☁ Submitted for Peer Review (group members in bold, student/postdoc indicated with *asterisk)
*Chan, M. A., M. J. Molina, and C. A. Metzler (Under Review). Hyper-Diffusion: Estimating Epistemic and Aleatoric Uncertainty with a Single Model. [Preprint]
Ebert-Uphoff, I., L. Ver Hoef, J. S. Schreck, J. Stock, M. J. Molina, A. McGovern, M. Yu, B. Petzke, K. Hilburn, D. Hall, D. J. Gagne, and S. Scheuerman. An Investigation of Metrics to Evaluate the Sharpness in AI-Generated Meteorological Imagery. [Preprint]
Molina, M. J., B. DePodwin, E. Smith, K. Putsavage, M. Behl, T. Washington, K. Goebbert, M. Lacke, and M. Glackin (Under Review). AMS Early Career Leadership Academy: From Idea to Reality to Evolution. Bulletin of the American Meteorological Society.
Schreck, J. S., D. J. Gagne II, C. Becker, W. E. Chapman, K. Elmore, G. Gantos, E. Kim, D. Kimpara, T. Martin, M. J. Molina, V. M. Pryzbylo, J. Radford, B. Saavedra, J. Willson, C. Wirz (Under Review). Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications. Artificial Intelligence for the Earth Systems. [Preprint]
Eyring, V., W. D. Collins, P. Gentine, E. A. Barnes, M. Barreiro, T. Beucler, M. Bocquet, C. S. Bretherton, H. M. Christensen, K. Dagon, D. J. Gagne, D. Hall, D. Hammerling, S. Hoyer, F. Iglesias-Suarez, I. Lopez-Gomez, M. C. McGraw, G. A. Meehl, M. J. Molina, C. Monteleoni, J. Mueller, M. S. Pritchard, D. Rolnick, J. Runge, P. Stier, O. Watt-Meyer, K. Weigel, R. Yu, and L. Zanna (Under Review). Pushing the Frontiers in Climate Modeling and Analysis with Machine Learning. Nature Climate Change.
☁ Peer-Reviewed Journal Publications
2024
Fasullo, J., J. C. Golaz, J. Caron, N. Rosenbloom, G. Meehl, W. Strand, S. Glanville, S. Stevenson, M. J. Molina, C. Shields, C. Zhang, J. Benedict, and T. Bartoletti (2024). An Overview of the E3SM version 2 Large Ensemble and Comparison to other E3SM and CESM Large Ensembles. Earth System Dynamics. [Link]
*Campbell, T., G. M. Lackmann, M. J. Molina, and M. D. Parker (2024). Severe Convective Storms in Limited Instability Organized by Pattern and Distribution. Weather and Forecasting. [Link]
2023
Shah, S. H., C. O'Lenick, A. Ramos Valle, J. Wan, O. Wilhelmi, K. Ash, C. M. Edgeley, M. J. Molina, J. Moulite, C. C. Pizzaro, K. Emard, O. Cameron, J. Done, C. W. Hazard, T. Hopson, M. Jones, F. Lacey, M. A. Lachaud, D. Lombardozzi, M. Mendez, R. Morss, K. Ricke, F. Tormos-Aponte, W. Wieder, and C. Williams (2023). Connecting Physical and Social Science Datasets: Challenges and Pathways Forward. Environmental Research Communications. [Link]
DuVivier, A. K., M. J. Molina, A. L. Deppenmeier, M. M. Holland, L. Landrum, K. Krumhardt, and S. Jenouvrier (2023). Projections of Winter Polynyas and Their Biophysical Impacts in the Ross Sea Antarctica. Climate Dynamics. [Link]
Molina, M. J., T. A. O'Brien, G. Anderson, M. Ashfaq, K. E. Bennett, W. D. Collins, K. Dagon, J. M. Restrepo, and P. A. Ullrich (2023). A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena. Artificial Intelligence for the Earth Systems. [Link]
Morales, A., M. J. Molina, J. E. Trujillo-Falcon, K. M. Nunez Ocasio, A. L. Lang, E. Murillo, C. