Abstract This study uses a machine learning algorithm, the bagged regression tree, to detect error patterns within 24-h forecasts of significant wave height time series. The input to the machine learning algorithm were bulk parameter outputs of the numerical wave model (WaveWatch III) and wind information from the Global Forecast System at buoy locations along the California-Oregon border in the United States. The output of the algorithm were predictions of hourly deviations between numerical model output and buoy observations of significant wave height. When these deviations were applied as corrections to the forecasts, error metrics root-mean-squared-error, bias, percent error, and scatter index were reduced in several different experiments, confirming that the error pattern was successfully detected by the machine learning algorithm. Furthermore, the detected error pattern was consistent between buoys at different locations, as presented in a geo-spatial application of the machine learning algorithm. As a descriptive tool, the algorithm delineated regions of similar error within the context of model phase space (significant wave height and mean wave period ( T m 01 )). Specifically, the algorithm detected significant wave height overestimations for significant wave heights greater than 3.4 m, wave period greater than 9.1 s, and waves coming from the W-NW quadrant.Also, for significant wave heights greater than the 95th percentile value (5.4 m), the algorithm detected differences in model phase space associated with mean error patterns.

中文翻译：

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机器学习算法在波浪模型输出中确定和描述误差模式的应用

摘要 本研究使用机器学习算法，即袋装回归树，来检测重要波高时间序列 24 小时预测内的错误模式。机器学习算法的输入是数值波浪模型 (WaveWatch III) 的批量参数输出和来自美国加利福尼亚-俄勒冈州边界沿线浮标位置的全球预报系统的风信息。该算法的输出是对数值模型输出与显着波高浮标观测值之间每小时偏差的预测。当这些偏差被用作预测的修正时，误差度量的均方根误差、偏差、百分比误差和分散指数在几个不同的实验中都减少了，证实了机器学习算法成功检测到错误模式。此外，如机器学习算法的地理空间应用中所示，检测到的错误模式在不同位置的浮标之间是一致的。作为一种描述性工具，该算法在模型相空间（显着波高和平均波周期 (T m 01 )）的背景下描绘了类似误差的区域。具体而言，该算法检测到显着波高高估的显着波高大于 3.4 m、波周期大于 9.1 s 以及来自 W-NW 象限的波浪。此外，对于大于 95% 百分位值 (5.4 m) 的显着波高)，算法检测到与平均误差模式相关的模型相位空间的差异。如机器学习算法的地理空间应用所示。作为一种描述性工具，该算法在模型相空间（显着波高和平均波周期 (T m 01 )）的背景下描绘了类似误差的区域。具体而言，该算法检测到显着波高高估的显着波高大于 3.4 m、波周期大于 9.1 s 以及来自 W-NW 象限的波浪。此外，对于大于 95% 百分位值 (5.4 m) 的显着波高)，算法检测到与平均误差模式相关的模型相位空间的差异。如机器学习算法的地理空间应用所示。作为一种描述性工具，该算法在模型相空间（显着波高和平均波周期 (T m 01 )）的背景下描绘了类似误差的区域。具体而言，该算法检测到显着波高高估的显着波高大于 3.4 m、波周期大于 9.1 s 以及来自 W-NW 象限的波浪。此外，对于大于 95% 百分位值 (5.4 m) 的显着波高)，算法检测到与平均误差模式相关的模型相位空间的差异。