Arcodia Research Group
Dr. Marybeth Arcodia
Assistant Professor
Background:
Dr. Marybeth Arcodia earned a Bachelor of Arts in Mathematics from Georgetown University in 2014 before completing a Ph.D. in Atmospheric Science at the University of Miami Rosenstiel School of Marine, Atmospheric and Earth Science in 2021. She is now an Assistant Professor joint between the Rosenstiel School of Marine, Atmospheric, and Earth Sciences Department of Atmospheric Sciences and the Frost Institute for Data Science and Computing at the University of Miami.
Research and Involvement:
Her research bridges Earth system predictability and prediction, integrating atmospheric science and AI-based techniques to explore variability and change across weather-to-climate scales. Her work focuses on localized impacts in future climates to further our understanding of the stressed climate system and aid in advancing preparedness for climate risk. She is a member of the US CLIVAR Predictability, Predictions, and Applications Interface Panel and the Working Group on Climate Data and Predictions for Coastal Solutions.
Postdoctoral Research Fellows
Chi-Jui Chen
Forecasting rainfall three to four weeks out is one of the hardest problems in weather and climate — too far ahead for day-to-day weather, too soon for seasonal signals to take over. Chi-Jui uses AI/machine learning to predict it as a full probability distribution, and to spot "forecasts of opportunity": the moments when the climate system is especially predictable. The aim is practical guidance — telling forecasters not just what to expect, but when and where to trust it.
Graduate Students
2nd Year Graduate Student
Juliette Rocha
Juliette's project examines whether ocean surface conditions in the Gulf of Mexico and Caribbean Sea can improve subseasonal (weeks 1–4) summer precipitation predictions for the United States Midwest. Using explainable neural networks trained on varied ocean surface layers (0–1 m to 0–10 m). She is evaluating forecast skill using sea surface temperature and salinity anomalies as predictors, while exploring the physical mechanisms linking ocean-atmosphere-land interactions to Midwest precipitation variability.
julietterocha@miami.edu
1st Year Graduate Student
Nimay Mahajan
Nimay's initial research used a CNN paired with explainable AI (XAI) to predict Week 3-4 subseasonal precipitation in Miami, aiming both to improve forecast skill and to pinpoint regions that serve as sources of predictability. He's now extending this work to test whether those XAI-flagged regions reflect physically plausible climate mechanisms. To do so, he's developing a hybrid CNN-Vision Transformer architecture to identify predictability sources the original CNN may have missed and to compare attribution patterns between the two models.
1st Year Graduate Student
Paul Harmon
Paul's project investigates whether XGBoost can improve subseasonal (weeks 1–6) forecasts of moderate heat stress onset across Florida's Coral Reef. Using physically meaningful site-specific and regional/global atmospheric-oceanic predictors, he evaluates this explainable machine learning approach against a logistic regression model and a historical frequency-based baseline, and applies SHAP to identify the physical mechanisms driving predictions across reef sites and lead times.
pmh107@miami.edu
Master Students
Taylor Korte (MPS)
Taylor's project aims to understand how sea surface temperature variabilities and anomalies are affecting sea turtle nesting in South Florida. By using long-term sea surface temperature data and sea turtle nesting data, correlation analysis will be ran to identify any relationship between the two.
