Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning
Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning
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使用可解释的机器学习了解美国东南部每日降水量的可预测性
DOI：
10.1175/aies-d-22-0011.1
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Kirtman, Ben P.
中科院分区：
文献类型：
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作者：
Pegion, Kathy;Becker, Emily J.;Kirtman, Ben P.
我们使用机器学习模型研究了与大尺度气候变率的同时预报相关的美国东南部(SEUS)日降水异常征兆的可预报性。使用基于指数的气候预报器和大尺度环流网格场作为预报器的模式。使用气候现象指数作为预测指标的Logistic回归(LR)和完全连接的神经网络产生的预测既不准确也不可靠，这表明指数本身并不是好的预测指标。使用网格场作为预测因子，LR和卷积神经网络(CNN)比基于指数的模型更准确。然而，只有美国有线电视新闻网才能做出可靠的预测，用来识别对机会的预测。使用可解释的机器学习，我们确定哪些变量和输入域的网格点与CNN中自信和正确的预测最相关。我们的结果表明，以850百帕位势高度和纬向风的最大相关性为代表的局地环流对作出熟练的、大概率的预报是最重要的。相应的合成异常与冬季的厄尔尼诺-南方涛动、夏季的大西洋年代际振荡和北大西洋副热带高压有关。
We investigate the predictability of the sign of daily southeastern U.S. (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using index-based climate predictors and gridded fields of large-scale circulation as predictors are utilized. Logistic regression (LR) and fully connected neural networks using indices of climate phenomena as predictors produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. Using gridded fields as predictors, an LR and convolutional neural network (CNN) are more accurate than the index-based models. However, only the CNN can produce reliable predictions that can be used to identify forecasts of opportunity. Using explainable machine learning we identify which variables and grid points of the input fields are most relevant for confident and correct predictions in the CNN. Our results show that the local circulation is most important as represented by maximum relevance of 850-hPa geopotential heights and zonal winds to making skillful, high-probability predictions. Corresponding composite anomalies identify connections with El Niño–Southern Oscillation during winter and the Atlantic multidecadal oscillation and North Atlantic subtropical high during summer.