Handout (431.0 kB)
To overcome these difficulties and make a good estimation of the weather radar spectrum, particularly in the ARAMIS context, we have developed a new method based on an optimization of the radar transmission scheme to reach a large number of points of the autocorrelation function R(τ). Basically, a dual PRT scheme gives 3 points of R(τ) for time lags 0,T1 and T2 ; a triple PRT scheme gives 4 points of R(τ) for time lags 0,T1, T2 and T3. A multiple nPRTs scheme gives (n+1) points of R(τ) for time lags 0,T1, T2 Tn. Additional points can also be obtained for combined lags (T1+T2, T2+T3, ).
Choosing Ti+1 = (Ti + δT), the function R(τ) is available over the interval [T1 , Tn] with a uniform sampling time δT. R(τ) can be forced to zero for the other time lags and the power spectrum simply recovered using classical FFT algorithm within an extended Nyquist interval of λ/(2 δT). This power spectrum is convolved by the sinc function which is the power spectrum of the time window (Tn T1).
After theoretical developments, we give in this paper simulation results showing the advantage of the new method with respect to the Sachidananda and Zrnic method in the Aramis network context. We then present an experimental validation campaign with an operational radar of the network, showing multiple PRTs spectra from ground echoes, and rain echoes. We finally conclude by operational recomandations.