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Precipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)  

Public [article]  inJournal of geodesy > vol 95 n° 4 (April 2021) . - n° 36 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 temporelle Résumé : (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 [article]

Copula-based modeling of dependence structure in geodesy and GNSS applications: case study for zenith tropospheric delay in complex terrain / Roya Mousavian in GPS solutions, vol 25 n° 1 (January 2021)  

Public [article]  inGPS solutions > vol 25 n° 1 (January 2021) . - n° 12 Titre : Copula-based modeling of dependence structure in geodesy and GNSS applications: case study for zenith tropospheric delay in complex terrain Type de document : Article/Communication Auteurs : Roya Mousavian, Auteur ; Christof Lorenz, Auteur ; Masoud Mashhadi Hossainali, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] corrélation croisée normalisée [Termes IGN] dissymétrie [Termes IGN] données GNSS [Termes IGN] Europe centrale [Termes IGN] modèle atmosphérique [Termes IGN] prévision météorologique [Termes IGN] retard troposphérique zénithal [Termes IGN] série temporelle Résumé : (auteur) Modeling and understanding the statistical relationships between geophysical quantities is a crucial prerequisite for many geodetic applications. While these relationships can depend on multiple variables and their interactions, commonly used scalar methods like the (cross) correlation are only able to describe linear dependencies. However, particularly in regions with complex terrain, the statistical relationships between variables can be highly nonlinear and spatially heterogeneous. Therefore, we introduce Copula-based approaches for modeling and analyzing the full dependence structure. We give an introduction to Copula theory, including five of the most widely used models, namely the Frank, Clayton, Ali-Mikhail-Haq, Gumbel and Gaussian Copula, and use this approach for analyzing zenith tropospheric delays (ZTDs). We apply modeled ZTDs from the Weather and Research Forecasting (WRF) model and estimated ZTDs through the processing of Global Navigation Satellite System (GNSS) data and evaluate the pixel-wise dependence structures of ZTDs over a study area with complex terrain in Central Europe. The results show asymmetry and nonlinearity in the statistical relationships, which justifies the application of Copula-based approaches compared to, e.g., scalar measures. We apply a Copula-based correction for generating GNSS-like ZTDs from purely WRF-derived estimates. Particularly the corrected time series in the alpine regions show improved Nash–Sutcliffe efficiency values when compared against GNSS-based ZTDs. The proposed approach is therefore highly suitable for analyzing statistical relationships and correcting model-based quantities, especially in complex terrain, and when the statistical relationships of the analyzed variables are unknown. Numéro de notice : A2021-007 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01044-4 date de publication en ligne : 02/11/2020 En ligne : https://doi.org/10.1007/s10291-020-01044-4 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96297 [article]

