the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
AMV Error Characterization and Bias Correction by Leveraging Independent Lidar Data: a Simulation using OSSE and Optical Flow AMVs
Abstract. Accurate estimation of global winds is crucial for various scientific and practical applications, such as global chemical transport modeling and numerical weather prediction. One valuable source of wind measurements is Atmospheric Motion Vectors (AMVs), which play a vital role in the global observing system and numerical weather prediction models. However, errors in AMV retrievals need to be addressed before their assimilation into data assimilation systems, as they can affect the accuracy of outputs.
An assessment of the bias and uncertainty in passive-sensor AMVs can be done by comparing them with information from independent sources such as active-sensor winds. In this paper, we examine the benefit and performance of a colocation scheme using independent and sparse lidar wind observations as a dependent variable in a supervised machine learning model. We demonstrate the feasibility and performance of this approach in an Observing System Simulation Experiment (OSSE) framework, with reference geophysical state data obtained from high resolution Weather Research and Forecasting (WRF) Model simulations of three different weather events.
Lidar wind data are typically available in only one direction, and our study demonstrates that this single component of wind in high-precision active-sensor data can be leveraged (via a machine learning algorithm to model the conditional mean) to reduce the bias in the passive-sensor winds. Further, this active-sensor wind information can be leveraged through an algorithm that models the conditional quantiles to produce stable estimates of the prediction intervals, which are helpful in design and application of error analysis. We also found that the uncertainty prediction of this single-component wind has a positive linear relationship with the total-vector root-mean-squared-vector-difference (RMSVD), which can aid in design of quality indicators and filters.
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Status: closed
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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
- AC1: 'Reply on RC1', Hai Nguyen, 13 Mar 2024
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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.
- AC2: 'Reply on RC2', Hai Nguyen, 13 Mar 2024
Status: closed
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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
- AC1: 'Reply on RC1', Hai Nguyen, 13 Mar 2024
-
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.
- AC2: 'Reply on RC2', Hai Nguyen, 13 Mar 2024
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