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A31K: Spatiotemporal AI Methods for Analyzing Aerosol, Clouds, and Air Pollution Poster

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Primary Convener:
Qian Liu, University of Missouri Columbia

Convener:
Manzhu Yu, Penn State University
Jeffrey Wood, University of Missouri

Chair:
Qian Liu, University of Missouri Columbia
Manzhu Yu, Penn State University

Atmospheric factors such as aerosols, clouds, and air pollution play critical roles in shaping global weather and climate patterns. Accurate and comprehensive analysis of these components is essential for numerical weather prediction and daily life applications. With advancements in computational resources, Artificial Intelligence (AI) and sophisticated spatiotemporal methods are transforming the study of atmospheric processes.This session invites contributions that explore innovative AI techniques, machine learning models, spatiotemporal analysis, and data-driven approaches for examining satellite and ground-based observations, numerical simulations, and in situ measurements. We encourage discussions on novel methodologies aimed at improving air quality forecasting, understanding aerosol-cloud interactions, and assessing climate impacts. Studies employing deep learning, data fusion, and spatiotemporal modeling to advance our knowledge of atmospheric dynamics are particularly welcome. Join us to explore cutting-edge advancements and interdisciplinary applications in environmental research.

Index Terms
0305 Aerosols and particles
0315 Biosphere|atmosphere interactions
0321 Cloud|radiation interaction
0345 Pollution: urban and regional

Suggested Itineraries:
Machine Learning and AI
Global Impacts‚ Solutions‚ & Policies
Environmental Justice

Neighborhoods:
3. Earth Covering

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Suggested Itineraries

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Discussion

Qian Liu

A31K-2192: Advancing Spatiotemporal PM2.5 Forecasts and Air Quality Alerts in Texas via Bidirectional LSTM and Double Kolmogorov-Arnold Networks

A31K-2193: A Deep Learning model for Sub-pixel cloud Detection of OMPS Nadir Mapper Imagery

A31K-2194: Satellite PM2.5 Retrieval Using Machine Learning Models – Maryland Case Studies

A31K-2195: Machine Learning Approaches to Analyze and Predict Particulate Matter Concentrations in Delhi–NCR

A31K-2196: Enhancing Spatiotemporal Continuity of TEMPO Tropospheric Nitrogen Dioxide via Gaussian Process Regression Gap-filling

A31K-2197: Seeing the Unseen: Satellite-Based Fog Variability over the Czech Republic (2006–2021)

A31K-2198: Evaluating PM2.5 Estimation Errors over South Korea: A Comparison between MPAS-CMAQ and WRF-CMAQ

A31K-2199 WITHDRAWN: Hybrid Transfer Learning for PM2.5 Estimation in Regions with Sparse Ground-Based Data: A Scalable Framework

A31K-2200: From Static to Dynamic: How Regular Retraining Improves Machine Learning Air Pollution Forecasts, illustrated with the NASA GEOS-CF use case.

A31K-2201: Improving Real-Time Air Quality Forecasting through Bias-Correction and Machine Learning over Southeastern U.S.

WITHDRAWN: DustCast: An Ensemble Machine Learning Model for Monthly Atmospheric Dust Aerosol Forecasting Across the Arabian Peninsula.

[ONLINE] A31K-VR8855: Spatio-Temporal forecast of PM2.5 over the Indian Subcontinent using an advanced Machine learning algorithm

[ONLINE] A31K-VR8856: Calibrating Low-Cost Air Quality Sensors for High Accuracy PM2.5 Measurements Using Machine and Deep Learning, Enabling Monitoring of Air Quality for a Wider Range of Geographic Regions

[ONLINE] A31K-VR8857: Enhancing Low-Cost Sensor Reliability for Air Quality Monitoring using Deep Learning and Monte Carlo-based Uncertainty Quantification

[ONLINE] A31K-VR8858: Evaluation of Spatiotemporal Heterogeneity in PM2.5 Forecasting Using ST-Transformer: Western US as a Case Study