Joshua S. Fu

Multisource Observation-Constrained Tuning for Air Quality Forecasting in a Machine Learning-Enhanced NOAA Unified Forecast System

This work presents an observation-driven machine learning framework that integrates multisource satellite products, including VIIRS aerosol optical depth and TEMPO NO2 with U.S. EPA AirNow measurements into a machine-learning surrogate of the NOAA Unified Forecast System (DeepAQM).