Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> The need for highly accurate atmospheric wind observations is a high priority in the science community, particularly for numerical weather prediction (NWP). To address this need, this study leverages Aeolus wind lidar level-2B data provided by the European Space Agency (ESA) as a potential comparison standard to better characterize atmospheric motion vector (AMV) bias and uncertainty. AMV products from geostationary (GEO) and low Earth orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August–September 2019. Winds from two Aeolus observing modes are compared with AMVs, namely Rayleigh-clear (RAY; derived from the molecular scattering signal) and Mie-cloudy (MIE; derived from the particle scattering signal). Quality-controlled (QC'd) Aeolus winds are co-located with QC'd AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean co-location differences (MCDs) and the standard deviation (SD) of those differences (SDCDs) are determined and analyzed. As shown in other comparison studies, the level of agreement between AMV and Aeolus wind velocities (HLOSVs) varies with the AMV type, geographic region, and height of the co-located winds, as well as with the Aeolus observing mode. In terms of global statistics, QC'd AMVs and QC'd Aeolus HLOSVs are highly correlated for both observing modes. Aeolus MIE winds are shown to have great potential value as a comparison standard to characterize AMVs, as MIE co-locations generally exhibit smaller biases and uncertainties compared to RAY co-locations. Aeolus RAY winds contribute a substantial fraction of the total SDCDs in the presence of clouds where co-location/representativeness errors are also large. Stratified comparisons with Aeolus HLOSVs are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, the Arctic, and at mid- to upper-levels in clear and cloudy scenes. AMVs in the SH/Antarctic generally exhibit larger-than-expected MCDs and SDCDs, most probably due to larger AMV height assignment errors and co-location/representativeness errors in the presence of high wind speeds and strong vertical wind shear, particularly for RAY comparisons.

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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