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Sea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach / Jian Zhao in Marine geodesy, vol 42 n° 4 (July 2019)  

Public [article]  inMarine geodesy > vol 42 n° 4 (July 2019) . - pp 344 - 366 Titre : Sea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach Type de document : Article/Communication Auteurs : Jian Zhao, Auteur ; Yanguo Fan, Auteur ; Yuxiang Mu, Auteur Année de publication : 2019 Article en page(s) : pp 344 - 366 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] détection d'anomalie [Termes IGN] données altimétriques [Termes IGN] données Jason [Termes IGN] données Topex-Poseidon [Termes IGN] image ERS-SAR [Termes IGN] méthode des moindres carrés [Termes IGN] montée du niveau de la mer [Termes IGN] Pacifique nord [Termes IGN] prévision [Termes IGN] réseau neuronal artificiel [Termes IGN] série temporelle Résumé : (auteur) Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2 cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast. Numéro de notice : A2019-281 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2019.1626306 Date de publication en ligne : 12/06/2019 En ligne : https://doi.org/10.1080/01490419.2019.1626306 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93115 [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]