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Application of empirical Bayes adaptive estimation technique for estimating winds from MST radar covering higher altitudes

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Abstract

Most of the works on estimating winds from the Doppler information provided by the very high frequency (VHF) radars use either parametric or nonparametric techniques. The studies using nonparametric signal processing techniques have tried to interpret the radar returns from higher altitudes where the signal to noise (SNR) is weak. It may be challenging to cover higher altitudes accurately without actually boosting signal strength by increasing transmit power and processing gain. The studies using parametric techniques have assumed data from a particular model to carry out pattern recognition and ascertain accuracy for a single frame of data, which does not guarantee consistency for an extended dataset. The present study introduces the empirical Bayesian approach for processing the radar data in near real-time to achieve higher altitude coverage. The eBayes adaptive estimation technique is a data-aware technique that learns from informative prior. It implements a hybrid model for data analytics, incorporating a nonparametric model to improve signal strength and a parametric model to reduce noise variance. It derives wind accurately from the Doppler, which is cross-validated with the wind information obtained from several GPS radiosonde simultaneous observations. In addition, the eBayes approach incorporates a modified version of classical moments estimation and introduces new quality parameters to quantify SNR characteristics. The study concludes that the eBayes analytical approach can accurately derive 3D winds consistently, covering higher altitudes of 25.2–28.8 km, improving the typical MST (Mesosphere Stratosphere and Troposphere) radar reference range of 21 km. This approach is helpful for several scientific investigations using measurements from VHF radars.

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Data availability

The related data and code are available for anonymous downloading at the Datacenter public repository https://www.narl.gov.in/datacenter/eBayes comprising the color versions of the figures and the supplementary figures in this article, and data in XLSX format. The related code is available at the public repository https://github.com/manasnarl/eBayesMST/. Name of the code/library: eBayesMST. Contact: Manas Ranjan padhy, Hardware requirements: Dual Intel XEON 3 GHz, 128 GB DDR4, Windows 11. Program language: MATLAB AND C# Interface Software required: DOT NET FRAMEWORK 5, MATLAB AND VISUAL STUDIO 22, C#, NMATH Program size: 7.26 MB.

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Acknowledgements

The first author acknowledges Sathyabama Institute of Science and Technology and NARL for issuing permission to pursue his doctoral program. The first and second authors jointly acknowledge NARL for providing data for their research. The authors thank RADG and ARTG teams of NARL for their help in conducting experiments.

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There was no funding for the current study.

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Authors and Affiliations

  1. Sathyabama Institute of Science and Technology, Chennai, India

    Manas Ranjan Padhy & Srinivasan Vigneshwari

  2. National Atmospheric Research Laboratory, Gadanki, India

    M. Venkat Ratnam

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  1. Manas Ranjan Padhy
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  2. Srinivasan Vigneshwari
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  3. M. Venkat Ratnam
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Contributions

MRP was involved in planning and implementing the data analytics, signal estimation, methodology, visualization, data curation, and writing—original draft. SV was involved in supervision and writing—review and editing. MVR was involved in resources and writing—review and editing.

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Correspondence to Manas Ranjan Padhy.

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This research is intended for the Ph.D. program of the first author.

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Not applicable. Study is not a human or animal.

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Padhy, M.R., Vigneshwari, S. & Ratnam, M.V. Application of empirical Bayes adaptive estimation technique for estimating winds from MST radar covering higher altitudes. SIViP 17, 3303–3311 (2023). https://doi.org/10.1007/s11760-023-02549-4

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