DUNE：一种基于机器学习深度UNet++的月度、季节和年度气候预测集成方法
DUNE: A Machine Learning Deep UNet++ based Ensemble Approach to Monthly, Seasonal and Annual Climate Forecasting
AI总结：
本研究提出基于深度UNet++的DUNE集成气候预测架构，利用ERA5数据训练，可快速生成高分辨率的多尺度全球温度预测，性能优于传统方法及部分AI模型，可应用于集成数据同化。
摘要：
借助基于高分辨率再分析的ERA5月度平均大气与气候场长期数据记录近期的可获取性，深度学习架构为次季节至季节（S2S）及年度平均预测提供了基于物理的每日数值天气预报之外的替代方案。本文提出了一种新颖的基于深度UNet++的集成（DUNE）神经架构，采用带残差块的多编码器-解码器结构。当以上一个月或上一年的数据初始化时，该架构生成了首个基于AI的全球月度、季节或年度平均2米气温（T2m）和海表温度（SST）预测。研究使用40年间每个月的ERA5月平均数据作为输入，包括陆地上的T2m、海洋上的SST以及大气顶太阳辐射，用以训练模型。验证预测在额外的两年中开展，随后进行五年的预测评估以考虑自然年际变率。经AI训练的推理预测权重可在数秒内生成预测，从而支持集成季节预测。本文给出了全球及特定区域的均方根误差（RMSE）、异常相关系数（ACC）和Heidke技巧评分（HSS）统计结果。这些预测在所有领域的表现均优于持续性预测、气候学预测和多元线性回归。DUNE预测在美国地区的统计准确率与NOAA的业务化月度和季节概率展望预测相当，但分辨率显著更高。与其他近期基于AI的每日预测相比，DUNE预测的RMSE和ACC误差统计也表现出更优的性能。DUNE模型应用于集成数据同化循环时，其预测准确率与单个高分辨率模型相当，有望无需在 extrapolated 数据集上重新训练。
英文摘要：
Capitalizing on the recent availability of ERA5 monthly averaged long-term data records of mean atmospheric and climate fields based on high-resolution reanalysis, deep-learning architectures offer an alternative to physics-based daily numerical weather predictions for subseasonal to seasonal (S2S) and annual means. A novel Deep UNet++-based Ensemble (DUNE) neural architecture is introduced, employing multi-encoder-decoder structures with residual blocks. When initialized from a prior month or year, this architecture produced the first AI-based global monthly, seasonal, or annual mean forecast of 2-meter temperatures (T2m) and sea surface temperatures (SST). ERA5 monthly mean data is used as input for T2m over land, SST over oceans, and solar radiation at the top of the atmosphere for each month of 40 years to train the model. Validation forecasts are performed for an additional two years, followed by five years of forecast evaluations to account for natural annual variability. AI-trained inference forecast weights generate forecasts in seconds, enabling ensemble seasonal forecasts. Root Mean Squared Error (RMSE), Anomaly Correlation Coefficient (ACC), and Heidke Skill Score (HSS) statistics are presented globally and over specific regions. These forecasts outperform persistence, climatology, and multiple linear regression for all domains. DUNE forecasts demonstrate comparable statistical accuracy to NOAA's operational monthly and seasonal probabilistic outlook forecasts over the US but at significantly higher resolutions. RMSE and ACC error statistics for other recent AI-based daily forecasts also show superior performance for DUNE-based forecasts. The DUNE model's application to an ensemble data assimilation cycle shows comparable forecast accuracy with a single high-resolution model, potentially eliminating the need for retraining on extrapolated datasets.
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。
