Data-driven approaches for medium-range weather forecasting are recently shown extraordinarily promising for ensemble forecasting for their fast inference speed compared to traditional numerical weather prediction (NWP) models, but their forecast accuracy can hardly match the state-of-the-art operational ECMWF Integrated Forecasting System (IFS) model. Previous data-driven attempts achieve ensemble forecast using some simple perturbation methods, like initial condition perturbation and Monte Carlo dropout. However, they mostly suffer unsatisfactory ensemble performance, which is arguably attributed to the sub-optimal ways of applying perturbation. We propose a Swin Transformer-based Variational Recurrent Neural Network (SwinVRNN), which is a stochastic weather forecasting model combining a SwinRNN predictor with a perturbation module. SwinRNN is designed as a Swin Transformer-based recurrent neural network, which predicts future states deterministically. Furthermore, to model the stochasticity in prediction, we design a perturbation module following the Variational Auto-Encoder paradigm to learn multivariate Gaussian distributions of a time-variant stochastic latent variable from data. Ensemble forecasting can be easily achieved by perturbing the model features leveraging noise sampled from the learned distribution. We also compare four categories of perturbation methods for ensemble forecasting, i.e. fixed distribution perturbation, learned distribution perturbation, MC dropout, and multi model ensemble. Comparisons on WeatherBench dataset show the learned distribution perturbation method using our SwinVRNN model achieves superior forecast accuracy and reasonable ensemble spread due to joint optimization of the two targets. More notably, SwinVRNN surpasses operational IFS on surface variables of 2-m temperature and 6-hourly total precipitation at all lead times up to five days.
翻译:以数据驱动的中程天气预报方法最近显示,对于与传统数字天气预测模型相比快速发酵速度的混合式预报,数据驱动的中程天气预报方法最近表现出超乎寻常的乐观性能,与传统的数字天气预测(NWP)模型相比,其快速发酵速度的混合性预测。但其预测的准确性几乎无法与ESCMFFF 综合预报系统(IFS)模型相匹配。先前的数据驱动尝试使用一些简单的扰动方法(如初步状况扰动和蒙特卡洛辍学)实现共振性预测。然而,它们大多会遭遇不令人满意的混合性反应性能,这可以归因于应用的次最优化的天气预测方法。我们建议基于SwinRFlorturive的变速性变速性变速性预测系统(SwinRWorld)的快速变速性预测速度,我们设计了一个基于快速变速性变速性变速性预测2级的快速变速性变速性分布模式,我们用自动变速性变速性变速性预测2次的频率流流流流流流流数据流数据流数据流到多变式数据模型,我们从自动变现数据流流变现数据流变现数据流变现数据模型,从自动变现数据变现数据流变现数据流变现数据流变现数据变现数据模型学系统变现,从SOVDOVVDOVDODODFSODFSODSODSDSDSDSDSDSDSDSDSDSDSDSODSOT,从SOT,从S,从S,到透性系统,从S,到S,从S,从S,到OVDSOVDOVD,从S,从S,到OD,到OVD,从S,从S,从SOVVVVVVDOVDVDMVDOVDOVDFL,从S,到OVDOVDMS,从S,从S,到OVDMS,从S,从S,到OVDMDMDMS,从S,从S,到S,从S,到S,到O