Poster Session 1 AI for Environmental Science Poster Session I

Monday, 13 January 2020: 4:00 PM-6:00 PM
Hall B (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
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:
356A
A feature extraction and sequence prediction framework for N-Dimensional data structures: an application for subseasonal rainfall and streamflow forecast.
Thomas C. M. Martin, Univ. of São Paulo, São Paulo, Brazil; and G. M. P. Perez and H. R. Rocha

356
U.S. Water Prices: A Machine Learning Approach
Quinn McColly, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and P. Tissot and D. Yoskowitz

357
Gradient-Based Optimization to Reduce Uncertainty in Radar Rainfall Estimates Using Deep Learning Techniques and In Situ Measurements from Disdrometers
Haonan Chen, Colorado State University and NOAA Physical Sciences Laboratory, Boulder, CO; and R. Cifelli and V. Chandrasekar

358
A Volume-to-Point Approach of Radar-Based QPE
Ting-Shuo Yo, National Taiwan Univ., Taipei City, Taiwan; National Taiwan Univ., Taipei, Taiwan; and S. H. Su, C. C. Wu, C. W. Chang, and H. C. Kuo

359
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

Handout (2.5 MB)

Available air channel capacity prediction by weather-capacity Graphical Neural Network(wcGNN)

360
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. X. Huang, R. Biswas, E. Xia, C. Dolan, and A. Hosseini Jafari
Manuscript (1.6 MB)

Handout (1.9 MB)

361
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

362
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

Handout (1.0 MB)

363
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

Handout (1.2 MB)

364
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

365
Comparison of Shallow and Deep Neural Network Water Temperature Predictions for Resource Management during Cold Stunning Events
Jensen DeGrande, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and P. Tissot, J. Wiliams, H. Kamangir, N. Durham, and S. Bates

366
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

367
Seasonal Hurricane Forecasting Using Machine Learning
Timothy Hall, Walkersville, MD; and K. Hall

Handout (1.2 MB)

368
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

368A
Relative Importance of Thermodynamic Variables to the Worldwide Variability of Thunderstorms
Chuntao Liu, Texas A&M—Corpus Christi, Corpus Christi, TX; and N. Liu and P. Tissot

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
See more of: 19th Conference on Artificial Intelligence for Environmental Science
<< Previous Session | Next Session >>