Session Artificial Intelligence for Environmental Science


Description:

This session is open to a wide variety of research, active work, and advancements in applications of artificial intelligence or machine learning methods and models to environmental science.


Papers:
Uncovering the drivers of urban air pollution using spatial machine learning for improved analysis and forecasting
Firas Gerges, Princeton University, Princeton, NJ; and M. Llaguno-Munitxa, M. A. Zondlo, M. Boufadel, and E. Bou-Zeid

Extreme Value Analysis of K-Means Single (k=1) Cluster Statistical Distances, a Heuristic Approach with Los Angeles and San Francisco Hourly Temperatures and Monthly Precipitation
Charles J. Fisk, Individual, Newbury Park, CA

Can AI Models Perform Dynamical Downscaling? Training Deep Generative Models to Downscale ERA5 Variables to High-Resolution COSMO-CLM Dynamical Reanalyses over Italy
Elena Tomasi, Fondazione Bruno Kessler - FBK, Trento, TN, Italy; and G. Franch and M. Cristoforetti

Development of a Prediction Method for Weather Types using a Deep Neural Network
Kazuki Tanaka, Japan Meteorological Agency, Tsukuba, Japan; and T. T. Sekiyama

Physics-informed Machine Learning Methods for Post-processing Weather Elements
Gurvir Kaur Bawa, Embry-Riddle Aeronautical University, Daytona Beach, FL; Embry-Riddle Aeronautical University, Daytona Beach, FL

FogNet-V2: Multi-view Tensorized Transformer for Coastal Fog Forecasting
Hamid kamangir, Texas A&M University-Corpus Christi, Corpus Christi, TX; Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and E. Krell, W. G. Collins, P. Tissot, S. A. King, and D. J. J. Gagne II

Using deep learning to predict cloud-to-ground lightning in the western United States
Dmitri Kalashnikov, Washington State University Vancouver, Vancouver, WA; and F. V. Davenport, Z. Labe, P. C. Loikith, J. Abatzoglou, and D. Singh

RainForests: A machine-learning approach to calibrating NWP precipitation forecasts
Belinda Trotta, BoM, Melbourne, VIC, Australia; and B. Owen, J. Liu, G. Weymouth, T. Gale, T. Hume, A. Schubert, J. Canvin, D. Mentiplay, and J. Whelan

True Positive Convective Storms Without Lightning in ThunderCast
Stephanie M. Ortland, University of Wisconsin-Madison, Madison, WI; Univ. of Wisconsin -- Madison, Madison, WI; and M. J. Pavolonis and J. L. Cintineo

Artificial Intelligence-based Investigation of Severe Thunderstorms Producing Downbursts in Canada.
Mohammad Hadavi, McGill University, Montreal, QC, Canada; and D. Romanic

Using Machine Learning to Produce Watch-to-Warning Severe Weather Guidance
Samuel Varga, University of Oklahoma, Norman, OK; Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, OK; National Severe Storms Laboratory, Norman, OK; and M. L. Flora and C. K. Potvin

Understanding Instabiltities of Neural Weather Models: Towards seamless weather to climate prediction
Ashesh K Chattopadhyay, Rice Univ., Houston, TX; and P. Hassanzadeh

An AI/ML Method for the Retrieval of Similar Weather Events Using Geostationary Satellite: COMS & GK2A
Byeongyeon Kim, National Institute of Meteorological Science, Seogwipo-si, Jeju-do, South korea; and Y. H. Baek, H. Shin, S. W. Ko, C. Yang, H. Lee, S. H. Kim, C. Han, S. Kim, G. Lee, M. Choe, H. M. Park, and K. Shin

Machine Learning for Predicting Air Quality Using Land Images
Jing Wang, Princeton University, Princeton, NJ; and F. Gerges and E. Bou-Zeid

Exploring Cross-Validation Techniques for ML Predictions of Rare Cold-Stunning Events
Jarett woodall, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and M. White, H. Marrero, M. Vicens-Miquel, and P. E. Tissot

Flood Detection and Mapping Through Sensor Fusion: A Comparative Study of Multi-Sensor Data Integration with UAV Optical Imagery
Mulham Fawakherji, north carolina agricultural and technical state university, Greensboro, NC; and L. Hashemi Beni

Submission Deadline: Thursday, 31 August 2023



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