Regime Characterization of Offshore Wind Resource
Self-Organizing Maps used to classify offshore wind regimes and assess siting risk.
Self-Organizing Maps used to classify offshore wind regimes and assess siting risk.
Geospatial analysis and wake modeling to identify optimal siting within California offshore wind energy areas.
Evaluation of CERES SYN1deg radiative flux profiles against ARM ENA observations.
Wind and weather variability assessment for California offshore wind energy areas to support siting and planning.
Multi-decade trend analysis of MISR and MODIS cloud products to study climate variability in cloud properties.
MISR–MODIS fusion algorithm for multilayer cloud-top heights, validated against independent observations.
Validation of MISR and MODIS cloud-top height retrievals against ISS-CATS lidar to quantify uncertainties.
This project developed one of the earliest neural-network–based ionospheric models capable of predicting global F2-layer peak density (NmF2) and peak height (hmF2) from long-term satellite and ground-based measurements. The work demonstrated that machine-learning approaches can replicate large-scale ionospheric electrodynamics traditionally captured only by empirical or physics-based models.