The data quality of the spectral (Doppler) moments (power, radial velocity and spectrum width) continues to be an on-going problem for the NEXRAD radar products. Data contaminants that are significant include so-called hard targets like ground clutter (both normal and anomalous propagation), birds, and airplanes. Even with clutter filtering, whether the legacy clutter filters or GMAP, Sigmet's spectral based clutter filter, clutter residue can still bias all moments. However, the new Open RDA system to be deployed on the WSR-88D fleet will allow much more flexibility for processing the so-called level 1, I&Q data. In particular, spectral-domain processing will become a viable method for calculating the moments, thus opening the door to advanced techniques, such as the NEXRAD Spectral Processing Algorithm (NSPA), described in this paper, that can improve moment estimates by isolating weather signals from contaminants, like clutter residue, airplanes, and isolated birds. Improvements to power and velocity estimates would be realized when, for example, weather and strong ground clutter echoes compete. Spectrum width estimates would be improved almost universally by using spectral processing rather than the current pulse pair estimator.

NSPA, like its predecessors NCAR Improved Moment Algorithm (NIMA) and NCAR Enhanced Spectra Processing Algorithm (NESPA), use spectral information along a radial, to determine which spectral features weather rather than contaminants. When contaminants are identified, the algorithm attempts to calculate the spectral moments of only the weather, excluding the contaminants. In this paper, the NSPA algorithm will be described and its performance evaluated.