基于确定性基础模型的轻量级概率降尺度方法

中文摘要 AI 辅助

气候数据降尺度是提高气候数据空间分辨率的任务，通常通过从粗分辨率的全球模型输出生成细分辨率的区域气候数据来实现。近期，在天气预报相关任务中的机器学习（ML）工作由于新设计的训练方法和架构组件而取得了显著改进，但这些改进尚未惠及降尺度任务。我们改编了其中两种方法，构建了一个基于改进的U-Net骨干网络的轻量级概率ML降尺度模型系列，并在CORDEX-ML-Bench基准套件上对三个地理区域（阿尔卑斯山、新西兰和南非）的日最高温度和降水进行了评估。我们发现，一种结合确定性预训练与概率微调的两阶段训练课程能很好地迁移到降尺度任务，在RMSE指标上超越了现有最先进水平。我们的工作推动了轻量级概率降尺度模型的发展，减少了当前计算强度与分布拟合之间的权衡。

英文摘要

Climate data downscaling is the task of increasing the spatial resolution of climate data, typically by generating fine-resolution regional climate data from coarse global model output. Recent machine learning (ML) work in the related task of weather forecasting has seen significant improvements due to newly devised training methods and architectural components, but these have not yet benefited downscaling. We adapt two of these methods to create a family of lightweight probabilistic ML downscaling models built on a modified U-Net backbone and evaluate them on the CORDEX-ML-Bench suite for daily maximum temperature and precipitation across three geographic regions: the Alps, New Zealand and South Africa. We find that a two-stage training curriculum, combining deterministic pretraining with probabilistic tuning, transfers well to downscaling, beating the state-of-the-art for RMSE. Our work provides an advancement towards lightweight, probabilistic downscaling models, reducing the current trade-off between computational intensity and distributional fit.

发表机构

• University of Glasgow（格拉斯哥大学）
• NORCE Research AS（挪威研究中心）
• Bjerknes Centre for Climate Research（皮耶克尼斯气候研究中心）

机构由 AI 辅助整理，请以论文原文为准。