A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator
Radial velocity estimates provided by Doppler weather radar are critical measurements used by operational forecasters for the detection and monitoring of life-impacting storms. The sampling methods used to produce these measurements are inherently susceptible to aliasing, which produces ambiguous ve...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
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American Meteorological Society Publications
2023
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| Online Access: | https://hdl.handle.net/1721.1/153198 |
| _version_ | 1811090112530874368 |
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| author | Veillette, Mark S. Kurdzo, James M. Stepanian, Phillip M. McDonald, Joseph Samsi, Siddharth Cho, John Y. N. |
| author2 | Lincoln Laboratory |
| author_facet | Lincoln Laboratory Veillette, Mark S. Kurdzo, James M. Stepanian, Phillip M. McDonald, Joseph Samsi, Siddharth Cho, John Y. N. |
| author_sort | Veillette, Mark S. |
| collection | MIT |
| description | Radial velocity estimates provided by Doppler weather radar are critical measurements used by operational forecasters for the detection and monitoring of life-impacting storms. The sampling methods used to produce these measurements are inherently susceptible to aliasing, which produces ambiguous velocity values in regions with high winds and needs to be corrected using a velocity dealiasing algorithm (VDA). In the United States, the Weather Surveillance Radar-1988 Doppler (WSR-88D) Open Radar Product Generator (ORPG) is a processing environment that provides a world-class VDA; however, this algorithm is complex and can be difficult to port to other radar systems outside the WSR-88D network. In this work, a deep neural network (DNN) is used to emulate the two-dimensional WSR-88D ORPG dealiasing algorithm. It is shown that a DNN, specifically a customized U-Net, is highly effective for building VDAs that are accurate, fast, and portable to multiple radar types. To train the DNN model, a large dataset is generated containing aligned samples of folded and dealiased velocity pairs. This dataset contains samples collected from WSR-88D Level-II and Level-III archives and uses the ORPG dealiasing algorithm output as a source of truth. Using this dataset, a U-Net is trained to produce the number of folds at each point of a velocity image. Several performance metrics are presented using WSR-88D data. The algorithm is also applied to other non-WSR-88D radar systems to demonstrate portability to other hardware/software interfaces. A discussion of the broad applicability of this method is presented, including how other Level-III algorithms may benefit from this approach.</jats:p>
<jats:sec>
<jats:title>Significance Statement</jats:title>
<jats:p>Accurate and timely estimates of wind within storms are critically important for a number of applications, including severe storm nowcasting, maritime operational planning, aviation forecasting, and public safety coordination. Velocity aliasing is a common artifact that requires data quality control. While velocity dealiasing algorithms (VDAs) have been developed for decades, they remain a computationally complex and challenging problem. This paper presents an application of deep neural networks (DNNs) to increase the computational efficiency and portability of VDAs. A DNN is trained to emulate an operational algorithm, and performance is quantified over a large dataset. This work gives a convincing example of the benefits that deep learning can provide for radar algorithms, and future work highlighting these opportunities is discussed.</jats:p></jats:sec> |
| first_indexed | 2024-09-23T14:33:43Z |
| format | Article |
| id | mit-1721.1/153198 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:33:43Z |
| publishDate | 2023 |
| publisher | American Meteorological Society Publications |
| record_format | dspace |
| spelling | mit-1721.1/1531982024-01-24T21:44:14Z A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator Veillette, Mark S. Kurdzo, James M. Stepanian, Phillip M. McDonald, Joseph Samsi, Siddharth Cho, John Y. N. Lincoln Laboratory Radial velocity estimates provided by Doppler weather radar are critical measurements used by operational forecasters for the detection and monitoring of life-impacting storms. The sampling methods used to produce these measurements are inherently susceptible to aliasing, which produces ambiguous velocity values in regions with high winds and needs to be corrected using a velocity dealiasing algorithm (VDA). In the United States, the Weather Surveillance Radar-1988 Doppler (WSR-88D) Open Radar Product Generator (ORPG) is a processing environment that provides a world-class VDA; however, this algorithm is complex and can be difficult to port to other radar systems outside the WSR-88D network. In this work, a deep neural network (DNN) is used to emulate the two-dimensional WSR-88D ORPG dealiasing algorithm. It is shown that a DNN, specifically a customized U-Net, is highly effective for building VDAs that are accurate, fast, and portable to multiple radar types. To train the DNN model, a large dataset is generated containing aligned samples of folded and dealiased velocity pairs. This dataset contains samples collected from WSR-88D Level-II and Level-III archives and uses the ORPG dealiasing algorithm output as a source of truth. Using this dataset, a U-Net is trained to produce the number of folds at each point of a velocity image. Several performance metrics are presented using WSR-88D data. The algorithm is also applied to other non-WSR-88D radar systems to demonstrate portability to other hardware/software interfaces. A discussion of the broad applicability of this method is presented, including how other Level-III algorithms may benefit from this approach.</jats:p> <jats:sec> <jats:title>Significance Statement</jats:title> <jats:p>Accurate and timely estimates of wind within storms are critically important for a number of applications, including severe storm nowcasting, maritime operational planning, aviation forecasting, and public safety coordination. Velocity aliasing is a common artifact that requires data quality control. While velocity dealiasing algorithms (VDAs) have been developed for decades, they remain a computationally complex and challenging problem. This paper presents an application of deep neural networks (DNNs) to increase the computational efficiency and portability of VDAs. A DNN is trained to emulate an operational algorithm, and performance is quantified over a large dataset. This work gives a convincing example of the benefits that deep learning can provide for radar algorithms, and future work highlighting these opportunities is discussed.</jats:p></jats:sec> Department of Defense (DoD) 2023-12-18T21:15:47Z 2023-12-18T21:15:47Z 2023-07 Article http://purl.org/eprint/type/JournalArticle 2769-7525 https://hdl.handle.net/1721.1/153198 Veillette, M. S., J. M. Kurdzo, P. M. Stepanian, J. McDonald, S. Samsi, and J. Y. N. Cho, 2023: A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator. Artif. Intell. Earth Syst., 2, e220084. 10.1175/AIES-D-22-0084.1 10.1175/aies-d-22-0084.1 Artificial Intelligence for the Earth Systems Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Meteorological Society Publications American Meteorological Society |
| spellingShingle | Veillette, Mark S. Kurdzo, James M. Stepanian, Phillip M. McDonald, Joseph Samsi, Siddharth Cho, John Y. N. A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator |
| title | A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator |
| title_full | A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator |
| title_fullStr | A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator |
| title_full_unstemmed | A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator |
| title_short | A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator |
| title_sort | deep learning based velocity dealiasing algorithm derived from the wsr 88d open radar product generator |
| url | https://hdl.handle.net/1721.1/153198 |
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