Turbulence Regression

TL;DR

This paper presents a novel Tucker neural network-based regression model for predicting air turbulence from sparse, discretized 3D wind data, outperforming traditional methods in accuracy.

Contribution

It introduces a discretized NeuTucker decomposition model that captures complex spatio-temporal interactions in wind data for turbulence prediction.

Findings

Superior performance in estimating missing data

Effective modeling of spatio-temporal wind interactions

Outperforms traditional regression models

Abstract

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.