Impact of INSAT-3D/3DR radiance data assimilation in predicting tropical cyclone Titli over the bay of Bengal / Raghu Nadimpalli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 6945 - 6957 Titre : Impact of INSAT-3D/3DR radiance data assimilation in predicting tropical cyclone Titli over the bay of Bengal Type de document : Article/Communication Auteurs : Raghu Nadimpalli, Auteur ; Akhil Srivastava, Auteur ; V. S. Prasad, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6945 - 6957 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Bengale, golfe du [Termes IGN] cyclone [Termes IGN] image INSAT-VHRR [Termes IGN] interpolation [Termes IGN] matrice de covariance [Termes IGN] modèle de transfert radiatif [Termes IGN] précipitation [Termes IGN] prévision météorologique [Termes IGN] radiance [Termes IGN] zone intertropicale Résumé : (auteur) This is the first study concerning the assimilation of the INSAT-3D/3DR radiance in the Hurricane Weather Research and Forecasting (HWRF) model and assesses its credibility to improve track, intensity, and precipitation forecasts of tropical cyclone (TC) Titli that occurred over the Bay of Bengal (BoB), which showed rapid intensification (RI) and weakening through its lifetime. The inbuilt Gridpoint Statistical Interpolation (GSI) method is used with a 3-D variational (3DVAR) configuration. Three sets of numerical experiments such as control (CNTL) (no assimilation), Global Telecommunication System (GTS) (observations from GTS network), and INSAT-3D/3DR (INSAT-3D/3DR sounder radiance data and GTS observations) were carried out with seven different initializations. The radiance analysis reproduced the initial vortex and the prominent synoptic scale features associated with TC Titli. The average root-mean-square errors (RMSE) of the analysis were relatively lower in the INSAT-3D/3DR compared to the CNTL and GTS. The HWRF performance is enhanced for track simulation, with improvements in mean landfall position errors by 40%–70% and 26%–52% for the INSAT-3D/3DR and GTS runs, respectively. The assimilation of radiance data has a positive impact on the simulation of warm core and thermodynamic structures, which has led to a more accurate intensity prediction (by 30–47%) over the CNTL. The assimilation run could realistically simulate the RI and weakening phases of the TC. A cold dry air intrusion is also observed when associated with the weakening. The study highlights the need to incorporate INSAT-3D/3DR radiances for improved TC predictions over the BoB basin. Numéro de notice : A2020-587 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2978211 date de publication en ligne : 25/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2978211 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95915 [article]

Homogenizing GPS integrated water vapor time series: Benchmarking break detection methods on synthetic data sets / Roeland Van Malderen in Earth and space science, vol 7 n° 5 (May 2020)  

Public [article]  inEarth and space science > vol 7 n° 5 (May 2020) . - n° e2020EA001121 Titre : Homogenizing GPS integrated water vapor time series: Benchmarking break detection methods on synthetic data sets Type de document : Article/Communication Auteurs : Roeland Van Malderen, Auteur ; Eric Pottiaux, Auteur ; Anna Klos, Auteur ; P. Domonkos, Auteur ; Michal Elias, Auteur ; Tong Ning, Auteur ; Olivier Bock , Auteur ; J. Guijarro, Auteur ; F. Alshawaf, Auteur ; M. Hoseini, Auteur ; Annarosa Quarello , Auteur ; et al., Auteur Année de publication : 2020 Projets : GNSS4SWEC / Article en page(s) : n° e2020EA001121 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] algorithme d'homogénéisation [Termes IGN] coordonnées GNSS [Termes IGN] données hétérogènes [Termes IGN] jeu de données [Termes IGN] prévision météorologique [Termes IGN] série temporelle [Termes IGN] station permanente [Termes IGN] teneur intégrée en vapeur d'eau Résumé : (auteur) We assess the performance of different break detection methods on three sets of benchmark data sets, each consisting of 120 daily time series of integrated water vapor differences. These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA‐Interim) integrated water vapor output, which serves as the reference series here. The benchmark includes homogeneous and inhomogeneous sections with added nonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced, with increasing complexity, by adding autoregressive noise of the first order to the white noise model and the periodic behavior and consecutively by adding gaps and allowing nonclimatic trends. The purpose of this “complex experiment” is to examine the performance of break detection methods in a more realistic case when the reference series are not homogeneous. We evaluate the performance of break detection methods with skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends of the homogeneous series. We found that most methods underestimate the number of breaks and have a significant number of false detections. Despite this, the degree of CRMSE reduction is significant (roughly between 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is even exceeding the 90% of the raw data error. For the complex experiment, the improvement ranges between 15% and 35% with respect to the raw data, both in terms of RMSE and trend estimations. Numéro de notice : A2020-335 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2020EA001121 date de publication en ligne : 20/04/2020 En ligne : https://doi.org/10.1029/2020EA001121 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96837 [article]

Techniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network / Adil Alim in Geoinformatica [en ligne], vol 24 n° 2 (April 2020)  

