Descripteur
Termes descripteurs IGN > sciences naturelles > sciences de la Terre et de l'univers > géosciences > géographie physique > météorologie > aérologie > atmosphère terrestre > modèle atmosphérique
modèle atmosphériqueVoir aussi |



Etendre la recherche sur niveau(x) vers le bas
G-band radar for humidity and cloud remote sensing / Ken B. Cooper in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
![]()
[article]
Titre : G-band radar for humidity and cloud remote sensing Type de document : Article/Communication Auteurs : Ken B. Cooper, Auteur ; Richard J. Roy, Auteur ; Robert Dengler, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1106 - 1117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes descripteurs IGN] antenne radar
[Termes descripteurs IGN] bruit thermique
[Termes descripteurs IGN] humidité de l'air
[Termes descripteurs IGN] modèle atmosphérique
[Termes descripteurs IGN] nuage
[Termes descripteurs IGN] rapport signal sur bruit
[Termes descripteurs IGN] réflectivité
[Termes descripteurs IGN] télédétection en hyperfréquenceRésumé : (auteur) VIPR (vapor in-cloud profiling radar) is a tunable G-band radar designed for humidity and cloud remote sensing. VIPR uses all-solid-state components and operates in a frequency-modulated continuous-wave (FMCW) radar mode, offering a transmit power of 200–300 mW. Its typical chirp bandwidth of 10 MHz over a center-frequency tuning span of 167–174.8 GHz results in a nominal range resolution of 15 m. The radar’s measured noise figure over the transmit band is between 7.4 and 10.4 dB, depending on its frequency and hardware configuration, and its calculated antenna gain is 58 dB. These parameters mean that with typical 1 ms chirp times, single-pulse cloud reflectivities as low as −26 dBZ are detectable with unity signal-to-noise at 5 km. Experimentally, radar returns from ice clouds above 10 km in height have been observed from the ground. VIPR’s absolute sensitivity was validated using a spherical metal target in the radar antenna’s far-field, and a G-band switch has been implemented in an RF calibration loop for periodic recalibration. The radar achieves high sensitivity with thermal noise limited detection both by virtue of its low-noise RF architecture and by using a quasioptical duplexing method that preserves ultrahigh transmit/receive isolation despite operation in an FMCW mode with a single primary antenna shared by the transmitter and receiver. Numéro de notice : A2021-112 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2995325 date de publication en ligne : 04/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2995325 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96916
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1106 - 1117[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)
![]()
[article]
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 descripteurs IGN] corrélation croisée normalisée
[Termes descripteurs IGN] dissymétrie
[Termes descripteurs IGN] données GNSS
[Termes descripteurs IGN] Europe centrale
[Termes descripteurs IGN] modèle atmosphérique
[Termes descripteurs IGN] prévision météorologique
[Termes descripteurs IGN] retard troposphérique zénithal
[Termes descripteurs IGN] série temporelleRésumé : (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
in GPS solutions > vol 25 n° 1 (January 2021) . - n° 12[article]A deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer / Xing Yan in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
![]()
[article]
Titre : A deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer Type de document : Article/Communication Auteurs : Xing Yan, Auteur ; Chen Liang, Auteur ; Yize Jiang, Auteur Année de publication : 2020 Article en page(s) : pp 8427 - 8437 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] analyse diachronique
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] changement climatique
[Termes descripteurs IGN] classification par réseau neuronal
[Termes descripteurs IGN] humidité du sol
[Termes descripteurs IGN] modèle atmosphérique
[Termes descripteurs IGN] radiomètre à hyperfréquence
