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Texture‐based classification of high‐resolution precipitation forecasts with machine‐learning methods
Quarterly Journal of the Royal Meteorological Society ( IF 8.9 ) Pub Date : 2020-05-16 , DOI: 10.1002/qj.3823
Yamina Hamidi 1 , Laure Raynaud 1 , Lucie Rottner 1 , Philippe Arbogast 1
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Object‐based methods are commonly used for verification and postprocessing of high‐resolution precipitation forecasts. They usually detect objects based on intensity criteria only, without considering the spatial organization of rainfall, known as texture. This article evaluates the performance of several machine‐learning methods to detect “continuous” and “intermittent” rainfall patterns in the forecasts of the French convective‐scale Arome model. A sliding‐window segmentation algorithm, which applies a classification model at each grid point, is implemented. Several classifiers and input textural features are compared. Overall, intermittent precipitation is the most difficult to detect. The random forest classifier is shown to provide the best classification results independently of the predictor used, with a surprising ability to extract a relevant signal from a synthetic descriptor such as the rainfall cumulative distribution function, as well as from the large amount of unprocessed information provided by neighbouring grid points. On the other hand, the logistic regression classifier needs a texture‐oriented predictor, such as the statistics derived from the grey‐level co‐occurrence matrix, to perform well. Global insight into model behaviour is then obtained by examining the importance of input features. Finally, we show that random forests trained on Arome deterministic forecasts can be applied successfully to discriminate between precipitation textures in different Arome configuration outputs and gridded observations.

中文翻译:

利用机器学习方法对高分辨率降水预测进行基于纹理的分类

基于对象的方法通常用于高分辨率降水预报的验证和后处理。他们通常仅根据强度标准检测物体,而不考虑降雨的空间组织(称为纹理)。本文评估了法国对流尺度Arome模型预报中几种检测“连续”和“间歇”降雨模式的机器学习方法的性能。实现了一种滑动窗口分割算法,该算法在每个网格点上应用了分类模型。比较了几个分类器和输入纹理特征。总体而言,间歇性降水最难检测。随机森林分类器可提供最佳的分类结果,而与所使用的预测变量无关,具有令人惊讶的能力,可以从合成描述符(例如降雨累积分布函数)以及从相邻网格点提供的大量未处理信息中提取相关信号。另一方面,逻辑回归分类器需要一个面向纹理的预测器,例如从灰度共现矩阵得出的统计量,才能表现良好。然后,通过检查输入功能的重要性来获得对模型行为的全局了解。最后,我们显示了可以对Arome确定性预报进行训练的随机森林可以成功地用于区分不同Arome配置输出和网格观测中的降水纹理。以及来自相邻网格点提供的大量未处理信息。另一方面,逻辑回归分类器需要一个面向纹理的预测器,例如从灰度共现矩阵得出的统计量,才能表现良好。然后,通过检查输入功能的重要性来获得对模型行为的全局了解。最后,我们显示了可以对Arome确定性预报进行训练的随机森林可以成功地用于区分不同Arome配置输出和网格观测中的降水纹理。以及来自相邻网格点提供的大量未处理信息。另一方面,逻辑回归分类器需要一个面向纹理的预测器,例如从灰度共现矩阵得出的统计量,才能表现良好。然后,通过检查输入功能的重要性来获得对模型行为的全局了解。最后,我们显示了可以对Arome确定性预报进行训练的随机森林可以成功地用于区分不同Arome配置输出和网格观测中的降水纹理。然后,通过检查输入功能的重要性来获得对模型行为的全局了解。最后,我们显示了可以对Arome确定性预报进行训练的随机森林可以成功地用于区分不同Arome配置输出和网格观测中的降水纹理。然后,通过检查输入功能的重要性来获得对模型行为的全局了解。最后,我们显示了可以对Arome确定性预报进行训练的随机森林可以成功地用于区分不同Arome配置输出和网格观测中的降水纹理。
更新日期:2020-05-16
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