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Comprehensive study on the tropospheric wet delay and horizontal gradients during a severe weather event / Victoria Graffigna in Remote sensing, vol 14 n° 4 (February-2 2022)
[article]
Titre : Comprehensive study on the tropospheric wet delay and horizontal gradients during a severe weather event Type de document : Article/Communication Auteurs : Victoria Graffigna, Auteur ; Manuel Hernández-Pajares, Auteur ; Francisco Azpilicueta, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 888 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] données météorologiques
[Termes IGN] gradient de troposphère
[Termes IGN] phénomène climatique extrême
[Termes IGN] positionnement ponctuel précis
[Termes IGN] retard troposphérique zénithal
[Termes IGN] station GNSS
[Termes IGN] surveillance météorologique
[Termes IGN] tempête
[Termes IGN] Texas (Etats-Unis)
[Termes IGN] vapeur d'eauRésumé : (auteur) GNSS meteorology is today one of the most growing technologies to monitor severe weather events. In this paper, we present the usage of 160 GPS reference stations over the period of 14 days to monitor and track Hurricane Harvey, which struck Texas in August 2017. We estimate the Zenith Wet Delay (ZWD) and the tropospheric gradients with 30 s interval using TOMION v2 software and carry out the processing in Precise Point Positioning (PPP) mode. We study the relationship of these parameters with atmospheric variables extracted from Tropical Rainfall Measuring Mission (TRMM) satellite mission and climate reanalysis model ERA5. This research finds that the ZWD shows patterns related to the rainfall rate and to the location of the hurricane. We also find that the tropospheric gradients are correlated with water vapor gradients before and after the hurricane, and with the wind and the pressure gradients only after the hurricane. This study also shows a new finding regarding the spectral distribution of the gradients, with a clear diurnal period present, which is also found on the ZWD itself. This kind of study approaches the GNSS meteorology to the increasing requirements of meteorologist in terms of monitoring severe weather events. Numéro de notice : A2022-166 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs14040888 Date de publication en ligne : 12/02/2022 En ligne : https://doi.org/10.3390/rs14040888 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99791
in Remote sensing > vol 14 n° 4 (February-2 2022) . - n° 888[article]Conventional and neural network-based water vapor density model for GNSS troposphere tomography / Chen Liu in GPS solutions, vol 26 n° 1 (January 2022)
[article]
Titre : Conventional and neural network-based water vapor density model for GNSS troposphere tomography Type de document : Article/Communication Auteurs : Chen Liu, Auteur ; Yibin Yao, Auteur ; Chaoqian Xu, Auteur Année de publication : 2022 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] classification par réseau neuronal
[Termes IGN] erreur absolue
[Termes IGN] étalonnage de modèle
[Termes IGN] modèle météorologique
[Termes IGN] propagation troposphérique
[Termes IGN] tomographie par GPS
[Termes IGN] vapeur d'eau
[Termes IGN] voxelRésumé : (auteur) Global navigation satellite system (GNSS) water vapor (WV) tomography is a promising technique to reconstruct the three-dimensional (3D) WV field. However, this technique usually suffers from the ill-posed problem caused by the poor geometry of GNSS rays, resulting in underdetermined tomographic equations. Such equations often rely on iterative methods for solving, but conventional iterative approaches require accurate initial WV density. To address this demand, we proposed two models for WV density estimation. One is the conventional model (CO model) that consists of an exponential model and a linear least-squares model, which are used to describe the spatial and temporal variability of the WV density, respectively. The other is a neural network model (NN model) that uses a backpropagation neural network (BPNN) to fit the nonlinear variation of WV density in both spatial and temporal domains. WV density derived from a Hong Kong (HK) radiosonde station (RS) during 2020 was used to validate the proposed models. Validation results show that both models well describe the spatial and temporal distribution of the WV density. The NN model exhibits better prediction performance than the CO model in terms of root mean square error (RMSE) and bias. We also applied the proposed models to GNSS WV tomography to test their performance in extreme weather conditions. Test results show that the proposed model-based GNSS tomography can correct the content of WV density but cannot accurately sense its irregular distribution. Numéro de notice : A2022-005 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01188-x Date de publication en ligne : 23/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01188-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98920
in GPS solutions > vol 26 n° 1 (January 2022) . - n° 4[article]Hourly rainfall forecast model using supervised learning algorithm / Qingzhi Zhao in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)
[article]
Titre : Hourly rainfall forecast model using supervised learning algorithm Type de document : Article/Communication Auteurs : Qingzhi Zhao, Auteur ; Yang Liu, Auteur ; Wanqiang Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4100509 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] autocorrélation
