99 A Reflectivity Climatology Study of the Contiguous United States Using the Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS)

Monday, 23 January 2017
4E (Washington State Convention Center )
Brandon R. Smith, OU/CIMMS and NOAA/OAR/NSSL, Norman, OK; and K. L. Ortega, A. E. Reinhart, M. C. Mahalik, and T. M. Smith

The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) is a project tasked with producing a robust dataset of quality-controlled, radar-derived products generated for the contiguous United States (CONUS) using data from the WSR-88D radar network.  MYRORSS uses the multi-radar, multi-sensor (MRMS) framework to blend data from individual radars  onto a 3D latitude-longitude-height grid, producing quasi-uniform coverage across the CONUS. This unique dataset allows for the creation of climatologies for various radar-derived variables on large temporal and spatial scales that previously haven’t been possible. 

This study investigates the climatology of the CONUS quality-controlled merged reflectivity field that spans the the entire MYRORSS database from 2000 - 2011.  The 3D merged reflectivity grid will be used to evaluate temporal and geographical distributions of reflectivity at different altitudes, including isothermal reflectivities and vertical composites.  The coverage of the individual radars within the 3D grid and the 3D grid itself will also be explored.  In addition, the frequencies of reflectivity values within different reflectivity products will be computed.  These frequencies will be useful in not only developing climatologies of reflectivity, but also evaluating the quality of the vertical sampling within the 3D grid (e.g. a frequency of 50 dBZ at the top of the 3D grid (20 km) can help describe where very tall convection, as well as vertical sampling insufficiencies, occur).  The low-level sampling of the 3D grid will also be evaluated, showing the effects of near surface features (e.g. ground clutter and wind turbines) as well as the lack of WSR-88D data coverage in certain regions of the CONUS.  All together, these findings provide a wealth of new information regarding reflectivity-based radar data products that can be further applied to other fields of meteorological research.  Some of these applications include merging this dataset with other research-quality datasets such as lightning, echo tops, and hail.

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