The Atmospheric motion vectors (AMVs) represent horizontal wind derived by tracking the cloud or water vapor features on successive satellite images. The launch of the Geostationary Operational Environmental Satellite-R Series (GOES-R), including GOES-16 (GOES-East) and GOES-17 (GOES-West), significantly enhanced data volume and geographic coverage over the contiguous U.S. and adjacent oceans. AMVs from GOES-16/17 products can augment wind data in data-sparse areas like oceanic atmospheric rivers (ARs). However, AMVs exhibit biases and uncertainties, especially due to height assignment issues, and there are fewer conventional data (e.g., radiosondes) to calibrate GOES-17 over oceans. The AR Reconnaissance (AR Recon) samples ARs to improve forecast skill over the U.S. West and provides a great opportunity to validate GOES-16/17 AMVs. This study quantifies biases and uncertainties in GOES-16/17 AMVs in the Northeast Pacific using dropsondes from AR Recon and assesses model analyses and background from the GFS at National Centers for Environmental Prediction (NCEP). Results for four representative AR cases show that GOES-16/17 AMVs improved wind data distribution, particularly in the upper and lower troposphere. A comparison with dropsondes reveals negative biases of AMVs in both wind components, with a slow wind speed bias of -0.7 m/s, particularly in upper levels. The uncertainty for AMVs is estimated at 5-6 m/s. Validation of GFS model background shows small biases, with RMSD of 3.2 m/s for dropsondes and 2.1 m/s for AMVs. Data assimilation reduces RMSD, but biases in operational AMVs need further attention, as they are a dominant wind data source in NWP models.
