Abstract
Atmospheric motion vectors (AMVs) are wind observations derived by tracking cloud or water‐vapour features in consecutive satellite images. These observations are incorporated into numerical weather prediction (NWP) through data assimilation. In the assimilation algorithm, the weighting given to an observation is determined by the uncertainty associated with its measurement and representation. Previous studies assessing AMV uncertainty have used direct comparisons between AMVs with collocated radiosonde data and AMVs derived from Observing System Simulation Experiments (OSSEs). These have shown that AMV error is horizontally correlated with the characteristic length‐scale up to 200 km. In this work, we take an alternative approach and estimate AMV error variance and horizontal error correlation using background and analysis residuals obtained from the Met Office limited‐area, 3 km horizontal grid‐length data assimilation system. The results show that the observation‐error variance profile ranges from 5.2–14.1 s m2 s−2, with the highest values occurring at high and medium heights. This is indicative that the maximum error variance occurs where wind speed and shear, in combination, are largest. With the exception of AMVs derived from the High Resolution Visible channel, the results show horizontal observation‐error correlations at all heights in the atmosphere, with correlation length‐scales ranging between 140 and 200 km. These horizontal length‐scales are significantly larger than current AMV observation‐thinning distances used in the Met Office high‐resolution assimilation.
Highlights
Atmospheric motion vectors (AMVs) are wind observations derived from satellite images by identifying a feature and later tracking it in consecutive images
The AMV derivation process is composed of three main steps: suitable feature selection, wind-vector calculation by measuring the displacement of the tracked feature in consecutive images and height assignment by converting the brightness temperature to pressure. (The derivation includes an initial preprocessing step and quality-control procedures at the end.) Currently, wind-speed observations are derived from medium-size features without rapid mutation and exclusively horizontal displacement, meaning that AMVs can describe the general atmospheric flow only
The AMVs under analysis within this article are derived by applying the High Resolution Wind (HRW) algorithm from the Nowcasting and Very Short Range Forecasting SAF group (NWCSAF)† to data observed using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat-10 MSG satellite
Summary
Atmospheric motion vectors (AMVs) are wind observations derived from satellite images by identifying a feature and later tracking it in consecutive images. It has been shown that, when the heightassignment 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, 2015). The new contribution of the work presented in this article is the estimation of AMV error statistics for the Met Office limited-area high-resolution data assimilation system, using the diagnostic of Desroziers et al (2005). The horizontal error correlation length-scales for AMVs at all pressure levels range between 150 and 210 km These length-scales are similar to results found in previous studies and are likely to be a result of height assignment and tracking errors. The main theoretical aspects of the diagnostic and its numerical implementation for this work are introduced
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
