A novel de-aliasing algorithm for Doppler radar velocity data has been developed at SMHI. It is based on a linear wind model in which the radial wind speed can be expressed as a function of radar range, azimuth and elevation angle. Assuming that the elevation angle and the distance to the radar are constant, each observation at a given azimuth angle is assigned a radial velocity. Unfortunately, the resulting curve could have discontinuities due to aliasing problems. To avoid this problem, we map the measurements onto the surface of a torus. The result is a continuous parametric curve with a tangent vector that can be calculated and used to arrive at a first guess for the de-aliased wind speed. This is used to de-alias the observed radial wind velocities.

For a case study, we generated de-aliased wind profiles using single-PRF (Pulse Repetition Frequency) data from the Finnish radar network based on the VVP (Volume Velocity Processing) method. Comparisons with profiles produced by commercial radar software as well as NWP (Numerical Weather Prediction) model simulations will be presented at the conference. However, this is just a primary step to check the quality of the new de-aliasing technique. Since commercial algorithms provide only one- or two-dimensional wind products, we will use our new method to correct volume radar data sets as well. These can be applied to variational assimilation schemes through the generation of so-called super-observations. A super-observation is an intelligently generalized observation created through smoothing in space, based on high resolution data. At SMHI, a method for generation of radial wind super-observations through horizontal averaging in polar space of the raw polar volume data is already implemented. The radial wind super-observations have been used for the development of a Doppler data assimilation system for HIRLAM (High Resolution Limited Area Model). Their use is expected to improve with the introduction of the proposed de-aliasing method.