Gap-filling in 3D radar mosaic analysis using vertical profile of reflectivity

The 3-D radar reflectivity data has become increasingly important for use in data assimilation towards convective scale numerical weather prediction as well as next generation precipitation estimation. The reflectivity data are typically objectively analyzed onto 3-D Cartesian grid prior to being assimilated into the models. Due to the earth's curvature and positive scan elevation angles, the height of the lowest radar beam increases as range increases from the radar location. As a result, data voids exist below the lowest radar beams when volume scans of radar data are transformed onto a 3-D Cartesian grid. These data voids result in discontinuities in reflectivity analysis fields and pose a problem for accurate depiction of atmospheric processes for data assimilation. In the current study, 3-D reflectivity structure of various storm types and their associated vertical profiles of reflectivity (VPR) are investigated using volume scan radar data across different seasons and geographical regions. The VPRs are derived using reflectivity observations at the close ranges to obtain accurate depiction of the vertical structure of weather systems. Then at the far ranges, the VPR are used to extrapolate reflectivity from the lowest radar tilt down to the surface. The extrapolation effectively fills in the gaps at lower levels in the 3D reflectivity grid. Preliminary results from a convective storm system and a wintertime stratiform precipitation system will be shown at the conference.