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A Study of Predictability of SST at Different Time Scales Based on Satellite Time

机译:基于卫星时间的不同时间尺度海表温度的可预测性研究

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Sea surface temperature (SST) is both an important variable for weather and ocean forecasting, but also a key indicator of climate change. Predicting future SST at different time scales constitutes an important scientific problem. The traditional approach to prediction is achieved through numerical simulation, but it is difficult to obtain a detailed knowledge of ocean initial conditions and forcing. This paper proposes a improved prediction system based on SOFT proposed by Alvarez et al and studies the predictability of SST at different time scales, i.e., 5 day, 10 day, 15 day, 20 day and month ahead. This method is used to forecast the SST in the Yangtze River estuary and its adjacent areas. The period of time ranging from Jan 1st 2000 to Dec 31st 2005 is employed to build the prediction system and the period of time ranging from Jan 1st 2006 to Dec 31st 2007 is employed to validate the performance of this prediction system. Resultsrnindicate: The prediction errors of 5 day, 10 day, 15 day, 20 day and monthly ahead are 0.78℃,0.86℃,0.90℃,1.00℃ andrn1.45℃ respectively. The longer of time scales prediction, the worse of prediction capability. Compared with the SOFTrnsystem proposed by Alvarez et al, the improved prediction system is more robust. Merging more satellite data and trying to better reflect the real state of ocean variables, we can greatly improve the predictive precision of long time scale.
机译:海面温度(SST)既是天气预报和海洋预报的重要变量,又是气候变化的关键指标。预测不同时间范围内的未来SST构成了重要的科学问题。传统的预测方法是通过数值模拟实现的,但是很难获得有关海洋初始条件和强迫的详细知识。本文提出了一种基于Alvarez等人提出的SOFT的改进的预测系统,并研究了在不同时间段(即提前5天,10天,15天,20天和一个月)SST的可预测性。该方法用于预测长江口及其附近地区的海表温度。使用从2000年1月1日到2005年12月31日的时间范围来构建预测系统,并使用从2006年1月1日到2007年12月31日的时间范围来验证此预测系统的性能。结果表明:未来5天,10天,15天,20天和每月的预测误差分别为0.78℃,0.86℃,0.90℃,1.00℃和1.45℃。时间尺度的预测时间越长,预测能力越差。与Alvarez等人提出的SOFTrn系统相比,改进的预测系统更加健壮。合并更多的卫星数据并试图更好地反映海洋变量的真实状态,我们可以大大提高长时间尺度的预测精度。

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