Atmospheric motion vectors from model simulations. Part II: Interpretation as spatial and vertical averages of wind and role of clouds

Abstract

This is the second part of a two-part paper whose main objective is to improve the characterization of atmospheric motion vectors (AMVs) and their errors to guide developments in the use of AMVs in numerical weather prediction (NWP). AMVs tend to exhibit considerable systematic and random errors. These errors can arise in the AMV derivation or the interpretation of AMVs as single-level point estimates of wind. An important difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. The study uses instead a simulation framework: geostationary imagery for Meteorological Satellite-8 (Meteosat-8) is generated from a high-resolution simulation with the Weather Research and Forecasting regional model, and AMVs are derived from sequences of these simulated images. The NWP model provides the “truth” with a sophisticated description of the atmosphere. This second part focuses on alternative interpretations of AMVs. The key results are 1) that interpreting the AMVs as vertical and horizontal averages of wind can give some benefits over the traditional single-level interpretation (improvements in RMSVD of 5% for high-level AMVs and 20% for low-level AMVs) and 2) that there is evidence that AMVs are more representative of either a wind average over the model cloud layer or wind at a representative level within the cloud layer than of wind at the model cloud top or cloud base.