Abstract
Accurate forecasting of three-dimensional (3D) cloud fields is important for atmospheric analysis and short-range numerical weather prediction, yet it remains challenging because cloud evolution involves cross-layer interactions, nonlocal dependencies, and multiscale spatiotemporal dynamics. Existing spatiotemporal prediction models based on convolutions, recurrence, or attention often rely on locality-biased representations and therefore struggle to preserve fine cloud structures in volumetric forecasting tasks. To address this issue, we propose QENO, a hybrid quantum-inspired spatiotemporal forecasting framework for 3D cloud fields. The proposed architecture consists of four components: a classical spatiotemporal encoder for compact latent representation, a topology-aware quantum enhancement block for modeling nonlocal couplings in latent space, a dynamic fusion temporal unit for integrating measurement-derived quantum features with recurrent memory, and a decoder for reconstructing future cloud volumes. Experiments on CMA-MESO 3D cloud fields show that QENO consistently outperforms representative baselines, including ConvLSTM, PredRNN++, Earthformer, TAU, and SimVP variants, in terms of MSE, MAE, RMSE, SSIM, and threshold-based detection metrics. In particular, QENO achieves an MSE of 0.2038, an RMSE of 0.4514, and an SSIM of 0.6291, while also maintaining a compact parameter budget. These results indicate that topology-aware hybrid quantum-classical feature modeling is a promising direction for 3D cloud structure forecasting and atmospheric Earth observation data analysis.
Abstract (translated)
三维（3D）云场的精确预报对大气分析和短临数值天气预报至关重要，然而由于云演变涉及跨层相互作用、非局部依赖关系以及多尺度时空动力学，该任务仍具有挑战性。现有基于卷积、循环或注意力机制的时空预测模型通常依赖局部偏置表示，因此在体量预测任务中难以保持精细的云结构。针对此问题，我们提出QENO——一种混合量子启发的3D云场时空预测框架。该架构包含四个组件：用于紧凑潜在表示的经典时空编码器、用于建模潜在空间中非局部耦合的拓扑感知量子增强块、用于融合测量衍生量子特征与循环记忆的动态融合时序单元，以及用于重建未来云体体积的解码器。在CMA-MESO 3D云场数据集上的实验表明，QENO在MSE、MAE、RMSE、SSIM及基于阈值的检测指标上持续优于代表性基线模型，包括ConvLSTM、PredRNN++、Earthformer、TAU及SimVP变体。尤其值得注意的是，QENO实现了0.2038的MSE、0.4514的RMSE和0.6291的SSIM，同时保持紧凑的参数规模。这些结果表明，拓扑感知的混合量子-经典特征建模是3D云结构预报及大气地球观测数据分析的一个有前景的方向。
URL
https://arxiv.org/abs/2603.29407