[Termes descripteurs IGN] température au solRésumé : (auteur) The ground-based microwave radiometer (MWR) retrieves atmospheric profiles with a high temporal resolution for temperature and humidity up to a height of 10 km. Such profiles are critical for understanding the evolution of climate systems. To improve the accuracy of profile retrieval in MWR, we developed a deep learning approach called batch normalization and robust neural network (BRNN). In contrast to the traditional backpropagation neural network (BPNN), which has previously been applied for MWR profile retrieval, BRNN reduces overfitting and has a greater capacity to describe nonlinear relationships between MWR measurements and atmospheric structure information. Validation of BRNN with the radiosonde demonstrates a good retrieval capability, showing a root-mean-square error of 1.70 K for temperature, 11.72% for relative humidity (RH), and 0.256 g/m 3 for water vapor density. A detailed comparison with various inversion methods (BPNN, extreme gradient boosting, support vector machine, ridge regression, and random forest) has also been conducted in this research, using the same training and test data sets. From the comparison, we demonstrated that BRNN significantly improves retrieval accuracy, particularly for the retrieval of temperature and RH near the surface. Numéro de notice : A2020-741 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2987896 date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2987896 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96371
in IEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8427 - 8437[article]Raytracing atmospheric delays in ground-based GNSS reflectometry / T. Nicolaidou in Journal of geodesy, vol 94 n° 8 (August 2020)
![]()
[article]
Titre : Raytracing atmospheric delays in ground-based GNSS reflectometry Type de document : Article/Communication Auteurs : T. Nicolaidou, Auteur ; M.C. Santos, Auteur ; Simon D.P. Williams, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 68 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes descripteurs IGN] coin réflecteur
[Termes descripteurs IGN] correction atmosphérique
[Termes descripteurs IGN] lancer de rayons
[Termes descripteurs IGN] modèle atmosphérique
[Termes descripteurs IGN] réflectométrie par GNSS
[Termes descripteurs IGN] réfraction
[Termes descripteurs IGN] temps de propagationRésumé : (auteur) Several studies have recognized that Global Navigation Satellite System Reflectometry (GNSS-R) is subject to atmospheric propagation delays. Unfortunately, there is little information in the peer-reviewed literature about the methods and algorithms involved in correcting for this effect. We have developed an atmospheric ray-tracing procedure to solve rigorously the three-point boundary value problem of ground-based GNSS-R observations. We defined the reflection-minus-direct or interferometric delay in terms of vacuum distance and radio length. We clarified the roles of linear and angular refraction in splitting the total delay in two components, along-path and geometric. We have introduced for the first time two subcomponents of the atmospheric geometric delay, the geometry shift and the geometric excess. We have defined corresponding atmospheric altimetry corrections necessary for unbiased altimetry retrievals. Using simulations, we examined the interferometric atmospheric delay for a range of typical scenarios, where it attained centimeter-level values at low satellite elevation angles ~ 5° for a 10-m high station. We found a linear and exponential dependence on reflector height and satellite elevation angle, respectively. A similar trend was found for the atmospheric altimetry correction, albeit with an amplified meter-level magnitude. The two delay components were similar near the horizon while the angular one vanished at zenith. For the altimetry correction components, both remained non-zero at zenith. We thus quantified the atmospheric bias in GNSS-R sea level retrievals. Numéro de notice : A2020-538 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01390-8 date de publication en ligne : 23/07/2020 En ligne : https://doi.org/10.1007/s00190-020-01390-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95731
in Journal of geodesy > vol 94 n° 8 (August 2020) . - n° 68[article]Comparative analysis of different atmospheric surface pressure models and their impacts on daily ITRF2014 GNSS residual time series / Zhao Li in Journal of geodesy, vol 94 n°4 (April 2020)
![]()
[article]
Titre : Comparative analysis of different atmospheric surface pressure models and their impacts on daily ITRF2014 GNSS residual time series Type de document : Article/Communication Auteurs : Zhao Li, Auteur ; Chen Wu, Auteur ; Tonie M. van Dam, Auteur ; Paul Rebischung , Auteur ; Zuheir Altamimi
, Auteur
Année de publication : 2020 Projets : 3-projet - voir note / Article en page(s) : n° 42 Note générale : bibliographie
This research is supported by the National Key Research and Development Program of China (Project 2016YFB0502101), the European Commission/Research Grants Council (RGC) Collaboration Scheme sponsored by the Research Grants Council of Hong Kong Special Administrative Region, China (Project No. E-PolyU 501/16), and the National Science Foundation for Distinguished Young Scholars of China (Grant No. 41525014).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes descripteurs IGN] analyse comparative
