Poster Session Artificial and Computational Intelligence and Its Applications to the Environmental Sciences Poster Session

Monday, 7 January 2019: 4:00 PM-6:00 PM
Hall 4 (Phoenix Convention Center - West and North Buildings)
Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences
Chair:
Philippe Tissot, Texas A&M University−Corpus Christi, Conrad Blucher Institute, Corpus Christi, TX

Papers:
176
One Problem: Two Methods? Factor Separation in the Atmospheric Sciences
Judah L. Cleveland, ORAU, Concord, NH; and J. A. Smith

Handout (1.4 MB)

177
Estimating Hourly PM2.5 Concentration from Satellite-Measured Top-of-Atmosphere Reflectance by Using a Machine Learning Algorithm
Jianjun Liu, Laboratory of Environmental Model and Data Optima, Laurel, MD; and F. Weng and Z. Li

Augmentation of Oceanographic Data with Deep Learning

178
A New Machine Learning−Based Cloud Phase Discrimination Algorithm Designed for Passive Infrared Satellite Sensors
Chenxi Wang, University Of Maryland, College Park, College Park, MD; and S. Platnick and K. Meyer

179
Radar Super-Resolution Using a Deep Convolutional Neural Network
Andrew Geiss, University of Washington, Seattle, WA; and J. C. Hardin

Handout (1.8 MB)

180
Estimating Tropical Cyclone Intensity in Passive Microwave Images Using Deep Learning Models
K. Ryder Fox, University of Miami, RSMAS, Miami, FL; and I. Gurung, J. J. Miller, M. Maskey, and A. L. Molthan

181
Toward an Enhanced Surface Classification Within MiRS Using a Deep Neural Network Approach
Ryan Honeyager, STAR, College Park, MD; and C. Grassotti, Y. K. Lee, S. Liu, and Q. Liu

182
Cloud Classification Using Artificial Intelligence

183
Using Machine Learning Techniques to Construct a Climatology of Mesoscale Convective Systems in the United States
Alex M. Haberlie, Louisiana State University, Baton Rouge, LA; and W. S. Ashley

184
Using Machine Learning to Classify Lightning for Earth Networks Total Lightning Network (ENTLN)
Saiadithya Cumbulam Thangaraj, Earth Networks, Germantown, MD; and M. Stock, J. Lapierre, M. Hoekzema, Y. Zhu, C. Schumann, R. Sonnenfeld, and L. C. Vidal

185
Atmospheric River Forecast Model Bias Correction
William Chapman, SIO, Brea, CA

186
Exploring the Use of Machine Learning to Develop a Predictive Model for Future Fire Seasons
Andrew T. White, University of Alabama in Huntsville, Huntsville, AL; and C. R. Hain, C. J. Schultz, J. L. Case, and K. D. White

187
Machine Learning to Predict Multi-Aerosol Mixing State Metrics
Zhonghua Zheng, University of Illinois at Urbana−Champaign, Urbana, IL; and V. G. Anantharaj, J. Gasparik, J. H. Curtis, Y. Yao, M. P. Hughes, D. Schmidt, M. West, and N. Riemer

188
Impacts of Different Background Error Samples Simulation Schemes on Numerical Forecast—Taking Typhoon "Soudelor" (1513) as an Example
Yehong Wang, Xiamen Meteorological Bureau, Xiamen, China

189
The Cloud-Based Future of ECMWF Data Services
Meghan Plumridge, ECMWF, Reading, United Kingdom

190
Spatio-Temporal Modeling for Regional Climate Model Evaluation: Eigenvector Filtering Versus Bayesian CAR
Meng Wang, Arizona State University, Tempe, AZ; and Y. Kamarianakis, M. Georgescu, and M. Moustaoui

191
Machine Learning and Big Data Analytics in Support of Fleet Safety during Severe Weather
Steve Wysmuller, IBM, Armonk, NY; and J. Traiteur and C. Reese

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