Title: Model Training, Data Assimilation, and Forecast Experiments with a Hybrid Atmospheric Model that Incorporates Machine Learning Show Chinese title

Title: 基于融合机器学习的混合大气模型的模型训练、数据同化和预报实验

Abstract: The hybrid model combines the physics-based primitive-equations model SPEEDY with a machine learning-based (ML-based) model component, while ERA5 reanalyses provide the presumed true states of the atmosphere. Six-hourly simulated noisy observations are generated for a 30-year ML training period and a one-year testing period. These observations are assimilated with a Local Ensemble Transform Kalman Filter (LETKF), and a 10-day deterministic forecast is also started from each ensemble mean analysis of the testing period. In the first experiment, the physics-based model provides the background ensemble members and the 10-day deterministic forecasts. In the other three experiments, the hybrid model plays the same role as the physics-based model in the first experiment, but it is trained on a different data set in each experiment. These training data sets are analyses obtained by using the physics-based model (second experiment), the hybrid model of the previous experiment (third experiment), and for comparison, ERA5 reanalyses (fourth experiment). The results of the experiments show that hybridizing the model can substantially improve the accuracy of the analyses and forecasts. When the model is trained on ERA5 reanalyses, the biases of the analyses are negligible and the magnitude of the flow-dependent part of the analysis errors is greatly reduced. While the gains in analysis accuracy are distinctly more modest in the other two hybrid model experiments, the gains in forecast accuracy tend to be larger in those experiments after 1-3 forecast days. However, these extra gains of forecast accuracy are achieved, in part, by a modest gradual reduction of the spatial variability of the forecasts. Show Chinese abstract

Abstract: 混合模型将基于物理的原始方程模型SPEEDY与基于机器学习（ML-based）的模型组件相结合，而ERA5再分析数据提供了大气的假设真实状态。 每六小时生成用于30年机器学习训练期和一年测试期的模拟噪声观测数据。 这些观测数据通过局部集合变换卡尔曼滤波器（LETKF）进行同化，并且从测试期的每个集合均值分析开始进行10天的确定性预报。 在第一个实验中，基于物理的模型提供背景集合成员和10天的确定性预报。 在其他三个实验中，混合模型在角色上与第一个实验中的基于物理的模型相同，但在每个实验中它使用不同的数据集进行训练。 这些训练数据集是通过使用基于物理的模型（第二个实验）、前一个实验的混合模型（第三个实验）以及作为比较的ERA5再分析数据（第四个实验）获得的分析结果。 实验结果表明，混合模型可以显著提高分析和预报的准确性。 当模型在ERA5再分析数据上进行训练时，分析的偏差可以忽略不计，分析误差中依赖于流动的部分的幅度大大减少。 虽然在其他两个混合模型实验中分析精度的提升较为有限，但这些实验在1-3天预报后的预报精度提升往往更大。 然而，这些预报精度的额外提升部分是通过适度的逐渐减少预报的空间变化性来实现的。