16 Nov 2023
 | 16 Nov 2023
Status: this preprint is currently under review for the journal AMT.

AMV Error Characterization and Bias Correction by Leveraging Independent Lidar Data: a Simulation using OSSE and Optical Flow AMVs

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

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.

Hai Nguyen et al.

Status: open (until 15 Jan 2024)

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Hai Nguyen et al.

Hai Nguyen et al.


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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.