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Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity
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Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity
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Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity
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Journal Article
Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity
2022
Overview
Based 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. Plain Language Summary With the launch of Geostationary Operational Environmental Satellites‐16 (GOES‐16) in November 2016, the impact of its derived high‐spatiotemporal‐resolution atmospheric motion vectors (AMVs) product in convective‐scale numerical weather prediction has not been extensively explored. In this study, the GOES‐16 AMVs together with WSR‐88D Doppler radar observations are effectively assimilated by a three‐dimensional variational data assimilation scheme developed at NOAA/National Severe Storms Laboratory. Both subjective and objective assessment results for five severe weather events suggest the slightly added forecast skill of GOES‐16 AMVs on conventional radar data for 0–3 hr reflectivity and precipitation forecasts. Key Points GOES‐R derived atmospheric motion vector (AMV) product has the potential to benefit convective scale data assimilation and forecasts The impact of assimilating GOES‐R derived AMVs together with NEXRAD observations on short‐range severe storm forecasts is evaluated The verification results for five severe weather events suggest the added forecast skill of GOES‐16 AMVs over conventional radar data
