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A parameterization of the cloud scattering polarization signal derived from GPM observations for microwave fast radative transfer models / Victoria Sol Galligani in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 11 (November 2021) . - pp 8968 - 8977 Titre : A parameterization of the cloud scattering polarization signal derived from GPM observations for microwave fast radative transfer models Type de document : Article/Communication Auteurs : Victoria Sol Galligani, Auteur ; Die Wang, Auteur ; Paola Belen Corales, Auteur ; Catherine Prigent, Auteur Année de publication : 2021 Article en page(s) : pp 8968 - 8977 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image GPM [Termes IGN] image radar [Termes IGN] latitude [Termes IGN] modèle atmosphérique [Termes IGN] modèle de transfert radiatif [Termes IGN] nuage [Termes IGN] polarisation [Termes IGN] prévision météorologique [Termes IGN] radiomètre à hyperfréquence [Termes IGN] reconstruction du signal [Termes IGN] variation saisonnière Résumé : (auteur) Microwave cloud polarized observations have shown the potential to improve precipitation retrievals since they are linked to the orientation and shape of ice habits. Stratiform clouds show larger brightness temperature (TB) polarization differences (PDs), defined as the vertically polarized TB (TBV) minus the horizontally polarized TB (TBH), with ~10 K PD values at 89 GHz due to the presence of horizontally aligned snowflakes, while convective regions show smaller PD signals, as graupel and/or hail in the updraft tend to become randomly oriented. The launch of the global precipitation measurement (GPM) microwave imager (GMI) has extended the availability of microwave polarized observations to higher frequencies (166 GHz) in the tropics and midlatitudes, previously only available up to 89 GHz. This study analyzes one year of GMI observations to explore further the previously reported stable relationship between the PD and the observed TBs at 89 and 166 GHz, respectively. The latitudinal and seasonal variability is analyzed to propose a cloud scattering polarization parameterization of the PD-TB relationship, capable of reconstructing the PD signal from simulated TBs. Given that operational radiative transfer (RT) models do not currently simulate the cloud polarized signals, this is an alternative and simple solution to exploit the large number of cloud polarized observations available. The atmospheric radiative transfer simulator (ARTS) is coupled with the weather research and forecasting (WRF) model, in order to apply the proposed parameterization to the RT simulated TBs and hence infer the corresponding PD values, which show to reproduce the observed GMI PDs well. Numéro de notice : A2021-886 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3049921 Date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3049921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98871 [article]

Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)  

Public [article]  inRemote sensing of environment > vol 257 (May 2021) . - n° 112355 Titre : Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning Type de document : Article/Communication Auteurs : Malarvizhi Arulraj, Auteur ; Ana P. Baros, Auteur Année de publication : 2021 Article en page(s) : n° 112355 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Appalaches [Termes IGN] apprentissage automatique [Termes IGN] bande S [Termes IGN] classification automatique [Termes IGN] classification barycentrique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image GPM [Termes IGN] orographie [Termes IGN] précipitation Résumé : (auteur) Ground-clutter is a significant cause of missed-detection and underestimation of precipitation in complex terrain from space-based radars such as the Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR). This research proposes an Artificial Intelligence (AI) framework consisting of a precipitation detection model (PDM) and a precipitation regime classification model (PCM) to improve orographic precipitation retrievals from GPM-DPR using machine learning. The PDM is a Random Forest Classifier using GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and low-level precipitation mixing ratios from the High-Resolution Rapid Refresh (HRRR) analysis as inputs. The PCM is a Convolutional Neural Network that predicts the precipitation regime class, defined independently based on quantitative features of ground-based radar reflectivity profiles, using GPM DPR Ku-band (Ku-PR) reflectivity profiles and GMI Tbs. The AI framework is demonstrated for warm-season precipitation in the Southern Appalachian Mountains over. Numéro de notice : A2021-279 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112355 Date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112355 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97372 [article]

