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

Liu, Hui; Garrett, Kevin; Ide, Kayo; Hoffman, Ross N.; Lukens, Katherine E.

doi:https://doi.org/10.5194/amt-15-3925-2022

Articles | Volume 15, issue 13

https://doi.org/10.5194/amt-15-3925-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

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

https://doi.org/10.5194/amt-15-3925-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 13

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, Kevin Garrett, Kayo Ide, Ross N. Hoffman, and Katherine E. Lukens

Data sets

Aeolus L2B Earth Explorer data set European Space Agency (ESA) https://aeolus-ds.eo.esa.int/oads/access/

Model code and software

Preliminary code of Aeolus wind assimilation with NOAA GSI H. Liu, K. Garrett, K. Ide, and R.-N. Hoffman https://essic.umd.edu/joom2/index.php/faculty-and-staff?layout=user&user_id=1020&dir=JSROOT%2Fhliu6/CODE

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.