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données GNSS
[Termes IGN] heure
[Termes IGN] modèle de simulation
[Termes IGN] modèle météorologique
[Termes IGN] précipitation
[Termes IGN] série temporelle
[Termes IGN] station GNSS
[Termes IGN] Taïwan
[Termes IGN] vapeur d'eauRésumé : (auteur) Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is proposed in this study to predict rainfall with high accuracy and time resolution. Hourly PWV derived from Global Navigation Satellite System (GNSS) and temperature data are used as input parameters of the HRF model, and a support vector machine is introduced to train the proposed model. In addition, this model also considers the time autocorrelation of rainfall in the previous epoch. Hourly PWV data of 21 GNSS stations and collocated meteorological parameters (temperature and rainfall) for five years in Taiwan Province are selected to validate the proposed model. Internal and external validation experiments have been performed under the cases of slight, moderate, and heavy rainfall. Average root-mean-square error (RMSE) and relative RMSE of the proposed HRF model are 1.36/1.39 mm/h and 1.00/0.67, respectively. In addition, the proposed HRF model is compared with the similar works in previous studies. Compared results reveal the satisfactory performance and superiority of the proposed HRF model in terms of time resolution and forecast accuracy. Numéro de notice : A2022-024 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3054582 Date de publication en ligne : 09/02/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3054582 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99253
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 1 (January 2022) . - n° 4100509[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)
[article]
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] voxelRésumé : (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
in Advances in space research > vol 69 n° 7 (April 2022) . - pp 2671 - 2681[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)
[article]
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] voxelRésumé : (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
in GPS solutions > vol 26 n° 1 (January 2022) . - n° 1[article]Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications / Min Guo in Advances in space research, vol 67 n° 12 (15 June 2021)PermalinkRefining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data / Jia He in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)PermalinkCloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques / Miao Tian in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkPrecipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)PermalinkVariations of precipitable water vapor using GNSS CORS in Thailand / Chokchai Trakolkul in Survey review, vol 53 n°376 (January 2021)PermalinkIntercomparisons of precipitable water vapour derived from radiosonde, GPS and sunphotometer observations / Shaoqi Gong in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)PermalinkGeostatistical analysis and mitigation of the atmospheric phase screens in Ku-band terrestrial radar interferometric observations of an alpine glacier / Simone Baffelli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkEstimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study / Mir Reza Ghaffari Razin in GPS solutions, Vol 24 n° 3 (July 2020)PermalinkMapping precipitable water vapor time series from Sentinel-1 interferometric SAR / Pedro Mateus in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkAdvanced GNSS tropospheric products for monitoring severe weather events and climate / Jonathan Jones (2020)PermalinkÉtude de la vapeur d’eau atmosphérique à partir de données GNSS dans le bassin sud-ouest de l’océan Indien et applications à l’étude du climat et des cyclones tropicaux / Edouard Lees (2020)PermalinkImproved algorithms for the measurement of total precipitable water and cloud liquid water from SARAL microwave radiometer observations / Rajput Neha Mangalsinh in Marine geodesy, vol 42 n° 4 (July 2019)PermalinkA new global grid model for the determination of atmospheric weighted mean temperature in GPS precipitable water vapor / Liangke Huang in Journal of geodesy, vol 93 n° 2 (February 2019)PermalinkStudy and mitigation of calibration factor instabilities in a water vapor Raman lidar / Leslie David in Atmospheric measurement techniques, vol 10 n° 7 (July 2017)PermalinkSentinel-1 interferometric SAR mapping of precipitable water vapor over a country-spanning area / Pedro Mateus in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkIntegrating uncertainty propagation in GNSS radio occultation retrieval: From bending angle to dry-air atmospheric profiles / Jakob Schwarz in Earth and space science, vol 4 n° 4 (April 2017)PermalinkMonitoring and prediction of precipitable water vapor using GPS data in Turkey / Kutubuddin Ansari in Journal of applied geodesy, vol 10 n° 4 (December 2016)PermalinkA seamless weather–climate multi‐model intercomparison on the representation of a high impact weather event in the western Mediterranean: HyMeX IOP12 / Samiro Khodayar in Quarterly Journal of the Royal Meteorological