The Spatial Structure of Observation Errors in Atmospheric Motion Vectors from Geostationary Satellite Data

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Ensemble forecasting (2008, background): “Quantitative estimates of observation error correlations have been obtained for indirect wind observations obtained from feature tracking in satellite imagery (atmospheric motion vectors, [53]).”
Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution (2007, background): “According to Bormann et al. (2003), observational errors assigned to AMVs at different NWP centers vary by more than a factor of 2.”
Stochastic parametrization and model uncertainty (2009, background): “For cloud-track wind observations, perturbations were horizontally correlated (Bormann et al., 2003).”
On the interaction of observation and prior error correlations in data assimilation (2018, unknown): “…different from those resolved by the model (e.g. Doppler radial winds: Waller et al. , 2016c) and high-level observation products that go through a large amount of preprocessing (e.g. atmospheric motion vectors (AMVs) derived from satellite radiances: Bormann et al. , 2003; Cordoba et al. , 2017).”
Estimates of spatial and interchannel observation‐error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data (2010, methodology): “A variant of the method has been used to estimate spatial-error correlations in atmospheric motion vectors (AMVs) from differences between AMVs and radiosondes (Bormann et al., 2003).”