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Browsing by Author "Wang, Liangwei, author"

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    Improving radar quantitative precipitation estimation through optimizing radar scan strategy and deep learning
    (Colorado State University. Libraries, 2024) Wang, Liangwei, author; Chen, Haonan, advisor; Chandrasekaran, Venkatchalam, committee member; Wang, Haonan, committee member
    As radar technology plays a crucial role in various applications, including weather forecasting and military surveillance, understanding the impact of different radar scan elevation angles is paramount to optimize radar performance and enhance its effectiveness. The elevation angle, which refers to the vertical angle at which the radar beam is directed, significantly influences the radar's ability to detect, track, and identify targets. The effect of different elevation angles on radar performance depends on factors such as radar type, operating environment, and target characteristics. To illustrate the impact of lowering the minimum scan elevation angle on surface rainfall mapping, this article focuses on the KMUX WSR-88D radar in Northern California as an example, within the context of the National Weather Service's efforts to upgrade its operational Weather Surveillance Radar. By establishing polarimetric radar rainfall relations using local disdrometer data, the study aims to estimate surface rainfall from radar observations, with a specific emphasis on shallow orographic precipitation. The findings indicate that a lower scan elevation angle yields superior performance, with a significant 16.1% improvement in the normalized standard error and a 19.5% enhancement in the Pearson correlation coefficient, particularly for long distances from the radar. In addition, conventional approaches to radar rainfall estimation have limitations, recent studies have demonstrated that deep learning techniques can mitigate parameterization errors and enhance precipitation estimation accuracy. However, training a model that can be applied to a broad domain poses a challenge. To address this, the study leverages crowdsourced data from NOAA and SFL, employing a convolutional neural network with a residual block to transfer knowledge learned from one location to other domains characterized by different precipitation properties. The experimental results showcase the efficacy of this approach, highlighting its superiority over conventional fixed-parameter rainfall algorithms. Machine learning methods have shown promising potential in improving the accuracy of quantitative precipitation estimation (QPE), which is critical in hydrology and meteorology. While significant progress has been made in applying machine learning to QPE, there is still ample room for further research and development. Future endeavors in machine learning-based QPE will primarily focus on enhancing model accuracy, reliability, and interpretability while considering practical operational applications in hydrology and meteorology.