注意力ResUNet小时降水后处理的方法变更

中文摘要 AI 辅助

本文是对先前发表的预印本的技术补充，该预印本描述了一种注意力残差U-Net，用于将The Weather Company的全球和区域大气预报（GRAF）模型的确定性预报后处理为概率性小时降水预报。本文记录了自发表以来该方法的变化。采用基于日历季节和预报提前期的特征级线性调制，为每个季节生成一个训练好的模型，取代了192个分别按月、按提前期训练的检查点。提前期从48小时延长至72小时。新增两个输入通道：逐像素的当地太阳时和静态的、逐月变化的降水气候态。在验证期间，计算Brier技能评分所依据的气候态参考现在增加了日变化维度，加上原有的月分辨率。比较了新训练与旧训练之间的Brier技能评分和可靠性。使用新训练生成的预报显示，当前训练相对于原始训练有适度且一致的改进。

英文摘要

This note is a technical companion to a previously published preprint describing an Attention Residual U-Net that postprocesses deterministic forecasts from The Weather Company's Global and Regional Atmospheric Forecast (GRAF) model into probabilistic hourly precipitation forecasts. It documents what has changed in that method since publication. Feature-wise Linear Modulation conditioning on calendar season and forecast lead time is used to produce a single trained model for each season, replacing 192 separately trained per-month, per-lead checkpoints. Lead time is extended from 48 to 72 h. Two new input channels are used, per-pixel local solar hour and a static, monthly-varying precipitation climatology. During verification, the climatological reference against which the Brier Skill Score is computed now has an added diurnal dimension, on top of the monthly resolution it already had. Brier Skill Score and reliability are compared between the new vs. the previous training. Forecasts generated with the new training show a modest, consistent improvement of the current training over the original.

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。