Public [article]  inGeoinformatica [en ligne] > vol 24 n° 2 (April 2020) . - pp 269 – 299 Titre : Techniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network Type de document : Article/Communication Auteurs : Adil Alim, Auteur ; Aparna Joshi, Auteur ; Feng Chen, Auteur ; Catherine T. Lawson, Auteur Année de publication : 2020 Article en page(s) : pp 269 – 299 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] corrélation [Termes IGN] détection d'événement [Termes IGN] détection de changement [Termes IGN] données spatiotemporelles [Termes IGN] entretien du réseau [Termes IGN] hiver [Termes IGN] météorologie [Termes IGN] prévision météorologique [Termes IGN] réseau routier [Termes IGN] sécurité routière [Termes IGN] trafic routier Résumé : (auteur) Adverse weather conditions have a significant impact on the safety, mobility, and efficiency of highway networks. Weather contributed to 23 percent of all non-reoccurring delay and approximately 544 million vehicle hours of delay each year (2014). Nearly 2.3 billion dollars each year are spent by transportation agencies for winter maintenance that contribute to close to 20 percent of most DOT’s yearly budgets (2014). These safety and mobility factors make it important to develop new and more effective methods to address road conditions during adverse weather conditions. Given weather and traffic sensors installed along side of the highway networks, how can we automatically detect weather and traffic change events and prevent from the traffic delay or harsh weather accidents? To this end, we propose a novel framework to address this problem. This paper develops techniques for efficiently detecting rapid weather change events and analyzing their impacts on the traffic flow characteristics of a highway network. It is composed of three components, including 1) detection of rapid weather change events in a highway network using the streaming weather information from a sensor network of weather stations; 2) detection of rapid traffic change events on the traffic flow characteristics (e.g., travel time) of the highway network; and 3) analysis of correlations between the detected weather and traffic change events in space and time. The proposed approach was applied to a weather dataset provided by New York State Mesonet and a traffic flow dataset the National Performance Management Research Data Set (NPMRDS) provided by NYSDOT. The empirical results provide potential evidence about the significant impacts of rapid weather change events on traffic flow characteristics of the Interstate 90 (I-90) Highway in the state of New York. We show the quantitative performance evaluation of our change event detection algorithm and three baseline methods on manually labeled the weather dataset and our method outperforms baselines in terms of precision, recall and F-score. We present the analysis of Top K detected change events as case studies and also provide the spatio-temporal correlation statistics of top k weather and traffic change events. The limitations of the proposed approach and the empirical study are also discussed. Numéro de notice : A2020-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00395-x date de publication en ligne : 12/02/2020 En ligne : https://doi.org/10.1007/s10707-020-00395-x Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95263 [article]

Improving operational radar rainfall estimates using profiler observations over complex terrain in Northern California / Haonan Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

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Mapping 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)  

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Typology of meteorological weather forecast maps printed in world newspapers / Jaromir Kolejka in Cartographic journal (the), Vol 57 n° 1 (February 2020)  

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High-resolution models of tropospheric delays and refractivity based on GNSS and numerical weather prediction data for alpine regions in Switzerland / Karina Wilgan in Journal of geodesy, vol 93 n°6 (June 2019)  

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A 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)  

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Understanding of atmospheric systems with efficient numerical methods for observation and prediction / Lei-Ming Ma (2019)  

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Optimum stochastic modeling for GNSS tropospheric delay estimation in real-time / Tomasz Hadas in GPS solutions, vol 21 n° 3 (July 2017)  

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Real-time precise point positioning augmented with high-resolution numerical weather prediction model / Karina Wilgan in GPS solutions, vol 21 n° 3 (July 2017)  

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Tropospheric refractivity and zenith path delays from least-squares collocation of meteorological and GNSS data / Karina Wilgan in Journal of geodesy, vol 91 n° 2 (February 2017)  

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A high-quality reprocessed ground-based GPS dataset for atmospheric process studies, radiosonde and model evaluation, and reanalysis of HyMeX Special Observing Period / Olivier Bock in Quarterly Journal of the Royal Meteorological Society, vol 142 n° S1 (August 2016)  

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