Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity

Abstract

AbstractBased on the idealized supercell and real case studies in Part I and II, the purpose of this subsequent study is to further investigate the impact of assimilating Geostationary Operational Environmental Satellites‐16 (GOES‐16) derived atmospheric motion vectors (AMVs) in addition to WSR_88D Doppler radar observations on convective scale numerical weather prediction. Five high‐impact weather events that occurred in spring 2018 and 2019 are analyzed using the National Severe Storms Laboratory three‐dimensional variational data assimilation (DA) system. Four types of experiments are implemented and compared: (a) the control experiment (NoDA) without assimilating any observation, (b) the radar DA experiment (RAD), (c) the GOES‐16 AMV DA experiment (AMV), and (d) the experiment assimilating AMVs together with radar data (AMV_RAD). Score metrics aggregated over all cases indicate that AMV_RAD performs slightly better than RAD in 0–3 hr reflectivity and precipitation forecasts especially at higher thresholds, suggesting the added value of GOES‐16 AMVs on radar data. Detailed case examinations also show that AMV_RAD generally exhibits slightly more skillful storm forecast in terms of the areal coverage, storm mode, and storm orientations, owing to improvements in the analysis of boundary locations and localized enhanced divergence signatures. In spite of encouraging objective and subjective evaluation results, AMV_RAD has difficulty in adjusting the moisture gradient associated with dryline and tends to underpredict the associated weak discrete storms.