My abbreviated CV is available here [full-length version].
McGraw, M.C., K. Haynes, K. Musgrave, and I. Ebert-Uphoff: Tropical Cyclone Structure in Synthetic Microwave Satellite Imgaery, in preparation for submission to Artificial Intelligence in Earth Systems, spring 2025.
Ganesh S., Saranya, F. I.-H. Tam, M.S. Gomez, M. McGraw, M. DeMaria, K. Musgrave, J. Runge, and T. Beucler: Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting, in preparation for submission to Artificial Intelligence in Earth Systems, spring 2025.
Fernandez, M., E.A. Barnes, R.J. Barnes, M. DeMaria, M. McGraw, G. Chirokova, and L. Lu: Predicting tropical cyclone track forecast errors using a probabilistic neural network, accepted at Artificial Intelligence in the Earth Systems, 03/2025. (available on [arXiv])
V. Eyring, W.D. Collins, and coauthors (inc. M. McGraw): Pushing the frontiers in climate modeling and analysis with machine learning. Nature Climate Change, 14, 916-928, doi:s41558-024-02095-y. [link]
McGovern, A., A. Bostrom, M. McGraw, R.J. Chase, D.J. Gagne II, I. Ebert-Uphoff, K. Musgrave, and A. Schumacher: Identifying and categorizing bias in AI/ML for earth sciences. Bull. Amer. Meteorol. Soc., 105, doi:10.117/BAMs-D-23-0196.1 [link]
McGovern, A., and coauthors (inc. M. McGraw) (2023): Trustworthy artificial intelligence for environmental sciences: An innovative approach for summer school. Bull. Amer. Meteorol. Soc., 104, doi:10.1175/BAMS-D-22-0225.1. [PDF]
Haynes, K., R. Lagerquist, M. McGraw, K. Musgrave, I. Ebert-Uphoff (2023): Creating and evaluating uncertainty estimates with neural networks for environmental-science applications, Artificial Intelligence for the Earth Systems, 1, doi:10.1175/AIES-D-22-0061.1. [PDF]
McGraw, M.C., Blanchard-Wrigglesworth, E., Clancy, R.P., and Bitz, C.M. (2022): Understanding the Predictability of Arctic Sea Ice Loss on Subseasonal Timescales. Journal of Climate, 35, doi:10.1175/JCLI-D-21-0301.1. [PDF]
Gonzalez, A.O., I. Ganguly, M.C. McGraw, and J. Larson (2022): Rapid dynamical evolution of ITCZ events over the east Pacific. Journal of Climate, 35,doi:10.1175/JCLI-D-21-0216.1.[PDF]
Clancy, R.P., C.M. Bitz, E. Blanchard-Wrigglesworth, M.C. McGraw, and S. M. Cavallo (2021): Drivers of asymmetric patterns in the atmosphere and sea ice during Arctic cyclones. Journal of Climate, 34, doi:10.1175/JCLI-D-21-0093.1. [PDF]
McGraw, M.C. and E.A. Barnes (2020): New insights on subseasonal Arctic-midlatitude causal connections from a regularized regression model. Journal of Climate, 33, doi:10.1175/JCLI-D-19-0142.1. [PDF; supplemental]
McGraw, M.C., C.F. Baggett, C. Liu, and B.D. Mundhenk (2019): Changes in Arctic moisture transport over the North Pacific associated with sea ice loss. Climate Dynamics, 54, doi:10.1007/s00382-019-05011-9. [PDF]
Samarasinghe, S., M.C. McGraw, E.A. Barnes, and I. Ebert-Uphoff (2019): A study of links between the Arctic and the midlatitude jet-streams using Granger and Pearl causality. Environmentrics, 30, doi:10.1002/env.2540. [PDF]
McGraw, M.C., and E.A. Barnes (2018): Memory matters: A case for Granger causality in climate variability studies. Journal of Climate, 31, doi:10.1175/JCLI-D-17-0334.1. [PDF]
Woollings, T., E. Barnes, B. Hoskins, Y.-O. Kwon, R.W. Lee, C. Li, E. Madonna, M. McGraw, T. Parker, R. Rodrigues, C. Spensbeger, K. Williams (2018): Daily to decadal modulation of jet variability. Journal of Climate, 31, doi:10.1175/JCLI-D-17-0286.1. [PDF]
Samarasinghe, S., M. McGraw, E. Barnes, and I. Ebert-Uphoff (2017): A study of causal links between the Arctic and the midlatitude jet-streams. Proceedings of the Seventh International Workshop on Climate Informatics (CI 2017), NCAR Technical Note NCAR/TN-536+PROC.
McGraw, M.C., E.A. Barnes, and C. Deser (2016): Reconciling the observed and modeled Southern Hemisphere circulation response to volcanic eruptions. Geophys. Res. Lett.,, L069835, doi:10.1002/2016GL069835. [PDF]
McGraw, M.C., and E.A. Barnes (2016): Seasonal sensitivity of the eddy-driven jet to tropospheric heating in an idealized AGCM. J. Climate, 29, doi:10.1175/JCLI-D-15-0723.1. [PDF]