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Cloud 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)
[article]
Titre : Cloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques Type de document : Article/Communication Auteurs : Miao Tian, Auteur ; Hao Chen, Auteur ; Guanghui Liu, Auteur Année de publication : 2021 Article en page(s) : pp 2781 - 2793 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection des nuages
[Termes IGN] dioxyde de carbone
[Termes IGN] image infrarouge
[Termes IGN] modèle atmosphérique
[Termes IGN] modèle de transfert radiatif
[Termes IGN] régression linéaire
[Termes IGN] vapeur d'eauRésumé : (auteur) Accurate cloud detection using infrared (IR) data is very challenging due to the limitations and uncertainties from many aspects in the satellite IR remote sensing. This article proposes an end-to-end cloud detection method for the Cross-track IR Sounder (CrIS) using machine learning (ML) techniques. The brightness temperatures from paired CrIS channels in the longwave and midwave water vapor bands and the longwave and shortwave CO 2 bands are used. After obtaining the linear regression coefficients for each of the selected channel pairs, a complete set of CrIS full spectral resolution (FSR) cloud detection index (FCDI) is derived from the temperature difference between the regression and observation for each channel pair. It is shown that FCDI captures cloud location and structure well by comparing with the cloud products (CPs) from the Visible IR Imaging Radiometer Suite (VIIRS). After collocating FCDI with VIIRS CP, ML techniques such as the extreme learning machine, support vector machine, and multilayer perceptron are used to train the collocated FCDIs for cloud detection. Simulation results show that the accuracy of FCDI cloud detection is slightly above 80%. Moreover, the results encourage the use of water vapor bands in FCDI, in addition to CO 2 bands. Numéro de notice : A2021-281 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3020120 Date de publication en ligne : 18/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3020120 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97387
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 2781 - 2793[article]Precipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)
[article]
Titre : Precipitable water vapor fusion based on a generalized regression neural network Type de document : Article/Communication Auteurs : Bao Zhang, Auteur ; Yibing Yao, Auteur Année de publication : 2021 Article en page(s) : n° 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] Amérique du nord
[Termes IGN] coefficient d'étalonnage
[Termes IGN] coefficient de corrélation
[Termes IGN] données GNSS
[Termes IGN] données météorologiques
[Termes IGN] erreur systématique
[Termes IGN] fusion de données
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] précipitation
[Termes IGN] prévision météorologique
[Termes IGN] régression
[Termes IGN] réseau neuronal artificiel
[Termes IGN] vapeur d'eau
[Termes IGN] variation temporelleRésumé : (auteur) Water vapor plays an important role in Earth’s weather and climate processes and energy transfer. Plenty of techniques have developed to monitor precipitable water vapor (PWV), but joint use of different techniques has some problems, including systematic biases, different spatiotemporal coverages and resolutions among different datasets. To address the above problems and improve the data utilization, we propose to use a generalized regression neural network (GRNN) to fuse PWVs from Global Navigation Satellite System (GNSS), Moderate-Resolution Imaging Spectroradiometer (MODIS), and European Centre for Medium‐Range Weather Forecasts Reanalysis 5 (ERA5). The core idea of this method is to use the high-quality GNSS PWV to calibrate and optimize the relatively low-quality MODIS and ERA5 PWV through the constructed GRNNs. Using the proposed method, we generated more than 400 PWV maps that combine GNSS, MODIS, and ERA5 PWVs in North America in 2018. Results show that the overall bias, standard deviation (STD), and root-mean-square (RMS) error are 0.0 mm, 2.1 mm, and 2.2 mm for the improved MODIS PWV, and 0.0 mm, 1.6 mm, and 1.6 mm for the improved ERA5 PWV. Compared to the original MODIS and ERA5 PWV, the total improvements are 37.1% and 15.8% in terms of RMS. The RMS improvements are mainly contributed from the calibration of bias for the MODIS PWV and optimization for the ERA5 PWV. It