Journal Article

Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 2. Assimilation Experiments of GOES‐16 Satellite Derived Winds

Zhao, Juan, Gao, Jidong, Hu, Junjun, Jones, Thomas A.

2021

Overview

Building on the results from the observing system simulation experiments in Part I, this study investigates the impact of assimilating Geostationary Operational Environmental Satellite‐16 (GOES‐16) derived atmospheric motion vector (AMV) data on the convective scale numerical weather prediction (NWP) by using the National Severe Storms Laboratory (NSSL) three‐dimensional variational (3DVAR) data assimilation (DA) system. The benefit of the AMV DA for short‐term severe weather forecast is assessed with three high‐impact weather events that occurred in spring 2018 and 2019 over the Great Plains of the United States. The results show that the wind and equivalent potential temperature fields associated with the storm environment and the nearby ongoing convection are improved by the AMV DA, which yields better simulation of the boundaries and the subsequent forecasts of storm evolution. For the quasi‐linear or mesoscale convective system, the assimilation of AMVs has a positive impact on the 0–3 h forecasts of composite reflectivity and accumulated precipitation in terms of the shape, location, and magnitude. However, the AMV DA has difficulty in capturing the sharp moisture gradient associated with the dryline and mostly underpredicts the associated scattered storms. Plain Language Summary The high‐spatiotemporal‐resolution atmospheric motion vectors (AMVs) derived from newly launched Geostationary Operational Environmental Satellites‐16/17 (GOES‐16/17; also named GOES East/West) may have the potential to improve short‐range severe weather forecasts. However, this has not been extensively explored. In this study, a three‐dimensional variational data assimilation scheme developed at NOAA/National Severe Storms Laboratory and the Weather Research and Forecasting model are used to investigate the impact of assimilating GOES‐R derived AMVs on short‐range severe thunderstorm forecasts. It is demonstrated that, compared to the control experiment without assimilating any observations, the short‐range forecasts of the quasi‐linear or mesoscale convective systems in three severe weather events are all improved based on verification against radar reflectivity and precipitation observations when assimilating the AMV data. Key Points Geostationary Operational Environmental Satellite‐16 (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 on short‐range severe thunderstorm forecasts is evaluated The short‐range forecasts of three severe weather events are improved by AMV DA compared to the control experiment without any observations

• Publisher: John Wiley & Sons, Inc,American Geophysical Union (AGU)
• Subject: 3DVAR / Algorithms / Atmospheric motion / Atmospheric Motion Vectors / Convection / Data assimilation / Data collection / Equivalent potential temperature / Experiments / GOES‐16 / Mesoscale convective systems / Moisture gradient / Numerical Weather Prediction / Potential temperature / Quality control / Reflectance / Satellites / Severe storms / Severe weather / Simulation / Storm forecasting / Storms / Temperature distribution / Temperature fields / Vectors / Weather / Weather forecasting / Wind / Winds