Estimates of spaceborne precipitation radar pulsewidth and beamwidth using sea surface echo data / Kaya Kanemaru in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 8 (August 2020) . - pp 5291 - 5303 Titre : Estimates of spaceborne precipitation radar pulsewidth and beamwidth using sea surface echo data Type de document : Article/Communication Auteurs : Kaya Kanemaru, Auteur ; Toshio Iguchi, Auteur ; Takeshi Masaki, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 5291 - 5303 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande Ku [Termes IGN] climat tropical [Termes IGN] écho radar [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] image GPM [Termes IGN] image TRMM-MI [Termes IGN] impulsion [Termes IGN] précipitation [Termes IGN] surface de la mer [Termes IGN] surface équivalente radar Résumé : (auteur) Calibration consistency between Ku-band radars flown on the Tropical Rainfall Measuring Mission’s (TRMM’s) precipitation radar (PR) and the global precipitation measurement (GPM) mission’s dual-frequency PR (DPR) can be attained by the use of the normalized radar cross section (NRCS) or σ0 over the oceans. With the use of the sea surface echo (SSE) data obtained from the spaceborne PRs, this article aims to estimate the radar parameters of pulsewidth and beamwidth and to evaluate the bias in the NRCS estimates caused by the discrete range sampling. Since the SSE shape is closely related to the received pulsewidth and the two-way cross-track beamwidth, those parameters are individually estimated from the SSE shapes. The SSE shapes are also used to evaluate the impact of the discrete range sampling on the NRCS statistics. The pulsewidth and beamwidth estimated from the SSEs compare well with the level-1 values and accurately reflect changes in the configuration of the radars. The NRCS statistics in GPM version 06 show that the calibration consistency between GPM KuPR and TRMM PR is evaluated within the range of −0.39 to +0.03 dB (−0.48 to +0.11 dB) with (without) the peak correction. Numéro de notice : A2020-471 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2963090 Date de publication en ligne : 22/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2963090 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95574 [article]

Improved algorithms for the measurement of total precipitable water and cloud liquid water from SARAL microwave radiometer observations / Rajput Neha Mangalsinh in Marine geodesy, vol 42 n° 4 (July 2019)  

Public [article]  inMarine geodesy > vol 42 n° 4 (July 2019) . - pp 367 - 381 Titre : Improved algorithms for the measurement of total precipitable water and cloud liquid water from SARAL microwave radiometer observations Type de document : Article/Communication Auteurs : Rajput Neha Mangalsinh, Auteur ; Atul Kumar Varma, Auteur Année de publication : 2019 Article en page(s) : pp 367 - 381 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] cryosphère [Termes IGN] données altimétriques [Termes IGN] hauteurs de mer [Termes IGN] image GPM [Termes IGN] image SSMIS [Termes IGN] image TRMM-MI [Termes IGN] nuage [Termes IGN] précipitation [Termes IGN] radiomètre à hyperfréquence [Termes IGN] température de luminance [Termes IGN] vapeur d'eau Résumé : (auteur) SARAL carried onboard a radar altimeter that provides very precise measurements of the sea surface height (SSH). Like other altimetric missions, SARAL carries a passive microwave radiometer (PMR) for wet tropospheric correction to SSH. In the present study, new algorithms are developed for the retrieval of cloud liquid water (CLW) and total precipitable water vapor (TPW) over the global oceans from PMR measurements of the brightness temperatures. A radiative transfer and genetic algorithm based retrieval scheme is proposed for the estimation of CLW and TPW from SARAL PMR. The comparisons of CLW from PMR with independent measurements from GPM-GMI and SSMIS within and outside ±40° latitudes show correlation (R) of 0.86 and 0.83, bias of 0.7 and −3.61 mg/cm2, and root mean square error (RMSE) of 8.42 and 8.07 mg/cm2, respectively. Similarly, TPW from PMR with GPM-GMI and SSMIS show R of 0.99 and 0.98, bias of −0.04 and −0.03 g/cm2 and RMSE of 0.17 and 0.17 g/cm2, respectively. The retrieval accuracy of CLW and TPW from the new algorithms is compared with these parameters provided in the SARAL geophysical data records as finished products, which showed substantial improvement in the quality of the parameters from the new algorithm. Numéro de notice : A2019-282 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2019.1624662 Date de publication en ligne : 12/06/2019 En ligne : https://doi.org/10.1080/01490419.2019.1624662 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93116 [article]

Remote sensing of precipitation, 1. Volume 1 / Silas Michaelides (2019)  

Public Titre de série : Remote sensing of precipitation, 1 Titre : Volume 1 Type de document : Monographie Auteurs : Silas Michaelides, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 480 p. ISBN/ISSN/EAN : 978-3-03921-286-6 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande K [Termes IGN] bande X [Termes IGN] climatologie [Termes IGN] cyclone [Termes IGN] données GNSS [Termes IGN] données météorologiques [Termes IGN] fréquence [Termes IGN] image GPM [Termes IGN] image radar [Termes IGN] météorologie [Termes IGN] neige [Termes IGN] pluie [Termes IGN] précipitation [Termes IGN] récepteur monofréquence [Termes IGN] satellite géostationnaire [Termes IGN] télédétection en hyperfréquence [Termes IGN] variation saisonnière Résumé : (Editeur) Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. Numéro de notice : 26511A Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03921-286-6 En ligne : http://doi.org/10.3390/books978-3-03921-286-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97192

Voir aussi

• Remote sensing of precipitation, 2. Volume 2 / Silas Michaelides (2019)  

Remote sensing of precipitation, 2. Volume 2 / Silas Michaelides (2019)  

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Joint analysis of passive and active land surface responses for Global Precipitation Measurement / Iris de Gelis (2017) 

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