Articles | Volume 14, issue 12
Research article
30 Nov 2021
Research article |  | 30 Nov 2021

Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry

Xingou Xu and Ad Stoffelen


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-200', Anonymous Referee #1, 31 Jul 2021
    • AC1: 'Reply on RC1', Xingou Xu, 27 Aug 2021
  • RC2: 'Comment on amt-2021-200', Anonymous Referee #2, 17 Aug 2021
    • AC2: 'Reply on RC2', Xingou Xu, 27 Aug 2021
    • AC3: 'Reply on RC2', Xingou Xu, 27 Aug 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Xingou Xu on behalf of the Authors (02 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (06 Sep 2021) by Marcos Portabella
RR by Anonymous Referee #1 (16 Sep 2021)
RR by Anonymous Referee #2 (18 Oct 2021)
ED: Publish as is (19 Oct 2021) by Marcos Portabella

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Xingou Xu on behalf of the Authors (22 Nov 2021)   Author's adjustment   Manuscript
EA: Adjustments approved (26 Nov 2021) by Marcos Portabella
Short summary
The support vector machine can effectively represent the increasing effect of rain affecting wind speeds. This research provides a correction of deviations that are skew- to Gaussian-like features caused by rain in Ku-band scatterometer wind. It demonstrates the effectiveness of a machine learning method when used based on elaborate analysis of the model establishment and result validation procedures. The corrected winds provide information previously lacking, which is vital for nowcasting.