Rapid Scale Wind Profiling with Autoregressive Modeling and L-Band Doppler Radar

TL;DR

This paper introduces an autoregressive modeling approach combined with the Maximum Entropy Method to rapidly and accurately estimate atmospheric wind profiles using L-band Radar Wind Profilers with very short integration times, validated through synthetic and real data.

Contribution

The study presents a novel AR-MEM method that significantly reduces integration time for wind profiling, outperforming classical spectral methods in short sample scenarios.

Findings

Reliable wind estimates with 2.5 s integration time

AR-MEM outperforms classical spectral approaches

Improved mitigation of uncooperative flyers

Abstract

Radar Wind Profilers (RWP) are well-established instruments for the probing of the atmospheric boundary layer, with the immense advantage of long-range and all-weather operation capability. One of their main limitations, however, is a relatively long integration time compared to other instruments such as lidars. In the context of L-band RWP we show that the use of Autoregressive (AR) modeling for the antenna signals combined with the Maximum Entropy Method (MEM) allows for a correct estimation of radial wind velocity profiles even with very short time samples. A systematical analysis of performance is made with the help of synthetic data. These numerical results are further confirmed by an experimental dataset acquired near the landing runways of Paris Charles de Gaulle (CDG) Airport, France, and validated using a colocated optical lidar at the Aerological station of Payerne, Switzerland. It is found that the AR-MEM approach can successfully derive reliable wind estimates using integration times as short as 2.5 s where the classical spectral approach can barely provide any measurement. In addition, the technique helps mitigating the impact of uncooperative flyers in the processing of the atmospheric echo, leading to a more successful application of the wind estimation algorithm.