Kolmogorov Arnold 神经插值器用于从实地观测降尺度和校正气象场
Kolmogorov Arnold Neural Interpolator for Downscaling and Correcting Meteorological Fields from In-Situ Observations
AI总结：
针对网格化气象数据与站点观测间的系统性偏差，提出KANI框架，利用Kolmogorov Arnold定理实现连续神经表示，结合稀疏观测校正偏差，并实现零样本降尺度，在温度与风速上分别提升40.28%和67.41%。
摘要：
在站点位置获取准确的天气预报是一个关键挑战，这源于多尺度、连续的大气特征与其离散的网格化表示之间的不匹配所产生的系统性偏差。以往的工作主要集中于对网格化气象数据进行建模，本质上忽略了大气状态的离网格、连续特性，使得这些偏差未能得到解决。为了解决这一问题，我们提出了Kolmogorov Arnold 神经插值器（KANI），这是一种新颖的框架，将气象场表示重新定义为源自离散网格的连续神经函数。基于Kolmogorov Arnold定理，KANI捕捉了大气状态的内在连续性，并利用稀疏的实地观测来系统地校正这些偏差。此外，KANI引入了一种创新的零样本降尺度能力，由高分辨率地形纹理引导，无需高分辨率气象场进行监督。在美国大陆三个子区域的实验结果表明，KANI在温度上实现了40.28%的精度提升，在风速上实现了67.41%的精度提升，凸显了其相对于传统插值方法的显著改进。这使得通过神经网络对气象变量进行连续神经表示成为可能，超越了传统基于网格表示的局限性。
英文摘要：
Obtaining accurate weather forecasts at station locations is a critical challenge due to systematic biases arising from the mismatch between multi-scale, continuous atmospheric characteristic and their discrete, gridded representations. Previous works have primarily focused on modeling gridded meteorological data, inherently neglecting the off-grid, continuous nature of atmospheric states and leaving such biases unresolved. To address this, we propose the Kolmogorov Arnold Neural Interpolator (KANI), a novel framework that redefines meteorological field representation as continuous neural functions derived from discretized grids. Grounded in the Kolmogorov Arnold theorem, KANI captures the inherent continuity of atmospheric states and leverages sparse in-situ observations to correct these biases systematically. Furthermore, KANI introduces an innovative zero-shot downscaling capability, guided by high-resolution topographic textures without requiring high-resolution meteorological fields for supervision. Experimental results across three sub-regions of the continental United States indicate that KANI achieves an accuracy improvement of 40.28% for temperature and 67.41% for wind speed, highlighting its significant improvement over traditional interpolation methods. This enables continuous neural representation of meteorological variables through neural networks, transcending the limitations of conventional grid-based representations.
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