Posters III

Wednesday, 31 January 2024: 3:00 PM-4:30 PM
Hall E (The Baltimore Convention Center)
Host: 23rd Conference on Artificial Intelligence for Environmental Science

Papers:
664
Predicting Surface Temperatures Using Compound Geopotential Heights in a Deep Learning Framework
Jahangir Ali, University of Arkansas, Fayetteville, AR; and L. Cheng

665
Discovering New Representations of Near-Surface Exchange Using Symbolic Regression
Andrew V. Geiss, PNNL, Richland, WA; and L. K. Berg

666
Impact of Latent Heating and Cloud Radiative Effects on the Variability of Jet Streams in Observations
Xinhuiyu Liu, Univ. of Virginia, CHARLOTTESVILLE, VA; and K. M. Grise and Y. Rao

667
Incorporating Causality with Deep Learning in Predicting Short-Term and Seasonal Sea Ice
Emam Hossain, University of Maryland Baltimore County, Baltimore, MD; and S. Ali, M.S., Y. Huang, N. J. Schlegel, J. Wang, A. C. Subramanian, and M. O. Gani

668
Estimating Causal Effects of Greenland Blocking on Arctic Sea Ice Melt using Deep Learning Technique
Sahara Ali, M.S., UMBC, Baltimore, MD; UMBC, Baltimore, MD; and Faruque, Y. Huang, M. O. Gani, N. J. Schlegel, A. Subramanian, and J. Wang

Handout (4.5 MB)

669
Assessing Radiation Emulator of Numerical Weather Prediction Models for Operational Application in Korea Meteorological Administration

670
Parameterizing Cloud Microphysics with Machine Learning-Enabled Bayesian Parameter Inference
Kaitlyn Loftus, Columbia University, New York, NY; and M. van Lier-Walqui, H. Morrison, K. K. Chandrakar, PhD, M. A. Bhouri, and S. P. Santos

671
Explainable Offline-Online Training of Neural Networks for Parameterization: A 1D Gravity Wave-QBO Testbed
Hamid A. Pahlavan, Rice University, Houston, TX; and P. Hassanzadeh and M. J. Alexander

672
Machine Learning Emulation of a Quasi Two-Moment Microphysical Scheme with Diagnosed Particle Sizes
Mircea Grecu, Morgan State University, Greenbelt, MD; and X. Li, M. L. Rilee, M. P. Bauer, and K. S. Kuo

Handout (979.5 kB)

673
Correcting Errors in MCS Quantitative Precipitation Forecasts of Intensity, Timing, and Location Using Machine Learning Algorithms
Tyreek JaQuan Frazier, Iowa State University, Ames, IA

Handout (1.1 MB)

674
Evaluation of Flash Drought Identification with Machine Learning Techniques, Part 1: Standard Machine Learning Algorithms
Stuart Galen Edris, University of Oklahoma, Norman, OK; and J. B. Basara, J. I. Christian, J. C. Furtado, A. McGovern, and X. Xiao

Handout (10.6 MB)

675
Evaluation of Flash Drought Identification with Machine Learning Techniques, Part 2: Common Deep Learning Algorithms
Stuart Galen Edris, University of Oklahoma, Norman, OK; and J. B. Basara, J. I. Christian, J. C. Furtado, A. McGovern, and X. Xiao

Handout (10.6 MB)

676
Enhancing Snowfall Accumulation Prediction in the Northeastern United States: Integrating NWP, Probabilistic Forecast and Machine Learning
Ummul Khaira, University of Connecticut, Storrs, CT; and M. Astitha

677
Bias Correction of Global Ensemble Forecast System (GEFS) Precipitation Forecasts using Long Short Term Memory (LSTM) Model over Complex Terrain Regions
Md Abul Ehsan Bhuiyan, National Oceanic and Atmospheric Administration, College Park, MD; and W. M. Thiaw and E. B. Bekele

678
Exploration of Machine Learning Methods from Decision Trees to Long-Short Term Memory Operators to Quality Control Soil Moisture Observations.
Ronald D. Leeper, MA in Geoscience and GIS Certificate, NCState & Cooperative Institute for Satellite Earth System Studies, Asheville, NC; and G. Graham, J. Alexander, V. Sudhakar, and M. A. Palecki

679
Weather Intelligence in Support of Developing Enhanced Decision Capabilities and the Modernization of Smart Military Installations
Brendon Hoch, US Army Corps of Engineers (USACE) Engineering Research Development Center (ERDC), Hanover, NH; and H. Bastian, I. Obiako, G. E. Gallarno, C. Rinaudo, J. Richards, R. Buchanan, N. Myers, E. Specking, G. S. Parnell, and M. Marufuzzman

Handout (4.3 MB)

680
Estimating Uncertainty of Water Temperature Predictions for Cold-Stunning Events in the Laguna Madre
Hector Miguel Marrero-Colominas, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and M. Shotande, A. H. Fagg, M. C. White, P. Tissot, and A. McGovern

681
The Application of "Deep Learning" Neural Networks for Sensor Failure Identification in the Kansas Mesonet
Cameron Howard Cousino, Ohio University, Athens, OH; and C. A. Redmond

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