Overview of 2026 Student Projects

Model Meets Reality (MMR): A Cross-Comparison of DP-SCREAM and WRF Against ARM Observations¶

Powell et al. (2026) prepared DP-SCREAM simulations to pair with WRF sims from LASSO-ShCu for three cases. ARM has dozens of measurements that can be used to examine the representativeness of these simulations. Cross compare the two models and put their differences in context with the observations.

Model Meets Reality Final Presentation

Aerosol-Cloud Interactions: Predicting Cloud Condensation Nuclei from AOS Measurements (Total Smokeshow)¶

Chakraborty et al. (2026) Explainable XGBoost machine learning model for prediction of cloud condensation nucleai (at 10m) from other measurable aerosol quantities.

Total Smokeshow Final Presentation

Aerosol Event Identification using Machine Learning at the Southern Great Plains Site (AIM-SGP)¶

Gary et al. (2026) Random Forest Model Development for automatic daily New Particle Formation (NPF) event classification

AIM-SGP Final Presentation

Atmospheric Radiation Measurement Synergy: Radar, Aerosol, Cloud and Energy (ARMS-Race)¶

Nairy et al. (2026) developed a Random Forest Machine Learning Algorithm to Classify Arctic Cloud Phase at ARM’s North Slope Alaska Facility

ARMS-Race Final Presentation

N-Dimensional Auto-Clustering of Lidar Measurements to define Defacto Aerosol Types¶

Al Mahatab et al. (2026) K-means Clustering Algorithm designed to classify aerosol types within High-Spectral Resolution Lidar (HSRL) Observations

N-Dim Auto Cluster Final Presentation

Race to the Bottom¶

Corner et al. (2026) Three-layer Neural Net with Long Short Term Memory designed to extend the Microwave Radiometers range of precipitable water vapor (PWV) detection.

Race to the Bottom Final Presentation