Quality Assessment and Impact of High-Resolution GOES-16 AMVs into the GSI-EnKF-Based WoFS

Advanced high-resolution remote sensing observations of the atmospheric state are an indispensable component of the numerical weather prediction (NWP) system. The GOES-R series carries the Advanced Baseline Imager (ABI) instrument from which Atmospheric Motion Vectors (AMVs) can be derived. The AMVs from cloud and moisture features is one of the important and valuable data sources for the high-resolution limited area modeling and forecasting. The high-resolution Ensemble Kalman Filter (EnKF) with GSI data assimilation experiments were performed to assess the impact of GOES-16 AMVs product into the Warn-on-Forecast System (WoFS). The NOAA’s WoFS project runs at convection permitting scales (~3 km) was initiated with the goal of developing an ensemble-based data assimilation and forecasting system to generate probabilistic high impact weather forecasts. First part of the study investigates the quality of GOES-16 AMVs using a different statistical method. The initial quality assessment of the AMVs data indicates that only less than 10% of data are used in the GSI system for quality control and data thinning. The second part of the study focuses on the importance of these data in the WoFS analysis field and their impact on short-term 2-3 hr forecasts. Multi convective events during the year 2018 and 2019 are considered in this study to access the potential impacts of GOES-16 AMVs. In addition, the overview of the status of the operational assimilation of GOES-16 AMVs into the WoFS in the year 2019 will be discussed. The results show that the impact of assimilation of AMVs into the WoFS is positive in several cases.