisons of precipitable water vapour derived from radiosonde, GPS and sunphotometer observations / Shaoqi Gong in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)  

Public [article]  inGeodetski vestnik > vol 64 n° 4 (December 2020 - February 2021) . - pp 562 - 577 Titre : Intercomparisons of precipitable water vapour derived from radiosonde, GPS and sunphotometer observations Type de document : Article/Communication Auteurs : Shaoqi Gong, Auteur ; Wenqin Chen, Auteur ; Cunjie Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 562 - 577 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] analyse comparative [Termes IGN] atmosphère terrestre [Termes IGN] coefficient de corrélation [Termes IGN] photomètre [Termes IGN] photométrie [Termes IGN] positionnement par GNSS [Termes IGN] précipitation [Termes IGN] radiosondage [Termes IGN] station d'observation [Termes IGN] valeur aberrante [Termes IGN] vapeur d'eau Résumé : (Auteur) The atmospheric precipitable water vapour (PWV) plays a crucial role in the hydrological cycle and energy transfer on a global scale. Radiosonde (RS), sunphotometer (SP) and GPS (as well as broader GNSS) receivers have gradually been the principal instruments for ground-based PWV observation. This study first co-locates the observation stations configured the three instruments in the globe and in three typical latitudinal climatic regions respectively, then the PWV data from the three instruments are matched each other according to the observing times. After the outliers are removed from the matched data pairs, the PWV intercomparisons for any two instruments are performed. The results show that the PWV estimates from any two instruments have a good agreement with very high correlation coefficients. The latitude and climate have no significant influence on the PWV measurements from the three instruments, indicating that the instruments are very stable and depend on their performance. The PWV differences of any two instruments display the normal distribution, indicating non-systematic biases among the two PWV datasets. The relative differences between SP and GPS are the smallest, the middle between SP and RS, and those between GPS and RS are the largest. This study will be useful to promote GPS (GNSS) and SP PWV to be a substitute for RS PWV as a benchmark because of their high temporal resolutions. Numéro de notice : A2020-778 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2020.04.562-577 En ligne : http://www.geodetski-vestnik.com/en/2020-4 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96709 [article]

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