Turbulence Regression
This addresses the challenge of turbulence prediction in meteorology and aviation, but appears incremental as it builds on existing Tucker decomposition and neural network methods.
The paper tackles the problem of accurately predicting low-altitude turbulence states using only wind profile radar data by introducing a NeuTucker decomposition model that discretizes continuous input data and constructs a four-dimensional Tucker interaction tensor. The model demonstrates superior performance in estimating missing observations in real datasets compared to various common regression models.
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.