  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.
