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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 2781 - 2793 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'eau Résumé : (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 [article]

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1106 - 1117 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 IGN] antenne radar [Termes IGN] bruit thermique [Termes IGN] humidité de l'air [Termes IGN] modèle atmosphérique [Termes IGN] nuage [Termes IGN] rapport signal sur bruit [Termes IGN] réflectivité [Termes IGN] télédétection en hyperfréquence Résumé : (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 [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]

Precipitation frequency in MED and EURO-CORDEX ensembles from 0.44° to convective permitting resolution: what explains the differences? / Minh Ha-Truong (2021)  

Public Titre : Precipitation frequency in MED and EURO-CORDEX ensembles from 0.44° to convective permitting resolution: what explains the differences? Type de document : Article/Communication Auteurs : Minh Ha-Truong, Auteur ; Sophie Bastin, Auteur ; Philippe Drobinski, Auteur ; Lluis Fita, Auteur ; Marjolaine Chiriaco, Auteur ; Jan Polcher, Auteur ; Olivier Bock , Auteur Editeur : Munich [Allemagne] : European Geosciences Union EGU Année de publication : 2021 Conférence : EGU 2021, General Assembly 19/04/2021 30/04/2021 en ligne OA Abstracts only Importance : 1 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] bassin méditerranéen [Termes IGN] convection [Termes IGN] données météorologiques [Termes IGN] Europe (géographie politique) [Termes IGN] modèle atmosphérique [Termes IGN] orographie [Termes IGN] précipitation [Termes IGN] teneur intégrée en vapeur d'eau Numéro de notice : C2021-001 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans En ligne : https://meetingorganizer.copernicus.org/EGU21/EGU21-8843.html Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97582

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8427 - 8437 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 IGN] analyse diachronique [Termes IGN] apprentissage profond [Termes IGN] changement climatique [Termes IGN] classification par réseau neuronal [Termes IGN] humidité du sol [Termes IGN] modèle atmosphérique [Termes IGN] radiomètre à hyperfréquence [Termes IGN] température au sol Résumé : (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 [article]

Raytracing atmospheric delays in ground-based GNSS reflectometry / T. Nicolaidou in Journal of geodesy, vol 94 n° 8 (August 2020)  

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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)  

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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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Comparison of atmospheric mass density models using a new data source: COSMIC satellite ephemerides / Yang Yang (2020)  

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Sensitivity of GPS tropospheric estimates to mesoscale convective systems in West Africa / Samuel Nahmani in Atmospheric chemistry and physics, vol 19 n° 14 (July 2019)  

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Improving 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)  

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Robust 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)  

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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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Study of trends and variability of atmospheric water vapour with climate models and observations from global gnss network / Ana-Claudia Bernardes Parracho (2017)  

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Quantitative assessment of meteorological and tropospheric Zenith Hydrostatic Delay models / Di Zhang in Advances in space research, vol 58 n° 6 (September 2016)  

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