Exploiting Aeolus Level-2B Winds to Better Characterize Atmospheric Motion Vector Bias and Uncertainty

Abstract

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.

Optimization and impact assessment of Aeolus HLOS wind assimilation in NOAA's global forecast system

“…The overall study includes the assessment of Aeolus data quality, the integration of Aeolus into NOAA regional and global NWP, and the ability of Aeolus observations to improve the use and impact of other observation types assimilated in NWP (e.g., AMVs). The present study focuses on the impact of Aeolus winds on global NWP, while other recent studies have focused on the quality and added value of Aeolus winds to other observing systems (e.g., AMVs) (Hoffman et al, 2022;Lukens et al, 2022).…”

System for Analysis of Wind Collocations (SAWC): A Novel Archive and Collocation Software Application for the Intercomparison of Winds from Multiple Observing Platforms

“…SAWC encompasses a global wind data archive (see Section 2.1), identified pairings between selected datasets in the form of index files (see Section 2.2.1), a collocation software application consisting of a collocation tool (see Section 2.2.1) and a plotting tool (see Section 2.2.2), and a user manual [28]. All of the components of SAWC are hosted and maintained on the NOAA/NESDIS/STAR public server, are continuously updated, and are publicly available online, as follows:…”

“…(see Section 2.2.2), and a user manual [28]. All of the components of SAWC are host maintained on the NOAA/NESDIS/STAR public server, are continuously update are publicly available online, as follows: SAWC's end-to-end process comprises the following three main steps: (1) data sition, (2) collocation of winds, and (3) analysis and visualization (Figure 1).…”

A collocation study of atmospheric motion vectors (AMVs) compared to Aeolus wind profiles with a feature track correction (FTC) observation operator

“…As points of reference, Lukens et al (2021) compile AMV statistics from studies comparing AMVs to radiosondes and find a range from roughly 4.5 to 9.0 m⋅s −1 for the vector RMS error. Assuming a middle value and converting to a component standard deviation yeilds 4.75 m⋅s −1 .…”

“…Third, Aeolus is in a polar twilight orbit, with all observations near dawn or dusk. Additional AMV limitations are discussed by Lukens et al (2021) who further analyze and discuss the collocation dataset used here. In spite of the limitations of the current study, we suggest that the FTC approach for AMVs has potential for improving (a) collocation studies comparing AMVs with wind profiles from various sources, (b) understanding of AMVs and the characterization of their errors, and (c) the use of AMVs in DA and NWP.…”