An Automatic Identification Algorithm for the Removal of Bright Band from Reflectivity of CINRAD/SA

An automatic identification algorithm for the removal of bright band from radar reflectivity data is introduced and tested by case analysis and pre-operational run. The algorithm has good results of removal of bright band based on the radar reflectivity data which have been transformed into a regular 3-dimensional Cartesian grid while configuration and running setup are relative simple. Firstly, the algorithm performs the recognition of vertical bright band profile. The algorithm considers each point independently, and attempts to find the evidence of a bright band by using a pattern recognition approach. Based on the typical reflectivity of the vertical profile attributes in the bright band deducted from statistics, a set of ideal templates of radar reflectivity vertical profiles is established, which can resemble the actual radar reflectivity characteristics in different bright band areas at the utmost. For each horizontal grid the algorithm attempts to couple the templates to the measured vertical profile of radar reflectivity. The couple function is obtained by computing the slope and the correlation of the least squares regression between the template and the measured profile. Secondly, the algorithm finds continuous regions of bright band. Regions of bright band may be further recognized by the horizontal uniformity of the signature. The algorithm attempts to use the goodness-of-fit and height-of-best-fit field computed above to gain further clues of a bright band. A grid kernel-based approach is used, in which a kernel is composed of 5×5 grids in the field. For the grids in the kernel, the algorithm counts the number of difference between contiguous goodness-of-fit values exceeding a given threshold or the difference in the height-of-best-fit values for contiguous points exceeding a given threshold. A high interested value is assigned to points with low numbers, since this indicates uniformity. So bright band areas are identified as those fields with high interested values. Finally, the algorithm removes the bright band signature. It removes the continuous regions of bright band by correcting the reflectivity within the bright band, which employs linear or non-linear interpolation methodology based on the measured reflectivity values in the profile above the band and the measured values below the band. Some thunderstorm cases analysis and pre-operational running tests suggest that the algorithm is able to successfully identify and remove most bright band regions, which influences quantitative precipitation estimation, based on CINRAD/SA radar reflectivity located at Tanggu of Tianjin. However, the algorithm has less effect on actual storms reflectivity data. Results of the analysis also demonstrate that the bright band regions usually appear at 2.5 km over Beijing and Tianjin in early summer. Because the algorithm is relatively easy to configure and run, it is appropriate to use in real-time or operational mode. Of course the algorithm has limitations and needs to be continuously improved for actual operational applications.