Centre de documentation
scientifique

Extension of satellite altimetry Jason-2 sea level anomalies towards the Red Sea coast using polynomial harmonic techniques / A. M. Taqi in Marine geodesy, vol 40 n° 5 (September 2017)  

Public [article]  inMarine geodesy > vol 40 n° 5 (September 2017) . - pp 315 - 328 Titre : Extension of satellite altimetry Jason-2 sea level anomalies towards the Red Sea coast using polynomial harmonic techniques Type de document : Article/Communication Auteurs : A. M. Taqi, Auteur ; A. M. Al-Subhi, Auteur ; M. A. Alsaafani, Auteur Année de publication : 2017 Article en page(s) : pp 315 - 328 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] altimétrie satellitaire par radar [Termes IGN] AVISO [Termes IGN] données altimétriques [Termes IGN] données Jason [Termes IGN] équation polynomiale [Termes IGN] fonction harmonique [Termes IGN] littoral [Termes IGN] niveau de la mer [Termes IGN] Rouge, mer [Termes IGN] série de Fourier [Vedettes matières IGN] Altimétrie Résumé : (Auteur) Satellite altimetry data are facing big challenges near the coasts. These challenges arise due to the fundamental difficulties of correction and land contamination in the foot print, which result in rejection of these data near the coast. Several studies have been carried out to extend these data towards the coast. Over the Red Sea, altimetry data consist of gaps, which extend to about 30–50 km from the coast. Two methods are used for processing and extending Jason-2 satellite altimetry sea level anomalies (SLAs) towards the Red Sea coast; Fourier Series Model (FSM), and the polynomial sum of sine model (SSM). FSM model technique uses Fourier series and statistical analysis reflects strong relationship with both the observation and AVISO data, with strong and positive correlation. The second prediction technique, SSM model, depends on the polynomial sum of sine, and does not reflect any relationship with the observations and AVISO data close to the coast and the correlation coefficient (CC) is weak and negative. The FSM model output results in SLA data significantly better and more accurate than the SSM model output. Numéro de notice : A2017-668 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2017.1333549 En ligne : https://doi.org/10.1080/01490419.2017.1333549 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87143 [article]