Society, vol 142 n° S1 (August 2016)PermalinkMulti-GNSS meteorology : real-time retrieving of atmospheric water vapor from BeiDou, Galileo, GLONASS, and GPS observations / Xingxing Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)PermalinkReal-time retrieval of precipitable water vapor from GPS and BeiDou observations / Cuixian Lu in Journal of geodesy, vol 89 n° 9 (september 2015)PermalinkReal-time GPS precise point positioning-based precipitable water vapor estimation for rainfall monitoring and forecasting / Junbo Shi in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)PermalinkPredicting floods with GPS / Paul Grad in Position, n° 76 (April - May 2015)PermalinkVoxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model / Biyan Chen in Journal of geodesy, vol 88 n° 7 (July 2014)PermalinkA high-quality, homogenized, global, long-term (1993–2008) DORIS precipitable water data set for climate monitoring and model verification / Olivier Bock in Journal of geophysical research : Atmospheres, vol 119 n° 12 (2014)PermalinkDynamic modeling of GNSS troposphere wet delay for estimation of precipitable water vapour / Ahmed El-Mowafy in Journal of applied geodesy, vol 8 n° 1 (April 2014)PermalinkHyperspectral-based adaptive matched filter detector error as a function of atmospheric water vapor estimation / Allan W. Yarbrough in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)PermalinkImproved one/multi-parameter models that consider seasonal and geographic variations for estimating weighted mean temperature in ground-based GPS meteorology / Yi Bin Yao in Journal of geodesy, vol 88 n° 3 (March 2014)PermalinkWater vapor probabilistic retrieval using GNSS signals / Andrea Antonini in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)PermalinkMonitoring precipitable water vapor in real-time using global navigation satellite systems / Seung-Woo Lee in Journal of geodesy, vol 87 n° 10-12 (October - December 2013)PermalinkGlobal empirical model for mapping zenith wet delays onto precipitable water / Yi Bin Yao in Journal of geodesy, vol 87 n° 5 (May 2013)PermalinkAdvanced global navigation satellite systems tropospheric products for monitoring severe weather events and climate (GNSS4SWEC) / Guergana Guerova (2013)PermalinkPermalinkInterpolating atmospheric water vapor delay by incorporating terrain elevation information / B. Xu in Journal of geodesy, vol 85 n° 9 (September 2011)PermalinkThree-dimensional humidity retrieval using a network of compact microwave radiometers to correct for variations in wet tropospheric path delay in spaceborne interferometric SAR imagery / S. Sahoo in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)Permalink4D GPS water vapor tomography: new parameterized approaches / Donat Perler in Journal of geodesy, vol 85 n° 8 (August 2011)PermalinkL'apport des données GPS AMMA pour l'étude du cycle de l'eau de la mousson africaine / Olivier Bock in Bulletin d'information scientifique et technique de l'IGN, n° 77 (avril 2011)PermalinkThe large‐scale water cycle of the West African monsoon / Olivier Bock in Atmospheric Science Letters, vol 12 n° 1 (January - March 2011)PermalinkThe water vapour intercomparison effort in the framework of the Convective and Orographically-induced Precipitation Study: airborne-to-ground-based and airborne-to-airborne lidar systems / Rohini Bhawar in Quarterly Journal of the Royal Meteorological Society, vol 137 n° S1 (January 2011)PermalinkA case study of using Raman lidar measurements in high-accuracy GPS applications / Pierre Bosser in Journal of geodesy, vol 84 n° 4 (April 2010)PermalinkPermalinkWater vapour intercomparison effort in the frame of the Convective and Orographically‐ induced Precipitation Study / Rohini Bhawar (2009)PermalinkWest African Monsoon observed with ground-based GPS receivers during African Monsoon Multidisciplinary Analysis (AMMA) / Olivier Bock in Journal of geophysical research : Atmospheres, vol 113 n° D21 (16 November 2008)PermalinkAssessment of water budgets computed from NWP models and observational datasets during AMMA-EOP / Olivier Bock (2008)PermalinkProcessing of Raman lidar measurements for water vapor mixing ratio retrieval / Pierre Bosser (2008)PermalinkWater vapour intercomparison effort in the frame of the convective and orographically-induced precipitation study / Rohini Bhawar (2008)PermalinkComparison of ground-based GPS precipitable water vapour to independent observations and NWP model reanalyses over Africa / Olivier Bock in Quarterly Journal of the Royal Meteorological Society, vol 133 n° 629 (Octobre 2007 part B)PermalinkAssessment of GPS data for meteorological applications over Africa: Study of error sources and analysis of positioning accuracy / Andrea Walpersdorf in Journal of Atmospheric and Solar-Terrestrial Physics, vol 69 n° 12 (August 2007)PermalinkMultiscale analysis of precipitable water vapor over Africa from GPS data and ECMWF analyses / Olivier Bock in Geophysical research letters, vol 34 n° 9 (16 May 2007)PermalinkDiurnal Cycle of Water Vapor as Documented by a Dense GPS Network in a Coastal Area during ESCOMPTE IOP2 / Sophie Bastin in Journal of Applied Meteorology and Climatology, vol 46 n° 2 (February 2007)PermalinkMapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS / D.A. Fuentes in Remote sensing of environment, vol 103 n° 3 (15 August 2006)Permalink