Research Interests

My research interests are centered around using statistical, data-driven, and machine learning models to study and model a variety of phenomena in weather and climate. I enjoy finding new signals in big, complex, noisy geospatial datasets, and I love thinking about how we as Earth scientists use these tools as much as I love applying them to exciting and relevant problems in weather and climate. Below, you can see some of the recurring themes that have turned up in my work throughout the years.
AI and Tropical Meteorology

Much of my work has been focused on developing machine learning models for various tropical cyclone forecasting tasks. Many of these models are developed with the end goal of producting operational forecast products for use at the National Hurricane Center or elsewhere at NOAA. Some of my work in this area includes:

  • Tropical Cyclone Artificial Neural-network Error Model: A neural network model that predicts tropical cyclone forecast errors for track and intensity; currently in the research to operations pipeline at the National Hurricane Center. See Mark DeMaria's conference talk, Martin Fernandez's paper in Artificial Intelligence in the Earth Systems, and the real-time forecasts at CIRA.
  • TC Structure in Synthetic Microwave Imagery: Using synthetic passive microwave data to study tropical cyclone evolution and structure at high time resolutions that aren’t available with observed satellite imagery. See my 2025 AMS talk; paper to be submitted soon!
  • TCBench: Effort led by the Universite de Lausanne to create a benchmarking dataset for tropical cyclone predictions in AI weather models; see preprint at arXiv
  • Tropical Cyclone Formation Probability: Using AI models to create extended global tropical cyclogenesis forecasts. See 2022 Hollings Scholar Marshall Baldwin's talk and the real-time TCFP page.
  • Causal discovery for hurricane forecasting: Dr. Saranya Ganesh Sudheesh developed a causal discovery model that identified potential new predictors for statistical hurricane intensity forecasts. Her work identified 6 potential new predictors that are currently being tested in existing operational tropical cyclone intensity forecast models; see preprint at arXiv and forthcoming manuscript in Weather and Forecasting.

    • Climate Variability and Change

    I have also given several invited talks at scientific conferences, mostly on the topic of Causality, Interpretability, and Uncertainty:
  • Causality and Uncertainty in Machine Learning for Climate Science". In 2025, I gave a keynote talk at the Gordon Research Conference on Machine Learning for Actionable Climate Science.
  • Causality and Interpretability in Climate Modeling, a talk at the Aspen Global Change Institute's workshop on machine learning for climate modeling.

    • Conference Presentations

    Some of my most recent conference presentations are listed here; a full list can be found in my CV.
  • McGraw, M.C.,, J. Rogers, and H. Hampson: Estimating Flood Risk for an Atlantic Meridional Overturning Circulation Collapse: A Study of Climate Tipping Points in Climate Risk Modeling. Houston Forum on Climate Linked Economics, American Meteorological Society Annual Meeting, Houston, TX. 01/2026.
  • McGraw, M.C., K. Haynes, K.D. Musgrave, I. Ebert-Uphoff, C. Slocum, and J. Knaff: Exploring Tropical Cyclone Structure and Evolution with AI-Based Synthetic Passive Microwave Data. 24th AI Conference, American Meteorological Society Annual Meeting, New Orleans, LA. 01/2025.
  • M. DeMaria, E.A. Barnes, M. Fernandez, R.J. Barnes, M. McGraw, G. Chirokova, L. Lu, P. Santos, and W.A. Hogsett: Applications of a Machine Learning Model for Estimating Tropical Cyclone Track and Intensity Forecast Uncertainty. 36th AMS Conference on Hurricanes and Tropical Meteorology, Long Beach, CA. 05/2024.
  • McGraw, M.C.,, K. Haynes, K.D. Musgrave, I. Ebert-Uphoff, C. Slocum, and J. Knaff: Tropical Cyclone Structure and Evolution in an AI-Based Synthetic Passive Microwave Dataset. 36th AMS Conference on Hurricanes and Tropical Meteorology, Long Beach, CA. 05/2024.
  • Gomez, M.S., M. McGraw, L. Poulain-Auzeau, F. I. H. Tam, S.G. Sudheesh, S.J. Camargo, D.R. Chavas, Y. Cohen, and T. Beucler: TCBench: A Platform for the Data-Driven Prediction of Tropical Cyclones (poster). 36th AMS Conference on Hurricanes and Tropical Meteorology, Long Beach, CA. 05/2024.
  • Beucler, T.G., S. Ganesh Sudheesh, F. I.-H. Tam, M. S. Gomez, M. McGraw, M. DeMaria, K.D. Musgrave, A. Gerhardus, and J. Runge: Causal Feature Selection for Tropical Cyclone Intensity Forecasting. 23rd AI Conference, American Meteorological Society Annual Meeting, Baltimore, MD. 01/2024.
  • McGraw, M.C.,K.D. Musgrave, J.A. Knaff, C.J. Slocum, and I. Ebert-Uphoff: What can machine learning methods tell us about the tropical cyclone intensity forecasting problem? 22nd AI Conference, American Meteorological Society Annual Meeting, Denver, CO. 01/2023.
  • Baldwin, M.R., C.J. Slocum, and M. McGraw: Using AI To Quantify Uncertainty on Tropical Cyclogenesis. Student Conference, American Meteorological Society Annual Meeting, Denver, CO. 01/2023.
  • Haynes, K., R. Lagerquist, M. McGraw, K. Musgrave, and I. Ebert-Uphoff: Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications. 22nd AI Conference, American Meteorological Society Annual Meeting, Denver, CO. 01/2023.
  • McGovern, A., A. Bostrom, D.J. Gagne II, I. Ebert-Uphoff, K. Musgrave, M. McGraw, and R. Chase: Classifying and Addressing Bias in AI/ML for the Earth Sciences. 22nd AI Conference, American Meteorological Society Annual Meeting, Denver, CO. 01/2023.
  • Sospreda-Alfonso, R., Exenberger, J., Dang, K., and M.C. McGraw: Statistical adjustment of decadal climate predictions using deep learning (spotlight presentation). Tackling Climate Change with Machine Learning Workshop, NeurIPS 2022.