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.

Diagnosing atmospheric motion vector observation errors for an operational high‐resolution data assimilation system

“…It has been shown that, when the height‐assignment error is small, the tracking error, estimated using observation‐minus‐background statistics, from the ECMWF and Met Office systems ranges from 2–3 m s −1 (Lean et al ). The height‐assignment error has been estimated by Folger and Weissmann (), Hernandez‐Carrascal and Bormann (), Lean et al () and Salonen et al () using the technique of best‐fit statistics . The errors have been quantified within a range of 50–150 hPa.…”

Uncertainty Quantification for Atmospheric Motion Vectors with Machine Learning

“…The realism and robustness of the resulting uncertainty estimates depend on the realism and representativeness of the reference dataset. This work builds upon the work of Bormann et al (2014) and Hernandez-Carrascal and Bormann (2014), who showed that wind tracking could be divided into distinct geophysical regimes by clustering by cloud conditions. This study supplements that approach with the addition of machine learning, which, compared with traditional linear modeling approaches, should allow the model to capture more complex non-linear processes in the error function.…”

GEO-GEO Stereo-Tracking of Atmospheric Motion Vectors (AMVs) from the Geostationary Ring

“…A reliable estimate of the magnitude of the height-assignment error is critical for NWP data assimilation and must be accounted for [69]. Numerous studies have also been done to investigate the interpretation and impact of interpreting satellite winds as a layer-average wind [64,68,[70][71]. The stereo method enables the retrieval of very accurate cloud heights, and therefore, very accurate satellite wind height assignments; and therefore, we expect potential for the stereo winds to contribute to improving the skill of NWP forecasts.…”