TRU-NET：一种深度学习的降雨高分辨率预测方法
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
摘要：
气候模式（CM）用于评估气候变化对洪水和强降水事件风险的影响。然而，这些数值模拟器有困难准确地表示降水事件，主要是由于有限的空间分辨率时，模拟大气中的多尺度动力学。为了提高对高分辨率降水的预测，我们应用了一种深度学习（DL）方法，该方法使用比当地降水更可预测的模型场（天气变量）的CM模拟输入。为此，我们提出了TRU-NET（时间递归U-Net），一个编码器-解码器模型，具有一个新的2D交叉注意力机制之间的连续卷积递归层，以有效地模拟多尺度时空天气过程。我们使用一个条件连续的损失函数来捕捉降雨的零偏%极端事件模式。实验表明，我们的模型始终达到较低的RMSE和MAE分数比流行的DL模型在短期降水预测和改进后的国家的最先进的动力学天气模型的降雨预测。此外，通过评估我们的模型在各种训练和测试数据制定策略下的性能，我们表明我们的深度学习方法有足够的数据来输出跨季节和不同地区的鲁棒，高质量的结果。
英文摘要：
Climate models (CM) are used to evaluate the impact of climate change on the risk of floods and strong precipitation events. However, these numerical simulators have difficulties representing precipitation events accurately, mainly due to limited spatial resolution when simulating multi-scale dynamics in the atmosphere. To improve the prediction of high resolution precipitation we apply a Deep Learning (DL) approach using an input of CM simulations of the model fields (weather variables) that are more predictable than local precipitation. To this end, we present TRU-NET (Temporal Recurrent U-Net), an encoder-decoder model featuring a novel 2D cross attention mechanism between contiguous convolutional-recurrent layers to effectively model multi-scale spatio-temporal weather processes. We use a conditional-continuous loss function to capture the zero-skewed %extreme event patterns of rainfall. Experiments show that our model consistently attains lower RMSE and MAE scores than a DL model prevalent in short term precipitation prediction and improves upon the rainfall predictions of a state-of-the-art dynamical weather model. Moreover, by evaluating the performance of our model under various, training and testing, data formulation strategies, we show that there is enough data for our deep learning approach to output robust, high-quality results across seasons and varying regions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
