Abstract

The need for highly accurate atmospheric wind observations is a high priority in the science community, and in particular numerical weather prediction (NWP). To address this requirement, this study leverages Aeolus wind LIDAR Level-2B data provided by the European Space Agency (ESA) to better characterize atmospheric motion vector (AMV) bias and uncertainty, with the eventual goal of potentially improving AMV algorithms. AMV products from geostationary (GEO) and low-Earth polar orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August and September 2019. Winds from two of the four Aeolus observing modes are utilized for comparison with AMVs: Rayleigh-clear (derived from the molecular scattering signal) and Mie-cloudy (derived from particle scattering). For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean collocation differences (MCD) and standard deviation (SD) of those differences (SDCD) are determined from comparisons based on a number of conditions, and their relation to known AMV bias and uncertainty estimates is discussed. GOES-16 and LEO AMV characterizations based on Aeolus winds are described in more detail. Overall, QC’d AMVs correspond well with QC’d Aeolus HLOS wind velocities (HLOSV) for both Rayleigh-clear and Mie-cloudy observing modes, despite remaining biases in Aeolus winds after reprocessing. Comparisons with Aeolus HLOSV are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, and in the Arctic, and at mid- to upper-levels in both clear and cloudy scenes. SH comparisons generally exhibit larger than expected SDCD, which could be attributed to height assignment errors in regions of high winds and enhanced vertical wind shear. GOES-16 water vapor clear-sky AMVs perform best relative to Rayleigh-clear winds, with small MCD (-0.6 m s-1 to 0.1 m s-1) and SDCD (5.4–5.6 m s-1) in the NH and tropics that fall within the accepted range of AMV error values relative to radiosonde winds. Compared to Mie-cloudy winds, AMVs exhibit similar MCD and smaller SDCD (~4.4–4.8 m s-1) throughout the troposphere. In polar regions, Mie-cloudy comparisons have smaller SDCD (5.2 m s-1 in the Arctic, 6.7 m s-1 in the Antarctic) relative to Rayleigh-clear comparisons, which are larger by 1–2 m s-1. The level of agreement between AMVs and Aeolus winds varies per combination of conditions including the Aeolus observing mode coupled with AMV derivation method, geographic region, and height of the collocated winds. It is advised that these stratifications be considered in future comparison studies and impact assessments involving 3D winds. Additional bias corrections to the Aeolus dataset are anticipated to further refine the results.

Highlights

  • 40 The need to improve atmospheric 3D wind observations in the troposphere has long been a high priority in the science community

  • 20 For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled atmospheric motion vector (AMV) in space and time, and the AMVs are projected onto the Aeolus horizontal line-of-sight (HLOS) direction

  • The availability of the Aeolus dataset provides the unique opportunity to directly assess the performance of AMVs derived from different retrieval channels relative to a global reference wind profile dataset observed by a single unit

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Summary

Introduction

40 The need to improve atmospheric 3D wind observations in the troposphere has long been a high priority in the science community. The availability of the Aeolus dataset provides the unique opportunity to directly assess the performance of AMVs derived from different retrieval channels relative to a global reference wind profile dataset observed by a single unit. The operational M1 bias correction uses instrument temperatures as predictors and innovation departures from ECMWF backgrounds as a reference, and is shown to improve the quality of the Rayleigh and Mie signal levels, reducing the Aeolus HLOS wind bias relative to ECMWF background winds by over 80%: the global average Rayleigh-clear bias decreased to near-zero and the Mie bias decreased to -0.15 m s-1 (Abdalla et al, 2020; information regarding the limitations of the operational M1 correction 105 are presented in Weiler et al, 2021). Efforts at ESA are currently underway to resolve these issues

Atmospheric motion vectors 135 AMVs examined in this study are used in the
Approach and quality controls
Rayleigh-clear (RAY) comparisons
Findings
Mie-cloudy (MIE) comparisons

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