ASSESSMENT OF RADAR-DERIVED TROPICAL CYCLONE STRUCTURE USING DIFFERENT QUALITY CONTROL METHODS

The Tail Doppler Radar (TDR) is a vertically-scanning radar found on the NOAAWP-3D and G-IV aircraft and is used for studying tropical cyclone structure and evolution. The quality control (QC) method applied to TDR data, NOAA-QC, has been shown to accurately identify non-meteorological data (NMD), but is aggressive at removing meteorological data (MD) especially in the lower and upper troposphere. The purpose of this study is to assess the benefits of reprocessing TDR data with a less aggressive QC method. We employ a recently-developed machine-learning quality control method, which has been shown to retain more MD than NOAA-QC, to reprocess a subset of cases. It is discovered that the machine-learning method is more accurate than NOAA-QC at retaining MD, especially within the lower and upper troposphere. Increased MD coverage with machine-learning is further demonstrated through increases in coverage within 3D wind analyses for each case.