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Extension of Satellite Altimetry Jason-2 Sea Level Anomalies Towards the Red Sea Coast Using Polynomial Harmonic Techniques

机译:利用多项式谐波技术将卫星测高仪Jason-2海平面异常扩展到红海沿岸

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摘要

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 30-50km 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.
机译:卫星测高数据在海岸附近正面临着巨大挑战。这些挑战的出现是由于校正的基本困难以及脚印中的土地污染,导致海岸附近的这些数据被拒绝。已经进行了一些研究,以将这些数据扩展到海岸。在红海上,测高数据由间隙组成,这些间隙距海岸约30-50公里。两种方法用于处理Jason-2卫星测高海平面异常(SLA)并将其扩展到红海海岸。傅里叶级数模型(FSM)和正弦模型的多项式和(SSM)。 FSM模型技术使用傅里叶级数,并且统计分析反映了与观测数据和AVISO数据之间的密切关系,具有正相关关系。第二种预测技术,SSM模型,取决于正弦多项式和,不反映与观测值和靠近海岸的AVISO数据的任何关系,并且相关系数(CC)弱且为负。与SSM模型输出相比,FSM模型输出在SLA数据中产生的结果明显更好,更准确。

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