Impact of Assimilating Refractivity Measurements from a Network of S-band and X-band Radars on the Forecast of Convective Initiation using the ARPS 3DVAR System

Near-surface atmospheric refractivity is calculated from a radar using phase change measurements between any two ground clutter targets aligned along the radar beams. These refractivity observations are most sensitive to atmospheric moisture content and, with the spacing of good ground targets determining the data resolution, can provide high-resolution information on the near-surface moisture field. Due to fine-scale structures of the boundary layer, the near-surface moisture field has high spatial and temporal variability. The analysis and prediction of convective-scale weather is known to be very sensitive to these fine-scale structures in low-level moisture, which can affect the exact timing and location of convective initiation within a numerical weather prediction (NWP) model. Thus assimilation of radar-derived refractivity data into an NWP model is expected to improve convective initiation (CI) and quantitative precipitation forecasting.

The Advanced Regional Prediction System (ARPS) ensemble Kalman filter (EnKF) system is enhanced to include the analysis of radar-derived refractivity measurements. Multiple experiments were performed for a few select cases of CI ahead of a dryline from years 2009 and 2010 to assess the impact of assimilating these observations into an NWP model. First, an Observation System Simulation Experiment (OSSE) using simulated refractivity data was done to test the sensitivity of the analysis and short-term forecast to observational error. Then, experiments were performed assimilating real refractivity data collected by the Oklahoma City (KTLX) and Fredrick (KFDR), Oklahoma S-band WSR-88D radars, and the X-band radars of the CASA IP1 testbed located between Oklahoma City and Fredrick radars. Because refractivity data . For both real and simulated data experiments, the analyses incorporate other available surface and upper-air data, including the Oklahoma Mesonet, and use NWP model gridded analyses as the background fields. Assimilation of mesonet data offers an important point of comparison in assessing the impact of assimilating refractivity data on CI prediction. The processed refractivity data, having spatial resolutions of around 2 to 4 kilometers, should show moderate improvements in the timing and location of CI and subsequent storm evolution prediction when compared to the mesonet.