[Termes descripteurs IGN] coefficient de corrélation
[Termes descripteurs IGN] données GNSS
[Termes descripteurs IGN] International Terrestrial Reference Frame
[Termes descripteurs IGN] MERRA
[Termes descripteurs IGN] modèle atmosphérique
[Termes descripteurs IGN] pression atmosphérique
[Termes descripteurs IGN] radar JPL
[Termes descripteurs IGN] résidu
[Termes descripteurs IGN] série temporelle
[Termes descripteurs IGN] station GNSSRésumé : (auteur) To remove atmospheric pressure loading (ATML) effect from GNSS coordinate time series, surface pressure (SP) models are required to predict the displacements. In this paper, we modeled the 3D ATML surface displacements using the latest MERRA-2 SP grids, together with four other products (NCEP-R-1, NCEP-R-2, ERA-Interim and MERRA) for 596 globally distributed GNSS stations, and compared them with ITRF2014 residual time series. The five sets of ATML displacements are highly consistent with each other, particularly for those stations far away from coasts, of which the lowest correlations in the Up component for all the four models w.r.t MERRA-2 become larger than 0.91. ERA-Interim-derived ATML displacement performs best in reducing scatter of the GNSS height for 90.3% of the stations (89.3% for NCEP-R-1, 89.1% for NCEP-R-2, 86.4% for MERRA and 85.1% for MERRA-2). We think that this may be possibly due to the 4D variational data assimilation method applied. Considering inland stations only, more than 96% exhibit WRMS reduction in the Up direction for all five models, with an average improvement of 3–4% compared with the original ITRF2014 residual time series before ATML correction. Most stations (> 67%) also exhibit horizontal WRMS reductions based on the five models, but of small magnitudes, with most improvements (> 76%) less than 5%. In particular, most stations in South America, South Africa, Oceania and the Southern Oceans show larger WRMS reductions with MERRA-2, while all other four SP datasets lead to larger WRMS reduction for the Up component than MERRA-2 in Europe. Through comparison of the daily pressure variation from the five SP models, we conclude that the bigger model differences in the SP-induced surface displacements and their impacts on the ITRF2014 residuals for coastal/island stations are mainly due to the IB correction based on the different land–sea masks. A unique high spatial resolution land–sea mask should be applied in the future, so that model differences would come from only SP grids. Further research is also required to compare the ATML effect in ice-covered and high mountainous regions, for example the Qinghai–Tibet Plateau in China, the Andes in South America, etc., where larger pressure differences between models tend to occur. Numéro de notice : A2020-159 Affiliation des auteurs : Géodésie+Ext (mi2018-2019) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01370-y date de publication en ligne : 20/03/2020 En ligne : https://doi.org/10.1007/s00190-020-01370-y Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94813
in Journal of geodesy > vol 94 n°4 (April 2020) . - n° 42[article]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)
PermalinkSensitivity of GPS tropospheric estimates to mesoscale convective systems in West Africa / Samuel Nahmani in Atmospheric chemistry and physics, vol 19 n° 14 (July 2019)
PermalinkImproving the modeling of the atmospheric delay in the data analysis of the Intensive VLBI sessions and the impact on the UT1 estimates / Tobias Nilsson in Journal of geodesy, vol 91 n° 7 (July 2017)
PermalinkRobust GPS/BDS/INS tightly coupled integration with atmospheric constraints for long-range kinematic positioning / Houzeng Han in GPS solutions, vol 21 n° 3 (July 2017)
PermalinkTropospheric 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)
PermalinkStudy of trends and variability of atmospheric water vapour with climate models and observations from global gnss network / Ana-Claudia Bernardes Parracho (2017)
PermalinkQuantitative assessment of meteorological and tropospheric Zenith Hydrostatic Delay models / Di Zhang in Advances in space research, vol 58 n° 6 (September 2016)
PermalinkA 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)
PermalinkComparative analysis of real-time precise point positioning zenith total delay estimates / F.A. Ahmed in GPS solutions, vol 20 n° 2 (April 2016)
PermalinkCorrection troposphérique des interférogrammes issus d’images radar par mesures GNSS et modèle global d’atmosphère / Vincent Dubreuil (2016)
Permalink