Real-time implementation of refractivity retrieval: Partnership Between the University of Oklahoma, National Severe Storms Laboratory, and the Radar Operations Center

High-resolution, near-surface refractivity measurements have the potential of becoming an important tool for operational forecasting and general scientific studies. Access to measured refractivity fields with high spatial and temporal resolution near the surface opens a new paradigm for understanding the convective processes within the boundary layer. It has been shown via advanced physical models that surface refractivity plays an important role in convective processes and, therefore, is expected to be valuable for forecasting the initiation and intensity of convective precipitation. For this project, the refractivity field is retrieved remotely using S-band radars by measuring the returned phase from ground clutter. Pioneering work of Fabry et. al. [J. Atmos. Oceanic Technol., 14, 978-987, 1997] has demonstrated the usefulness of this technique. By adopting this refractivity retrieval concept, an independent real-time software platform has been developed. The software was written with a modular design for portability and will be tested during the spring 2007 storm season on two radars in Oklahoma. Both the National Weather Radar Testbed (Phased Array), maintained by the National Severe Storm Laboratory (NSSL), and the WSR-88D weather radar near Oklahoma City (KTLX), supported by the Radar Operations Center (ROC), will be used for this study. Using the raw Level-I time series data from the radars, the modular software platform will be used to process the data in real-time for refractivity fields, which will be sent to the Norman Weather Forecast Office (WFO). The refractivity fields will be displayed through the Warning Decision Support System - Integrated Information (WDSS-II) for evaluation. Working closely with the WFO forecasters, qualitative assessment procedures will be followed to evaluate the usefulness of the refractivity fields for operational forecasting.