Preprints
https://doi.org/10.5194/amt-2022-20
https://doi.org/10.5194/amt-2022-20
 
18 Jan 2022
18 Jan 2022
Status: a revised version of this preprint is currently under review for the journal AMT.

A Statistically Optimal Analysis of Systematic Differences between Aeolus HLOS Winds and NOAA’s Global Forecast System

Hui Liu1,2, Kevin Garrett1, Kayo Ide3, Ross Hoffman1,2, and Katherine Lukens1,2 Hui Liu et al.
  • 1NOAA/NESDIS/Center for Satellite Applications and Research (STAR), College Park, MD 20740, USA
  • 2Cooperative Institute for Satellite Earth System Studies (CISESS), University of Maryland, College Park, MD 20740, USA
  • 3University of Maryland, College Park, MD 20740, USA

Abstract. The European Space Agency Aeolus mission launched the first of its kind spaceborne Doppler wind lidar in August 2018. To optimize assimilation of the Aeolus Level-2B (L2B) Horizontal Line-of-Sight (HLOS) winds, systematic differences (referred as biases hereafter) between the observations and numerical weather prediction (NWP) background winds should be removed. Total least squares (TLS) regression is used to estimate speed-dependent biases between Aeolus HLOS winds (L2B10) and the National Oceanic and Atmospheric Administration (NOAA) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) 6-h forecast winds. Unlike ordinary least squares regression, TLS regression optimally accounts for random errors in both predictors and predictands. Large well-defined, speed-dependent biases are found particularly in the lower stratosphere and troposphere of the tropics and Southern Hemisphere. These large biases should be corrected to increase the forecast impact of Aeolus data assimilated into global NWP systems.

Hui Liu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-20', Anonymous Referee #1, 09 Feb 2022
    • AC1: 'Reply on RC1', Hui Liu, 09 Apr 2022
  • RC2: 'Comment on amt-2022-20', Anonymous Referee #2, 02 Mar 2022
    • AC2: 'Reply on RC2', Hui Liu, 09 Apr 2022

Hui Liu et al.

Hui Liu et al.

Viewed

Total article views: 494 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
415 64 15 494 5 7
  • HTML: 415
  • PDF: 64
  • XML: 15
  • Total: 494
  • BibTeX: 5
  • EndNote: 7
Views and downloads (calculated since 18 Jan 2022)
Cumulative views and downloads (calculated since 18 Jan 2022)

Viewed (geographical distribution)

Total article views: 504 (including HTML, PDF, and XML) Thereof 504 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 May 2022
Download
Short summary
A TLS regression is used to optimally estimate speed-dependent biases between Aeolus L2B winds and short-term (6-h) forecasts of NOAA’s FV3GFS. The winds for 1–7 September 2019 are analyzed. Clear speed-dependent biases for both Mie and Rayleigh winds are found, particularly in the lower troposphere and stratosphere of the tropics and Southern Hemisphere. The biases are underestimated by the OLS regression of Aeolus O-B on FV3GFS winds; but are overestimated on Aeolus winds.