Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs

Nguyen, Hai; Posselt, Derek; Yanovsky, Igor; Wu, Longtao; Hristova-Veleva, Svetla

doi:https://doi.org/10.5194/amt-17-3103-2024

Articles | Volume 17, issue 10

https://doi.org/10.5194/amt-17-3103-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-17-3103-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 10

Research article

|

23 May 2024

Research article |

| 23 May 2024

Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs

Hai Nguyen, Derek Posselt, Igor Yanovsky, Longtao Wu, and Svetla Hristova-Veleva

Download

Final revised paper (published on 23 May 2024)
Preprint (discussion started on 16 Nov 2023)

Interactive discussion

Status: closed

RC1:
'Comment on amt-2023-239', Anonymous Referee #1, 11 Jan 2024

Overall the proposed scheme is interesting and a useful contribution to the field. Up to and including the bias correction scheme it is well described. After that once we get into (I think) a proposed error estimation scheme I found it difficult to understand. I’ve picked out a few things that were unclear to me, if these things are explained in the text I’m happy to be corrected. Some specific comments are included in the attached PDF

Citation: https://doi.org/10.5194/amt-2023-239-RC1
- AC1: 'Reply on RC1', Hai Nguyen, 13 Mar 2024
  
  Thank you for the detailed comments. Please see the responses to the comments in the attached PDF. Thank you!
  
  Citation: https://doi.org/10.5194/amt-2023-239-AC1
RC2:
'Comment on amt-2023-239', Anonymous Referee #2, 22 Jan 2024

These suggestions aim to refine certain aspects of the manuscript, ensuring a clearer and more cohesive presentation of the innovative work undertaken by the authors.

Citation: https://doi.org/10.5194/amt-2023-239-RC2
- AC2: 'Reply on RC2', Hai Nguyen, 13 Mar 2024
  
  Thank you for the comments. Please see the responses to the comments in the attached PDF. Thank you!
  
  Citation: https://doi.org/10.5194/amt-2023-239-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Hai Nguyen on behalf of the Authors (13 Mar 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (14 Mar 2024) by Ad Stoffelen

RR by Anonymous Referee #2 (14 Mar 2024)

RR by Anonymous Referee #1 (19 Mar 2024)

ED: Publish as is (21 Mar 2024) by Ad Stoffelen

AR by Hai Nguyen on behalf of the Authors (27 Mar 2024) Manuscript

Short summary

Accurate global wind estimation is crucial for weather prediction and environmental modeling. Our study investigates a method to refine atmospheric motion vectors (AMVs) by comparing them with high-precision active-sensor winds. Leveraging supervised learning, we discovered that using high-precision active-sensor data can significantly reduce biases in passive-sensor winds in addition to providing estimates of the wind errors, thereby improving their reliability.