IRENE：一种用于意大利雷达降水临近预报的卷积GRU集成模型

中文摘要 AI 辅助

我们提出了IRENE（意大利雷达集成临近预报实验），这是一种深度学习模型，用于在意大利区域以1公里空间分辨率和5分钟时间分辨率进行概率性短期降水临近预报。IRENE采用基于多尺度卷积门控循环单元（ConvGRUs）的编码器-预测器架构，并使用意大利民防部门（DPC）生成的国家雷达合成产品进行训练。一种重要性采样方案将训练重点放在与降水相关的事件上，同时采用几乎公平的连续排序概率得分（afCRPS）作为主要的概率损失函数。另外提出了两种训练配置：一种是对抗性（GAN）变体IRENE-GAN，旨在提高生成预报的空间清晰度；另一种是光谱约束变体IRENE-GAN-RAPSD，其中对抗性目标辅以对径向平均功率谱密度的显式惩罚。这三种配置与随机外推方法STEPS和预训练的深度学习模型DGMR进行了评估比较。所有IRENE配置在每个提前时间都获得了比两个基准更低的连续排序概率得分，并且秩直方图更接近均匀分布，表明其具有更好的概率技能和集成校准能力。在集成平均绝对误差方面，优势仅限于前90分钟，此后强烈衰减的DGMR场以及程度较轻的STEPS变得具有竞争力。光谱分析表明，对抗性训练消除了IRENE所表现出的尺度方差逐渐损失的问题，但代价是在长提前时间下出现过量的细尺度功率，而光谱惩罚只能部分控制这一问题。

英文摘要

We present IRENE (Italian Radar Ensemble Nowcasting Experiment), a deep learning model for probabilistic short-range precipitation nowcasting over the Italian domain at \SI{1}{km} spatial and 5 min temporal resolution. IRENE adopts an encoder--forecaster architecture built on multi-scale Convolutional Gated Recurrent Units (ConvGRUs), trained on the national radar composite produced by the Italian Civil Protection Department (DPC). An importance-sampling scheme focuses training on precipitation-relevant events, while the almost-fair Continuous Ranked Probability Score (afCRPS) is adopted as the primary probabilistic loss function. Two additional training configurations are proposed: an adversarial (GAN) variant, IRENE-GAN, designed to improve the spatial sharpness of the generated forecasts, and a spectrally constrained variant, IRENE-GAN-RAPSD, in which the adversarial objective is complemented by an explicit penalty on the radially averaged power spectral density. The three configurations are evaluated against the stochastic extrapolation method STEPS and the pre-trained deep learning model DGMR. All IRENE configurations attain a lower Continuous Ranked Probability Score than both benchmarks at every lead time and rank histograms closer to uniformity, indicating better probabilistic skill and ensemble calibration. In terms of ensemble-mean mean absolute error the advantage is confined to the first 90 min, beyond which the strongly damped DGMR fields and, to a lesser extent, STEPS become competitive. Spectral analysis shows that the adversarial training removes the progressive loss of small-scale variance exhibited by IRENE, at the cost of an excess of fine-scale power at long lead times that the spectral penalty only partially controls.

发表机构

• Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）

机构由 AI 辅助整理，请以论文原文为准。