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Browsing by Author "Chen, Haonan, advisor"

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    Deep learning for radar beam blockage correction
    (Colorado State University. Libraries, 2023) Tan, Songjian, author; Chen, Haonan, advisor; Chandrasekaran, V., committee member; Wang, Haonan, committee member
    This thesis aims to propose a deep learning framework based on generative adversarial networks (GANs) for correcting partial beam blockage regions in polarimetric radar observations. The correction of such data is an essential step in radar data quality control and subsequent quantitative applications, especially in complex terrain environments. The proposed methodology is demonstrated using two S-band operational Weather Surveillance Radar - 1988 Doppler (WSR-88D) located in different regions of the western United States, characterized by different precipitation types. To train the GAN model, observation sectors of both radars are manually cropped to simulate partial beam blockage situations. The effectiveness of the trained models is demonstrated using independent precipitation events in Texas and California, and their generalization capacity is examined by cross-testing the data with different precipitation features. The beam blockage correction performance is compared with a traditional linear interpolation approach, and the results show that the proposed approach significantly improves the continuity of precipitation observations in both domains. While visible discrepancies exist between the models trained based on convective and stratiform precipitation events in Texas and California, respectively, both models outperform the traditional interpolation method. The repaired observations demonstrate great potential for improved quantitative applications, despite the unavailability of ground truth for real blocked radar data.
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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.
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    Interpolating RGB radar images based on machine learning
    (Colorado State University. Libraries, 2023) Yi, Chenke, author; Chandrasekar, V., advisor; Chen, Haonan, advisor; Siller, Thomas, committee member; Gooch, Steven, committee member
    Weather radar interpolation is the process of estimating and predicting rainfall data in areas that are not directly observed by radar. This technique is commonly used in weather forecasting, flood prediction, and agricultural planning. The main goal of weather radar interpolation is to produce accurate and reliable precipitation maps in areas with limited radar coverage or where the radar data is incomplete. The interpolation methods can be categorized into two main groups: deterministic and stochastic. Deterministic methods use mathematical equations and physical models to estimate the rainfall, while stochastic methods rely on statistical algorithms to analyze the correlations between the radar measurements and ground observations. In recent years, machine learning algorithms have also been applied to weather radar interpolation, showing promising results in accuracy and robustness. In this paper, we mainly propose a radar image interpolation method based on spatio-temporal convolutional networks. The experiments are mainly compared and analyzed for different combinations of networks, connection methods, and different loss functions.