Kaijun Ren

Wuxin Wang, Weicheng Ni, Lilan Huang et al.

Recent advancements in Artificial Intelligence (AI) demonstrate significant potential to revolutionize weather forecasting. However, most AI-driven models rely on Numerical Weather Prediction (NWP) systems for initial condition preparation, which often consumes hours on supercomputers. Here we introduce XiChen, the first observation-scalable fully AI-driven global weather forecasting system, whose entire pipeline, from Data Assimilation (DA) to medium-range forecasting, can be accomplished within only 17 seconds. XiChen is built upon a foundation model that is pre-trained for weather forecasting. Meanwhile, this model is subsequently fine-tuned to serve as both observation operators and DA models, thereby scalably assimilating conventional and raw satellite observations. Furthermore, the integration of four-dimensional variational knowledge ensures that XiChen's DA and medium-range forecasting accuracy rivals that of operational NWP systems, amazingly achieving a skillful forecasting lead time exceeding 8.25 days. These findings demonstrate that XiChen holds strong potential toward fully AI-driven weather forecasting independent of NWP systems.

Yifan Hu, Fukang Yin, Weimin Zhang et al.

Long-term stability stands as a crucial requirement in data-driven medium-range global weather forecasting. Spectral bias is recognized as the primary contributor to instabilities, as data-driven methods difficult to learn small-scale dynamics. In this paper, we reveal that the universal mechanism for these instabilities is not only related to spectral bias but also to distortions brought by processing spherical data using conventional convolution. These distortions lead to a rapid amplification of errors over successive long-term iterations, resulting in a significant decline in forecast accuracy. To address this issue, a universal neural operator called the Spherical Harmonic Neural Operator (SHNO) is introduced to improve long-term iterative forecasts. SHNO uses the spherical harmonic basis to mitigate distortions for spherical data and uses gated residual spectral attention (GRSA) to correct spectral bias caused by spurious correlations across different scales. The effectiveness and merit of the proposed method have been validated through its application for spherical Shallow Water Equations (SWEs) and medium-range global weather forecasting. Our findings highlight the benefits and potential of SHNO to improve the accuracy of long-term prediction.

Xiang Wang, Xiaoyong Li, Junxing Zhu et al.

Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality. High-dimensional data are usually in a coherent structure and make the data in relatively small true degrees of freedom. There are global and local dimensionality reduction methods to alleviate the problem. Most of existing methods for local dimensionality reduction obtain an embedding with the eigenvalue or singular value decomposition, where the computational complexities are very high for a large amount of data. Here we propose a novel local nonlinear approach named Vec2vec for general purpose dimensionality reduction, which generalizes recent advancements in embedding representation learning of words to dimensionality reduction of matrices. It obtains the nonlinear embedding using a neural network with only one hidden layer to reduce the computational complexity. To train the neural network, we build the neighborhood similarity graph of a matrix and define the context of data points by exploiting the random walk properties. Experiments demenstrate that Vec2vec is more efficient than several state-of-the-art local dimensionality reduction methods in a large number of high-dimensional data. Extensive experiments of data classification and clustering on eight real datasets show that Vec2vec is better than several classical dimensionality reduction methods in the statistical hypothesis test, and it is competitive with recently developed state-of-the-art UMAP.