also demonstrates that the original MODIS PWV tends to be greater than the GNSS PWV while the ERA5 PWV has very small biases. After calibration and optimization, the correlation coefficients between the modified PWV and the GNSS PWV are 0.96 for the MODIS PWV and 0.98 for the ERA5 PWV. The proposed method also diminishes the temporal and spatial variations in accuracy, generating homogeneous PWV products. Since the biases among the three datasets are well removed and data accuracies are improved to the same level, they are thus easily fused and jointly used. Numéro de notice : A2021-259 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01482-z Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1007/s00190-021-01482-z Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97299
in Journal of geodesy > vol 95 n° 4 (April 2021) . - n° 36[article]Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde / Li Wang in Space weather, vol 19 n° 3 (March 2021)
[article]
Titre : Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde Type de document : Article/Communication Auteurs : Li Wang, Auteur ; Zhao Dongsheng ; Changyong He , Auteur ; et al., Auteur Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : n° e2020SW002605 Note générale : bibliographie
The authors greatly appreciate the financial support from the National Natural Science Foundations of China (Grant No. 41730109, 41804013), the Natural Science Foundation of Jiangsu Province (Grant No. BK20200646, BK20200664), the Fundamental Re-search Funds for the Central Universi-ties (Grant No. 2020QN31, 2020QN30), the Project funded by China Postdoc-toral Science Foundation (Grant No. 2020M671645), the Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution (Grant No. KLSPWSEP-A06), A Project Funded by the Priority Academic Pro-gram Development of Jiangsu Higher Education Institutions (Surveying and Mapping).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] image Formosat/COSMIC
[Termes IGN] modèle ionosphérique
[Termes IGN] Perceptron multicouche
[Termes IGN] réseau neuronal artificiel
[Termes IGN] teneur totale en électrons
[Termes IGN] variation saisonnièreRésumé : (auteur) The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal-vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space-borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY-3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three-dimensional electron density model based on an artificial neural network, namely ANN-TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root-mean-square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN-TDD is 30%–60% higher than the IRI-2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN-TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI-2016 with the STORM option activated. Additionally, the ANN-TDD successfully reproduces the large-scale horizontal-vertical ionospheric electrodynamic features, including seasonal variation and hemispheric asymmetries. These features agree well with the structure revealed by the RO profiles derived from the FORMOSAT/COSMIC-2 mission. Furthermore, the ANN-TDD successfully captures the prominent regional ionospheric patterns, including the equatorial ionization anomaly, Weddell Sea anomaly and mid-latitude summer nighttime anomaly. The new model is expected to play an important role in the application of GNSS navigation and in the explanation of the physical mechanisms involved. Numéro de notice : A2021-504 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2020SW002605 Date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.1029/2020SW002605 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99369
in Space weather > vol 19 n° 3 (March 2021) . - n° e2020SW002605[article]Integrity investigation of global ionospheric TEC maps for high-precision positioning / Jiaojiao Zhao in Journal of geodesy, vol 95 n° 3 (March 2021)
[article]
Titre : Integrity investigation of global ionospheric TEC maps for high-precision positioning Type de document : Article/Communication Auteurs : Jiaojiao Zhao, Auteur ; Manuel Hernández-Pajares, Auteur ; Ningbo Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] carte ionosphérique mondiale
[Termes IGN] erreur moyenne quadratique
[Termes IGN] International GNSS Service
[Termes IGN] modèle ionosphérique
[Termes IGN] modèle stochastique
[Termes IGN] positionnement ponctuel précis
[Termes IGN] tempête magnétique
[Termes IGN] teneur totale en électronsRésumé : (auteur) Aside from the ionospheric total electron content (TEC) information, root-mean-square (RMS) maps are also provided as the standard deviations of the corresponding TEC errors in global ionospheric maps (GIMs). As the RMS maps are commonly used as the accuracy indicator of GIMs to optimize the stochastic model of precise point positioning algorithms, it is of crucial importance to investigate the reliability of RMS maps involved in GIMs of different Ionospheric Associated Analysis Centers (IAACs) of the International GNSS Service (IGS), i.e., the integrity of GIMs. We indirectly analyzed the reliability of RMS maps by comparing the actual error of the differential STEC (dSTEC) with the RMS of the dSTEC derived from the RMS maps. With this method, the integrity of seven rapid IGS GIMs (UQRG, CORG, JPRG, WHRG, EHRG, EMRG, and IGRG) and six final GIMs (UPCG, CODG, JPLG, WHUG, ESAG and IGSG) was examined under the maximum and minimum solar activity conditions as well as the geomagnetic storm period. The results reveal that the reliability of the RMS maps is significantly different for the GIMs from different IAACs. Among these GIMs, the values in the RMS maps of UQRG are large, which can be used as ionospheric protection level, while the RMS values in EHRG and ESAG are significantly lower than the realistic RMS. The rapid and final GIMs from CODE, JPL and WHU provide quite reasonable RMS maps. The bounding performance of RMS maps can be influenced by the location of the stations, while the influence of solar activity and the geomagnetic storm is not obvious. Numéro de notice : A2021-220 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01487-8 Date de publication en ligne : 22/02/2021 En ligne : https://doi.org/10.1007/s00190-021-01487-8 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97188
in Journal of geodesy > vol 95 n° 3 (March 2021) . - n° 35[article]Modélisation des délais ionosphériques appliquée au traitement PPP-RTK centimétrique avec ambiguïtés entières de phase / Camille Parra in XYZ, n° 166 (mars 2021)
[article]
Titre : Modélisation des délais ionosphériques appliquée au traitement PPP-RTK centimétrique avec ambiguïtés entières de phase Type de document : Article/Communication Auteurs : Camille Parra, Auteur Année de publication : 2021 Article en page(s) : pp 43 - 49 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] délai d'obtention de la première position
[Termes IGN] erreur systématique
[Termes IGN] modèle ionosphérique
[Termes IGN] phase GNSS
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement ponctuel précis
[Termes IGN] précision centimétrique
[Termes IGN] résolution d'ambiguïté
[Termes IGN] temps de convergenceRésumé : (Auteur) Geoflex est une entreprise fournissant un positionnement précis, fiable, continu et en temps réel à ses clients partout dans le monde. Ce positionnement GNSS (Global Navigation Satellite System) se base sur l’utilisation de la Technologie PPP Geoflex/CNES (Precise Point Positioning) développée en partenariat avec le CNES (Centre national d’études spatiales) et commercialisée par Geoflex. Avec cette solution, qui repose sur la résolution d’ambiguïtés entières de phase en zéro-différence, Geoflex diffuse des flux de corrections permettant à l’utilisateur de se positionner partout dans le monde et sans aucune infrastructure GNSS proche de l’utilisateur, en mode statique ou cinématique, en temps réel ou différé, avec une précision horizontale de 4 cm à 95 % du temps. L’inconvénient de cette technique est son temps de convergence relativement important, d’environ 30 min, avec des observations bi-fréquences et bi-constellations disponibles sur les récepteurs GNSS mass-market commençant à équiper les voitures pour des meilleurs systèmes d’aide à la conduite, voire de certains smartphones. Le but de cet article est de montrer l’impact que peut avoir un modèle ionosphérique sur le temps de convergence d’un calcul PPP grâce à la technique du PPP-RTK (Real Time Kinematic). Il sera montré que grâce à cet apport, il est possible de réduire le temps de convergence de 90 % par rapport à une solution PPP-IAR (Integer Ambiguity Resolution) classique, mais qu’une attention particulière doit être apportée aux biais électroniques. Numéro de notice : A2021-247 Affiliation des auteurs : ENSG (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97325
in XYZ > n° 166 (mars 2021) . - pp 43 - 49[article]Réservation
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