Poster Session 1 AI for Environmental Science Poster Session I

Monday, 13 January 2020: 4:00 PM-6:00 PM
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs:
John K. Williams, The Weather Company, An IBM Business, Andover, MA and Zhonghua Zheng, University of Illinois at Urbana−Champaign, Department of Civil and Environmental Engineering, Urbana, IL

Papers:
U.S. Water Prices: a Machine Learning Approach
Quinn McColly, Texas A&M University-Corpus Christi, Corpus Christi, TX; and P. Tissot and D. Yoskowitz

Gradient-based optimization to reduce uncertainty in radar rainfall estimates using deep learning techniques and in situ measurements from disdrometers
Haonan Chen, Colorado State Univ. and NOAA/Earth System Research Laboratory, Fort Collins, CO; and R. Cifelli and V. Chandrasekar

Available air channel capacity prediction by weather-capacity Graphical Neural Network(wcGNN)
Yao Xiao, Shanghai Em-Data Technology Co., Ltd, Shanghai, China; and J. Hang, H. Zuo, X. Guo, Z. Yan, and C. Lu

A Volume-to-Point Approach of Radar Based QPE
Ting-Shuo Yo, National Taiwan University, Taipei City, Taiwan; National Taiwan University, Taipei, Taiwan; and S. H. Su, C. C. Wu, C. W. Chang, and H. C. Kuo

Reconstruction of Severe Storms Observed by Weather Radars Using Recurrent Neural Networks
Cesar Beneti, SIMEPAR - Parana Meteorological System, Curitiba, Brazil; and C. Oliveira, S. Scheer, and L. Calvetti

Automated Detection of the Above Anvil Cirrus Plume Severe Storm Signature with Deep Learning
charles liles, NASA, Hampton, VA; and K. M. Bedka, T. D. Smith, Y. huang, R. biswas, and E. Xia

Exploring the Application of Machine Learning to Identification of Storm Objects
Patrick A. Campbell, CIMMS/Univ. of Oklahoma and NOAA/NSSL, Norman, OK; and K. L. Ortega, S. S. Williams, and T. M. Smith

MRMS-based Hail Sizing and Classification Using Different, Large Databases
Jose Efraim Aguilar Escamilla, OU/CIMMS and NOAA/OAR/NSSL, Norman, OK; and S. S. Williams and K. L. Ortega

Developing a Hail Probability Product for the Probabilistic Hazards Information Framework
Kiel L. Ortega, OU/CIMMS and NOAA/OAR/NSSL, Norman, OK; and S. S. Williams

A New Machine Learning-Based Tornado Detection Algorithm for the WSR-88D Network
Thea Sandmael, CIMMS/Univ. of Oklahoma and NOAA/OAR/NSSL, Norman, OK; and K. L. Elmore and B. R. Smith

Comparison of shallow and deep neural network water temperature predictions for resource management during cold stunning events
Jensen DeGrande, Texas A&M University-Corpus Christi, Corpus Christi, TX; and P. Tissot, J. Wiliams, H. Kamangir, N. Durham, and S. Bates

Implementation of an Artificial Neural Network to Forecast Storm Surge Time Series
Alexandra N. Ramos-Valle, Rutgers Univ., New Brunswick, NJ; and E. N. Curchitser and C. L. Bruyère

Seasonal Hurricane Forecasting Using Machine Learning
Timothy Hall, Association of Certified Meteorologists (ACM), Walkersville, MD; and K. Hall

Single Station Forecasting from Deep Learning Methods
Nathaneal Beveridge, Air Force Institute of Technology, Wright-Patterson AFB, OK; and A. Geyer and R. C. Tournay

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