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

Abstract

A method to apply an empirical feature track correction (FTC) in a new observation operator for atmospheric motion vectors (AMVs) is proposed. The FTC AMV observation operator determines the background estimate of the observed AMV vector wind, adjusting the background profile by determining an optimal height adjustment, averaging the profile over a layer of optimal thickness, and applying a linear correction to the averaged profile wind. The FTC observation operator is tested in the context of a collocation study between AMVs projected onto the collocated Aeolus horizontal line-of-sight (HLOS) and the Aeolus HLOS wind profiles. This study is a prototype for a variational FTC for numerical weather prediction data assimilation systems in which the Aeolus wind profiles take the place of the background in the FTC observation operator. Compared to a collocation where the Aeolus profile is interpolated linearly in height to the AMV height, a simple ad hoc averaging approach and the FTC approach reduce the mean square difference between the AMV observation and the Aeolus estimated AMV observation by 38% and 43%, respectively.

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).…”

Aeolus Rayleigh‐channel winds in cloudy conditions

“…In fact, there were changes of the settings in 2019 in mid July, end of September, early October, mid October, and end of December and in 2020 at the end of March. These changes were motivated to improve Aeolus sampling of seasonal effects, such as the sampling of polar stratospheric clouds during the Antarctic winter period and dedicated campaigns as part of Aeolus calibration/validation (Cal/Val) activities; for example, the atmospheric motion vector (AMV) campaign (first half of October 2019) with very dense Mie sampling below 3 km for optimal collocation of Mie winds with AMV winds, Hoffman et al (2021) and the Strateole 3 campaign (end September 2019 and mid October 2019 until end of March 2020) for optimal collocation of Aeolus winds with superpressure balloons drifting around the globe in the stratosphere, Bley et al (2022). As a result, the sampling of atmospheric particulates is not constant over the full period, and sampling clouds at varying heights and at varying resolutions may at least partly explain the variations of the parameters in Figure 5.…”

“…These changes were motivated to improve Aeolus sampling of seasonal effects, such as the sampling of polar stratospheric clouds during the Antarctic winter period and dedicated campaigns as part of Aeolus calibration/validation (Cal/Val) activities; for example, the atmospheric motion vector (AMV) campaign (first half of October 2019) with very dense Mie sampling below 3 km for optimal collocation of Mie winds with AMV winds, Hoffman et al . (2021) and the Strateolecampaign (end September 2019 and mid October 2019 until end of March 2020) for optimal collocation of Aeolus winds with superpressure balloons drifting around the globe in the stratosphere, Bley 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

“…Numerous studies have been conducted to address the decadal survey's 3D wind requirement (e.g., [7,8,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]). Many involve the characterization and validation of AMVs, e.g., from establishing collocation standards between sondes and AMVs (time difference < 90 min, height difference < 25 hPa, horizontal distance < 150 km) [12] to investigating the importance of the AMV error characteristics (e.g., height assignment accuracy and spatial error correlations) for DA [14,15], as well as intercomparing AMVs derived by several international satellite wind producing centers [7,8,16,19].…”

“…Many involve the characterization and validation of AMVs, e.g., from establishing collocation standards between sondes and AMVs (time difference < 90 min, height difference < 25 hPa, horizontal distance < 150 km) [12] to investigating the importance of the AMV error characteristics (e.g., height assignment accuracy and spatial error correlations) for DA [14,15], as well as intercomparing AMVs derived by several international satellite wind producing centers [7,8,16,19]. Recent studies now include Aeolus in their comparisons, e.g., to validate Aeolus winds [20,23], and to leverage Aeolus as a potential comparison standard for AMV characterization [24,25].…”