发表机构
The Weather Company（天气公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出注意力残差U-Net方法，结合NWP数据与地形、湿度信息，生成高分辨率校准概率小时降水量，在地形复杂区域的概率估计上表现优于气候学及简单参考方法。
AI 中文摘要
本文描述了一种用于概率定量降水预报（PQPF）的“注意力残差U-Net”方法，该方法预测无降水的小时概率以及由两个Gamma分布加权混合构成的正降水量分布。该神经网络的训练数据来自：The Weather Company的对流允许GRAF（全球高分辨率大气预报）模型输出的数值天气预报（NWP）小时降水量块，以及美国国家海洋和大气管理局（NOAA）的GFS（全球预报系统）提供的地形信息和柱平均相对湿度；目标数据为NOAA的MRMS（多雷达多传感器）经雨量计校正、质量控制的雷达数据，采样后与GRAF数据采用相同网格。该网络输出每个模型网格点的分布参数，训练采用负对数似然作为恰当评分规则，使用气候学初始化以实现稳定收敛；推理过程为对美国本土（CONUS）区域执行单次前向传播，采用边缘复制填充以满足网络的空间可分性要求。后续预报在空间上细节丰富、高度可靠，相较于气候学方法和更简单的参考预报方法具有技巧性，该方法对地形变化大的区域的概率估计特别有用。
英文摘要
An ``Attention Residual U-Net'' method is described for probabilistic quantitative precipitation forecasting (PQPF) that predicts the hourly probability of no precipitation plus the distribution of positive precipitation from a weighted mixture of two Gamma distributions. The neural network is trained on patches of numerical weather prediction (NWP) hourly precipitation from The Weather Company's convection-permitting GRAF (Global high-Resolution Atmospheric Forecasting) model along with terrain information and column-average relative humidity from the National Oceanic and Atmospheric Administration's (NOAA's) Global Forecast System (GFS). The target data are NOAA's Multi-Radar, Multi-Sensor (MRMS) gauge-corrected, quality controlled radar data sampled to the same grid as the GRAF data. The network outputs distributional parameters for each model grid point. Training uses negative log-likelihood as a proper scoring rule, with climatological initialization for stable convergence. Inference is performed as a single forward pass over the contiguous United States (CONUS) domain, with edge-replication padding to satisfy the network's spatial-divisibility requirement. The subsequent forecasts are spatially detailed, highly reliable, and skillful with respect to climatology and a simpler reference forecast method. The method is particularly useful for estimating probabilities in regions with large terrain variation.
