Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: Kathrin Folger x
  • All content x
Clear All Modify Search
Kathrin Folger and Martin Weissmann

Abstract

This study uses lidar observations from the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite to correct operational atmospheric motion vector (AMV) pressure heights. This intends to reduce the height assignment error of AMVs for their use in data assimilation. Additionally, AMVs are treated as winds in a vertical layer as proposed by several recent studies. Corrected and uncorrected AMV winds are evaluated using short-term forecasts of the global forecasting system of the German Weather Service. First, a direct lidar-based height reassignment of AMVs with collocated CALIPSO observations is evaluated. Assigning AMV winds from Meteosat-10 to ~120-hPa-deep layers below the lidar cloud top reduces the vector root-mean-square (VRMS) differences of AMVs from Meteosat-10 by 8%–15%. However, such a direct reassignment can only be applied to collocated AMV–CALIPSO observations that compose a comparably small subset of all AMVs. Second, CALIPSO observations are used to derive statistical height bias correction functions for a general height correction of all operational AMVs from Meteosat-10. Such a height bias correction achieves on average about 50% of the reduction of VRMS differences of the direct height reassignment. Results for other satellites are more ambiguous but still encouraging. Given that such a height bias correction can be applied to all AMVs from a geostationary satellite, the method exhibits a promising approach for the assimilation of AMVs in numerical weather prediction models in the future.

Full access
Kathrin Folger and Martin Weissmann

Abstract

Atmospheric motion vectors (AMVs) provide valuable wind information for the initial conditions of numerical weather prediction models, but height-assignment issues and horizontal error correlations require a rigid thinning of the available AMVs in current data assimilation systems. The aim of this study is to investigate the feasibility of correcting the pressure heights of operational AMVs from the geostationary satellites Meteosat-9 and Meteosat-10 with cloud-top heights derived from lidar observations by the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The study shows that the wind error of AMVs above 700 hPa is reduced by 12%–17% when AMV winds are assigned to 120-hPa-deep layers below the lidar cloud tops. This result demonstrates the potential of lidar cloud observations for the improvement of the AMV height assignment. In addition, the lidar correction reduces the “slow” bias of current upper-level AMVs and is expected to reduce the horizontal correlation of AMV errors.

Full access
Martin Weissmann, Kathrin Folger, and Heiner Lange

Abstract

Uncertainties 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.

Full access