|Title||Atmospheric motion vectors from model simulations. Part II: Interpretation as spatial and vertical averages of wind and role of clouds|
|Publication Type||Technical memorandum|
|Secondary Title||ECMWF Technical Memoranda|
|Authors||Hernandez-Carrascal, A, Bormann, N|
The main objective of this study is to improve the characterization of Atmospheric Motion Vectors (AMVs) and their errors to improve the use of AMVs in Numerical Weather Prediction (NWP). AMVs are estimates of atmospheric wind derived by tracking apparent motion across sequences of satellite images, and they tend to exhibit considerable systematic and random errors and geographically varying quality. These errors can arise in the AMV derivation or the interpretation of AMVs as single-level point observations of wind. An important difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. To overcome that difficulty, this study uses a simulation framework: geostationary imagery for Meteosat-8 is generated from a high resolution NWP model simulation (performed with the WRF regional model with a nominal horizontal resolution of 3 km), and AMVs are derived from sequences of these simulated images. The NWP model provides the "truth" with a sophisticated description of the atmosphere. The study considers infrared and water vapour AMVs from cloudy scenes. This is the second part of a two-part paper, focussing on observation operator aspects, i.e. on alternative interpretations of what AMV represent best. The key results are: 1) there is evidence that high level AMVs are more representative of the wind at a level within the cloud, rather than the cloud top; 2) interpreting the AMVs as vertical averages of wind can give some benefits, but these are relatively small compared to interpreting the AMVs as single-level wind estimates for a suitably-chosen level within the cloud; and 3) low-level AMVs seem to be more representative of a wind average over the cloud layer than of the wind at the base or the top of the cloud.