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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]Preparation of the VENµS satellite data over Israel for the input into the GRASP data treatment algorithm / Maeve Blarel (2022)
Titre : Preparation of the VENµS satellite data over Israel for the input into the GRASP data treatment algorithm Type de document : Mémoire Auteurs : Maeve Blarel, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 73 p. Format : 21 x 30 cm Note générale : Bibliographie
Rapport de projet pluridisciplinaire, cycle ING2Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aérosol
[Termes IGN] conversion de données
[Termes IGN] correction atmosphérique
[Termes IGN] correction géométrique
[Termes IGN] correction radiométrique
[Termes IGN] image hyperspectrale
[Termes IGN] image Venµs-VSSC
[Termes IGN] Israël
[Termes IGN] microsatellite
[Termes IGN] Python (langage de programmation)
[Termes IGN] série temporelle
[Termes IGN] traitement de données localiséesIndex. décimale : PROJET Mémoires : Rapports de projet - stage des ingénieurs de 2e année Résumé : (Auteur) Réalisé au sein du laboratoire de télédétection de l’Institut Jacob Blaustein pour la recherche sur le désert (BIDR) de l’Université Ben-Gourion du Négev, en Israël, et financé par une mission du CNRS, ce stage a pour objectif l’adaptation d’un driver dédié à la conversion des données satellites VENµS et à leur préparation pour le traitement par l’algorithme GRASP. VENµS (Vegetation and Environment monitoring New Micro-Satellite) est un microsatellite, fruit d’une collaboration franco-israélienne pour l’observation de la Terre à l’aide d’une caméra super spectrale. Les visées de la mission scientifique sont déterminées par le CESBIO et le CNES, en France, et l’Université Ben-Gourion du Néguev, en Israël. Son objectif est de fournir des observations à haute résolution spatiale pour la recherche scientifique portant sur la surveillance, l’analyse et la caractérisation du fonctionnement de la surface terrestre, sous les effets de facteurs environnementaux et des activités humaines. Plus particulièrement, ces données sont dédiées à des applications dans l’agriculture de précision, l’urbanisation et la surveillance des masses d’eau. Les images acquises au-dessus d’Israël ont un format différent de celles prises à travers le monde pour une gestion distincte des données. Aujourd’hui, les recherches israélienne et française souhaitent une caractérisation des aérosols atmosphériques sur Israël et un traitement des données par GRASP. La problématique rencontrée est la conversion des données sur Israël pour leur entrée dans cet algorithme. Après une phase de découverte et de compréhension des données satellites VENµS et celles requises à l’entrée de GRASP, le travail de ce présent stage consiste à développer une solution d’adaptation du programme informatique pour la conversion des données VENµS sur Israël. Des perspectives existent pour ce projet. Pour observer la Terre, on souhaite des données de plus en plus précises par des améliorations de l’acquisition et du traitement des images. Concernant l’acquisition de données, les intervalles de temps de revisite limitent actuellement l’avantage multi-pixel. D’un autre côté, l’un des objectifs de cette mission satellitaire est le développement des algorithmes pour exploiter des séries temporelles de données, incluant les corrections géométriques et radiométriques. Pour GRASP, la gestion du masque des nuages doit être perfectionnée et concernant le driver adapté, les observations directionnelles demandent une exploitation plus grande. L’ensemble des codes Python, fonctionnels et commentés, implémenté au cours du stage est confidentiel et reste à la propriété de GRASP. Par conséquent, aucun script provenant du code source ne sera présenté au cours de ce rapport. Note de contenu : Introduction
1. Internship presentation
1.1 Context
1.2 Issues and Objectives
1.3 State of current research
2. Technical study
2.1 Driver architecture
2.2 Language, libraries and software in use
2.3 The data
3. Achievement
3.1 Implementation
3.2 Progress of internship
3.3 Difficulties encountered and Solutions adopted
ConclusionNuméro de notice : 26872 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire de projet pluridisciplinaire Organisme de stage : Laboratoire de télédétection de l’Institut Jacob Blaustein (Université Ben-Gourion du Négev) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101702 Documents numériques
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Preparation of the VENµS satellite data over Israel for the input into the GRASP data treatment algorithm - pdf auteurAdobe Acrobat PDF 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)PermalinkLa campagne Caddiwa dans la région des îles du Cap-Vert / Cyrille Flamant in La Météorologie, n° 115 (2021)PermalinkExploration 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 - 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May 2015)PermalinkSeeing through shadow: Modelling surface irradiance for topographic correction of Landsat ETM+ data / Tobias Schulmann in ISPRS Journal of photogrammetry and remote sensing, vol 99 (January 2015)PermalinkPlume tracking with a mobile sensor based on incomplete and imprecise information / Juliane Brink in Transactions in GIS, vol 18 n° 5 (October 2014)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)PermalinkAn improved dark object method to retrieve 500 m-resolution AOT (Aerosol Optical Thickness) image from MODIS data: A case study in the Pearl River Delta area, China / Lili Li in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 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)PermalinkStatistical data fusion of multi-sensor AOD over the Continental United States / Sweta Jinnagara Puttaswamy in Geocarto international, vol 29 n° 1 - 2 (February - April 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)PermalinkChiffres clés du climat France Monde, édition 2014 / CGDD Commissariat Général au Développement Durable (2013)PermalinkChiffres clés de l'environnement édition 2013 / CGDD Commissariat Général au Développement Durable (2013)PermalinkA new technique using infrared satellite measurements to improve the accuracy of the CALIPSO cloud-aerosol discrimination method / A. Naeger in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 2 (January 2013)PermalinkThe soil moisture and ocean salinity (SMOS) mission: first results and achievements / Yann H. Kerr in Revue Française de Photogrammétrie et de Télédétection, n° 200 (Novembre 2012)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)PermalinkQualitative change detection using sensor networks based on connectivity information / J. Jiang in Geoinformatica, vol 15 n° 2 (April 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)PermalinkPM10 remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region / E. Emili in Remote sensing of environment, vol 114 n° 11 (15/11/2010)PermalinkAtmospheric correction to IRS-P6 AWiFS data and its validation with ground measurements: a study over the semi-arid region / Ashu Sharma in Geocarto international, vol 25 n° 7 (November 2010)Permalinkvol 31 n° 17 - 18 - September 2010 - Pan ocean remote sensing : oceanic manifestation of global changes (Bulletin de International Journal of Remote Sensing IJRS) / G. LevyPermalinkSpectroscopic calibration correlation of field and lab-sized fluorescence LIDAR systems / B. Déry in IEEE Transactions on geoscience and remote sensing, vol 48 n° 9 (September 2010)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)PermalinkApport des mesures directionnelles et polarisées aux corrections atmosphériques au-dessus des océans ouverts. Application à la mission PARASOL / Tristan Harmel (2009)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)Permalinkvol 29 n° 21 - October 2008 - Satellite observations of the atmosphere, oceans and their interface in relation to climate, natural hazards and management of coastal zone (Bulletin de International Journal of Remote Sensing IJRS) / G. 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