用神经张量分解提升低空风场湍流预测精度
Turbulence Regression
- 将连续风场数据离散化，构建四维张量捕捉时空交互
- 在真实数据缺失补全任务中优于多种传统回归模型
- 适合气象预报与航空安全领域研究人员参考
空气湍流是由气流速度、压力或方向剧烈变化引发的无序运动状态。多种复杂因素导致低空湍流结果高度复杂。在当前观测条件下，尤其是仅使用风廓线雷达数据时，传统方法难以准确预测湍流状态。为此，本文提出一种基于离散化数据的NeuTucker分解模型。该模型专为连续但稀疏的三维风场数据设计，利用张量神经网络构建低秩Tucker分解模型，以捕捉三维风场数据中的潜在交互。核心思想包括：1）将连续输入数据离散化，适配需离散输入的NeuTucF模型；2）构建四维张量表示不同高度与三维风速间的全部时空交互。在真实数据集缺失观测估计任务中，该离散化NeuTucF模型性能显著优于多种常见回归模型。
原文摘要 · Abstract (English)
Air turbulence refers to the disordered and irregular motion state generated by drastic changes in velocity, pressure, or direction during airflow. Various complex factors lead to intricate low-altitude turbulence outcomes. Under current observational conditions, especially when using only wind profile radar data, traditional methods struggle to accurately predict turbulence states. Therefore, this paper introduces a NeuTucker decomposition model utilizing discretized data. Designed for continuous yet sparse three-dimensional wind field data, it constructs a low-rank Tucker decomposition model based on a Tucker neural network to capture the latent interactions within the three-dimensional wind field data. Therefore, two core ideas are proposed here: 1) Discretizing continuous input data to adapt to models like NeuTucF that require discrete data inputs. 2) Constructing a four-dimensional Tucker interaction tensor to represent all possible spatio-temporal interactions among different elevations and three-dimensional wind speeds. In estimating missing observations in real datasets, this discretized NeuTucF model demonstrates superior performance compared to various common regression models.
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