Articles | Volume 15, issue 13
Atmos. Meas. Tech., 15, 3925–3940, 2022

Special issue: Aeolus data and their application (AMT/ACP/WCD inter-journal...

Atmos. Meas. Tech., 15, 3925–3940, 2022
Research article
05 Jul 2022
Research article | 05 Jul 2022

A statistically optimal analysis of systematic differences between Aeolus horizontal line-of-sight winds and NOAA's Global Forecast System

Hui Liu et al.

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Cited articles

Cress, A.: Validation and impact assessment of Aeolus observations in the DWD modeling system, Status report, Aeolus NWP Impacts Working Meeting, June 2020, (last access: 2 January 2022), 2020. 
Daley, R.: Atmospheric data analysis, Cambridge Atmospheric and Space Science series, Cambridge University Press, Cambridge, 457 pp., ISBN-13: 978-0521458252, 1991. 
de Kloe, J., Rennie, M., Stoffelen, A., Tan, D., Anderson, E., Dabas, A., Poli, P., and Huber, D.: Aeolus Data Innovation Science Cluster DISC ADM-Aeolus Level-2B/2C Processor Input/Output Data Definitions Interface Control Document, KNMI, Aeolus, DISC, Tech. rep., REF: AED-SD-ECMWF-L2B-037, (last access: 2 January 2022), 2020. 
Deming, W. E.: Statistical adjustment of data, Wiley, NY, 288 pp., ISBN: 0-486-64685-8, 1943. 
European Space Agency (ESA): Aeolus L2B Earth Explorer data set, ESA [data set], (last access: 25 February 2021), 2020. 
Short summary
A total least squares (TLS) regression is used to optimally estimate linear speed-dependent biases between Aeolus Level-2B winds and short-term (6 h) forecasts of NOAA’s FV3GFS. The winds for 1–7 September 2019 are examined. Clear speed-dependent biases for both Mie and Rayleigh winds are found, particularly in the tropics and Southern Hemisphere. Use of the TLS correction improves the forecast of the 26–28 November 2019 winter storm over the USA.