Height Correction of Atmospheric Motion Vectors Using Airborne Lidar Observations

Abstract

ncertainties in the height assignment of atmospheric motion vectors (AMVs) are the main contributor to the total AMV wind error, and these uncertainties introduce errors that can be horizontally correlated over several hundred kilometers. As a consequence, only a small fraction of the available AMVs are currently used in numerical weather prediction systems. For this reason, alternative approaches for the height assignment of AMVs are investigated in this study: 1) using collocated airborne lidar observations and 2) treating AMVs as layer winds instead of winds at a discrete level. Airborne lidar observations from a field campaign in the western North Pacific Ocean region are used to demonstrate the potential of improving AMV heights in an experimental framework. On average, AMV wind errors are reduced by 10%?15% when AMV winds are assigned to a 100?150-hPa-deep layer beneath the cloud top derived from nearby lidar observations. In addition, the lidar?AMV height correction is expected to reduce the correlation of AMV errors as lidars provide independent cloud height information. This suggests that satellite lidars may be a valuable source of information for the AMV height assignment in the future. Furthermore, AMVs are compared with dropsonde and radiosonde winds averaged over vertical layers of different depth to investigate the optimal height assignment for AMVs in data assimilation. Consistent with previous studies, it is shown that AMV winds better match sounding winds vertically averaged over ~100 hPa than sounding winds at a discrete level. The comparison with deeper layers further reduces the RMS difference but introduces systematic differences of wind speeds.