363224 The Multi-Year Reanalysis of Remotely Sensed Storms: Past, Present, and Future

Tuesday, 14 January 2020
Hall B1 (Boston Convention and Exhibition Center)
Skylar S. Williams, OU/CIMMS and NOAA/OAR/NSSL, Norman, OK; and K. L. Ortega, A. E. Reinhart, and T. M. Smith

The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) is a historical radar database with all available WSR-88D data processed using the Multi-Radar Multi-Sensor (MRMS) framework to blend multiple radars with near storm environmental data. This blending technique creates a three-dimensional reflectivity volume over the CONUS that is used to create other radar derived products such as Maximum Estimated Size of Hail (MESH), Vertically Integrated Liquid (VIL), and Echo Top Altitudes. Additionally, two-layer merged maximal azimuthal shear products are included in the MYRORSS database. MYRORSS began processing in 2012 but has recently been completed for the years 1998 through 2011.

The next goal of MYRORSS is to complete data processing for 2012 through present. The workflow of data processing needed to be redesigned in order to move forward with processing and address new challenges because of enhancements to the WSR-88D system, such as polarimetric radar upgrades and implementation of the Supplemental Adaptive Intra-Volume Low-Level Scan (SAILS) technique. Previously, data processing was completed on a distributed computer cluster system within NSSL. Now, data is being processed at the University of Oklahoma’s supercomputing center for increased computing resources and allow multiple years of MYRORSS data processing to occur simultaneously with the goal of quickly processing 2012 through 2019 over the next year and being able to create the reanalysis in near real-time.

This presentation will discuss the processing framework on the distributed and supercomputing systems, challenges of dual-pol era data, and the manual quality control the dataset undergoes. In addition, ongoing research using MYRORSS will be discussed including applying machine learning techniques developed on smaller datasets and how to apply them to create real-time algorithms.

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