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Browsing by Author "Chandrasekaran, V., committee member"

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    Cloud property retrievals using polarimetric radar: untangling signals of pristine ice and snow
    (Colorado State University. Libraries, 2020) Kedzuf, Nicholas J., author; Chiu, J. Christine, advisor; van Leeuwen, Peter Jan, committee member; DeMott, Paul, committee member; Chandrasekaran, V., committee member
    Ice and mixed phase clouds are critical components of Earth's climate system via their strong controls on global precipitation distribution and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of ice number concentration, due to the difficulty in distinguishing embedded pristine ice from snow aggregates in remote sensing observations. This hinders our ability to study detailed cloud ice microphysical processes from observations. This thesis presents a rigorous method that separates the scattering signals of pristine ice and snow aggregates in scanning polarimetric radar observations to retrieve their respective abundances and sizes for the first time. This method, dubbed ENCORE-ICE, is built on an iterative ensemble retrieval framework. It provides number concentration, median volume diameter, and ice water content of pristine ice and snow aggregates with full error statistics. The retrieved cloud properties are evaluated against in-situ aircraft measurements from a UK field campaign. For a stratiform cloud system with embedded convective features associated with observed ice number concentration of 0.1–10 L–1 and ice water content from 0.01–0.6 g m–3, the retrievals are mainly in the range of 1.0 –15 L–1 and 0.003–0.6 g m–3. To investigate the ice property evolution in a Lagrangian sense, the retrieval method is also applied to along-wind scanning radar measurements from an Atmospheric Radiation Measurement (ARM) campaign in Finland. For the cases presented, snow aggregates are typically of 5–10 mm size in diameter, which is ~10 times larger than pristine ice and thus dominates radar reflectivity. However, the partitioning in ice water content between pristine ice and aggregates varies and largely depends on ice number concentration. More importantly, the retrieved pristine ice number concentration exceeds the predicted concentration of primary ice nuclei at a mid-cloud temperature of –15°C by two orders of magnitude, suggesting possible secondary ice production, one of the outstanding issues in cloud physics. This highlights the potential of using ENCORE-ICE to identify secondary ice production events and understand their trigger mechanisms.
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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.