Session 12B AI for Radars: Observations, Data Processing, Classification, Estimation, and Nowcasting

Wednesday, 31 January 2024: 4:30 PM-6:00 PM
338 (The Baltimore Convention Center)
Host: 23rd Conference on Artificial Intelligence for Environmental Science
Submitters:
Haonan Chen, Cooperative Institute for Research in the Atmosphere (CIRA), Boulder, CO; Keith Kelly and James M. Kurdzo, PhD, MIT Lincoln Laboratory, Air Traffic Control Systems, Lexington, MA
Cochairs:
David Ryglicki, NRL, Marine Meteorology Division, Monterey, CA and Kyle A. Hilburn

This session is devoted to data sciences and machine learning techniques in weather radar observations and quantitative applications. Topics include but are not limited to weather radar signal and data processing, physical sciences in radar meteorology and hydrology, and radar applications such as precipitation classification, estimation and nowcasting. This session will also feature presentations about combining radar measurements in the context of numerical model assimilation and short-term forecasts, as well as multiscale data fusion and regularization.

Papers:
4:30 PM
12B.1
Partial Beam Blockage Correction for Improving Radar Quantitative Precipitation Estimation
Songjian Tan, Colorado State University, Fort Collins, CO; and H. Chen

4:45 PM
12B.2
Evaluating and Optimizing the MRMS Machine Learning QPE Performance over the Western CONUS
Andrew P. Osborne, CIWRO/Univ. of Oklahoma and NOAA/NSSL, Norman, OK; and J. Zhang, R. A. Clark III, and K. Howard

5:00 PM
12B.3
Tornado Detection using Deep Neural Networks and Full-Resolution Polarimetric Weather Radar Data
Mark Sanford Veillette, MIT Lincoln Laboratory, Lexington, MA; and J. M. Kurdzo, S. Samsi, P. M. Stepanian, J. McDonald, and J. Y. Cho

5:15 PM
12B.4
Automatic AI Severe Weather Alerting from Radar Data
Matej Choma, Meteopress, Prague, Czech republic; and M. Murín, J. Bartel, M. Troller, and M. Najman

5:30 PM
12B.5
GeoRain v2.0: Enhancing Radar Rainfall Maps Using a Diffusion-Based Generative AI Approach with Satellite Imagery
Doyi Kim, SI Analytics, Daejeon, Yuseong-gu, South korea; and Y. Choi

5:45 PM
12B.6
Data Fusion Approach for Precipitation Nowcasting with ConvLSTM
Otavio Medeiros Feitosa, INPE, São José dos Campos, SP, Brazil; INPE, São José dos Campos - SP, SP, Brazil; and S. Freitas, H. F. D. C. Velho, and A. D. Chovert

Recorded Presentation
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
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