Theoretical, model- and/or data-centric AI in radar meteorology; AI and machine learning advancements exemplified through weather radar applications; novel uncertainty quantification and eXplainable AI (XAI) techniques; submissions should have a clear emphasis on novel AI/ML methods.

Papers:

124

An Implicit Generative Model Approach to Radar-Based Precipitation Nowcasting: Fundamentals and Evaluations

Jaeho Choi, KMA, Seoul, South korea; and Y. Kim, K. H. Kim, S. H. Jung, and I. Cho

125

Combining TITAN and LSTM Model in a New Tool of Nowcasting

Andrea Salome Viteri Lopez, Sao Paulo, SP, Brazil; and C. A. Morales

126

Airborne Radar Quality Control with Machine Learning

Alexander James DesRosiers, Colorado State University, Atmospheric Science, Fort Collins, CO; and M. Bell

127

Multisensor Agile Adaptive Sampling (MAAS): a methodology to collect radar observations of convective cell life cycle

Katia Lamer, Brookhaven National Laboratory, Upton, NY; and P. Kollias, E. Luke, B. P. Treserras, M. Oue, and B. Dolan

128

Impact of LDAPS model error on precipitation types from Spectral Bin Model

Wonbae Bang, Kyungpook National University, Daegu, Daegu, Korea, Republic of (South); and K. Kim, J. Carlin, A. V. Ryzhkov, and G. LEE

129

A Machine Learning Approach for Intelligent VAD Wind Estimation with S-band Polarimetric Weather Radars

Precious Kizeze Jatau, University of Oklahoma, Norman, OK; and T. Y. Yu and V. M. Melnikov

130

A Deep Learning Model for Precipitation Short-Term Forecasting over China Based on Radar Observations

Sheng Chen, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences; and Q. Huang and J. Tan

131

Probabilistic quantitative precipitation estimation based on a random forest approach and operational polarimetric measurements from the Swiss radar network

Rebecca Gugerli, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Vaud, Switzerland; METEOSWISS, Locarno-Monti, Ticino, Switzerland; and D. Wolfensberger, M. Gabella, U. M. Germann, and A. Berne

Handout (5.5 MB)

132

SaGRU: A self-attention-based recurrent neural network model for radar nowcasting

Yao Shun, Colorado State University, Fort Collins, CO; and H. Chen, V. Chandrasekar, and E. Thompson

132

Fusing observations and model output for radar nowcasting

A Radar and Meteorology Derived Hail Damage Estimate

133

Hail nowcasting based on radar and model fields using random forests

Frederic Fabry, McGill Univ., Montreal, QC, Canada; and C. Jing

Handout (1.9 MB)

Development of Radar-Based Nowcasting for Intense Precipitation in the Tropics

134

Evaluation of Super Resolution using Attention U-net over Dallas Fort Worth

EunYeol Kim, Colorado State Univ., Fort Collins, CO; and V. Chandrasekar

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