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On the Prediction of Upwelling Events at the Colombian Caribbean Coasts from Modis-SST Imagery

机译:基于Modis-SST影像对哥伦比亚加勒比海沿岸上升流事件的预测

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摘要

The upwelling cores on the Caribbean Colombian coasts are mainly located at the Peninsula de la Guajira and Cabo de la Aguja. We used monthly averaged Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature as the only information to build up a prediction model for the upwelling events. This comprised two steps: (i) the reduction of the complexity by means of the Karhunen–Loève transform and (ii) a prediction model of time series. Two prediction models were considered: (a) a parametric autoregressive-moving average (ARMA) time series from the Box–Jenkins methodology and (b) a harmonic synthesis model. The harmonic synthesis also comprised of two steps: the maximum entropy spectral analysis and a least-squares harmonic analysis on the set of frequencies. The parametric ARMA time series model failed at the time of prediction with a very narrow range, and it was quite difficult to apply. The harmonic synthesis allowed prediction with a horizon of six months with a correlation of about 0.80. The results can be summarized using the time series of the weights of the different oscillation modes, their spatial structures with the nodal lines, and a high confidence model with a horizon of prediction of about four months.
机译:加勒比海哥伦比亚海岸的上升流核心主要位于瓜伊拉半岛和阿古贾角。我们使用每月平均中等分辨率成像光谱仪(MODIS)的海面温度作为建立上升事件预测模型的唯一信息。这包括两个步骤:(i)通过Karhunen-Loève变换降低复杂度,以及(ii)时间序列的预测模型。考虑了两个预测模型:(a)Box-Jenkins方法学中的参数自回归移动平均(ARMA)时间序列,以及(b)谐波合成模型。谐波合成还包括两个步骤:最大熵频谱分析和一组频率的最小二乘谐波分析。在预测时,参数ARMA时间序列模型失败了,范围非常狭窄,很难应用。谐波合成可以进行六个月的预测,相关系数约为0.80。可以使用不同振荡模式的权重的时间序列,带有节点线的空间结构以及具有大约四个月的预测范围的高置信度模型来总结结果。

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