Shun Yao
Ph.D. QualifyingMar 10, 2023, 10:00 am - 12:00 pm
Rockwell 165
N/A - qualifying exam
Abstract: qualifying exam
Adviser: Haonan Chen
Co-Adviser: Venkatachalam,Chandra
Non-ECE Member: Ketul Popat
Member 3: Anura Jayasumana
Addional Members: N/A
Co-Adviser: Venkatachalam,Chandra
Non-ECE Member: Ketul Popat
Member 3: Anura Jayasumana
Addional Members: N/A
Publications:
S. Yao, H. Chen, E. J. Thompson and R. Cifelli, "An Improved Deep Learning Model for High-Impact Weather Nowcasting," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 7400-7413, 2022, doi:10.1109/JSTARS.2022.3203398.
L. Han, J. Zhang, H. Chen, W. Zhang and S. Yao, "Toward the Predictability of a Radar-Based Nowcasting System for Different Precipitation Systems," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 1005705, doi:10.1109/LGRS.2022.3185031.
Y. Zhang, S. Bi, L. Liu, H. Chen, Y. Zhang, P. Shen, F. Yang, Y. Wang, Y. Zhang, S. Yao, “Deep Learning for Polarimetric Radar Quantitative Precipitation Estimation during Landfalling Typhoons in South China,” Remote Sensing, vol. 13, no. 16, p. 3157, Aug. 2021, doi:10.3390/rs13163157.
S. Yao, H. Chen and L. Han, "Short-term prediction of precipitation associated with landfalling hurricanes through deep learning," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 7176-7179, doi:10.1109/IGARSS47720.2021.9553303.
S. Yao, H. Chen and V. Chandrasekar, "A Self-attention based Deep Learning Model for Hurricane Nowcasting," 2023 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA, 2023, pp. 292-293, doi: 10.23919/USNC-URSINRSM57470.2023.10043145.
S. Yao and H. Chen, "Impact of Precipitation Regimes on Deep Learning based Nowcasting Performance," 2022 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA, 2022, pp. 243-244, doi: 10.23919/USNC-URSINRSM57467.2022.9881410.
S. Tan, H. Chen, S. Yao and V. Chandrasekar, "Weather Radar Beam Blockage Correction Using Deep Learning," 2023 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA, 2023, pp. 296-297, doi: 10.23919/USNC-URSINRSM57470.2023.10043151.
S. Tan, H. Chen, and S. Yao, “Inpainting polarimetric radar observations in complex environments,” USNC-URSI National Radio Science Meeting, Boulder, CO, 4-8 January 2022.
S. Yao, H. Chen, V. Chandrasekar, and H. Butler, “Blending of Deep Learning Nowcasting System and Operational Forecasting Products over the San Francisco Bay Area,” the 103rd AMS Annual Meeting, Denver, CO, USA, 8–12 January 2023.
S. Yao, and H. Chen, “Deep Learning for Nowcasting Precipitation Associated with Atmospheric Rivers,” the 103rd AMS Annual Meeting, Denver, CO, USA, 8–12 January 2023.
S. Yao, H. Chen, L. Han, and V. Chandrasekar, “Hurricane-Net: An adaptive deep learning framework for rainfall prediction of landfalling hurricanes,” the 102nd AMS Annual Meeting, Online, 23-27 January 2022.
H. Chen, S. Yao, and V. Chandrasekar, “A guide to deep learning for radar-based high-impact weather nowcasting,” AGU Fall Meeting, Chicago, IA, 12-16 December 2022.
Publications to be Reviewed:
Which Polarimetric Variables Are Important for Weather/No-Weather Discrimination?
A low-cost post-processing technique improves weather forecasts around the world
S. Yao, H. Chen, E. J. Thompson and R. Cifelli, "An Improved Deep Learning Model for High-Impact Weather Nowcasting," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 7400-7413, 2022, doi:10.1109/JSTARS.2022.3203398.
L. Han, J. Zhang, H. Chen, W. Zhang and S. Yao, "Toward the Predictability of a Radar-Based Nowcasting System for Different Precipitation Systems," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 1005705, doi:10.1109/LGRS.2022.3185031.
Y. Zhang, S. Bi, L. Liu, H. Chen, Y. Zhang, P. Shen, F. Yang, Y. Wang, Y. Zhang, S. Yao, “Deep Learning for Polarimetric Radar Quantitative Precipitation Estimation during Landfalling Typhoons in South China,” Remote Sensing, vol. 13, no. 16, p. 3157, Aug. 2021, doi:10.3390/rs13163157.
S. Yao, H. Chen and L. Han, "Short-term prediction of precipitation associated with landfalling hurricanes through deep learning," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 7176-7179, doi:10.1109/IGARSS47720.2021.9553303.
S. Yao, H. Chen and V. Chandrasekar, "A Self-attention based Deep Learning Model for Hurricane Nowcasting," 2023 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA, 2023, pp. 292-293, doi: 10.23919/USNC-URSINRSM57470.2023.10043145.
S. Yao and H. Chen, "Impact of Precipitation Regimes on Deep Learning based Nowcasting Performance," 2022 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA, 2022, pp. 243-244, doi: 10.23919/USNC-URSINRSM57467.2022.9881410.
S. Tan, H. Chen, S. Yao and V. Chandrasekar, "Weather Radar Beam Blockage Correction Using Deep Learning," 2023 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA, 2023, pp. 296-297, doi: 10.23919/USNC-URSINRSM57470.2023.10043151.
S. Tan, H. Chen, and S. Yao, “Inpainting polarimetric radar observations in complex environments,” USNC-URSI National Radio Science Meeting, Boulder, CO, 4-8 January 2022.
S. Yao, H. Chen, V. Chandrasekar, and H. Butler, “Blending of Deep Learning Nowcasting System and Operational Forecasting Products over the San Francisco Bay Area,” the 103rd AMS Annual Meeting, Denver, CO, USA, 8–12 January 2023.
S. Yao, and H. Chen, “Deep Learning for Nowcasting Precipitation Associated with Atmospheric Rivers,” the 103rd AMS Annual Meeting, Denver, CO, USA, 8–12 January 2023.
S. Yao, H. Chen, L. Han, and V. Chandrasekar, “Hurricane-Net: An adaptive deep learning framework for rainfall prediction of landfalling hurricanes,” the 102nd AMS Annual Meeting, Online, 23-27 January 2022.
H. Chen, S. Yao, and V. Chandrasekar, “A guide to deep learning for radar-based high-impact weather nowcasting,” AGU Fall Meeting, Chicago, IA, 12-16 December 2022.
Publications to be Reviewed:
Which Polarimetric Variables Are Important for Weather/No-Weather Discrimination?
A low-cost post-processing technique improves weather forecasts around the world
Program of Study:
ECE512
ECE558
ECE580
ECE513
CS545
STAT547
ATS741
ECE799
ECE512
ECE558
ECE580
ECE513
CS545
STAT547
ATS741
ECE799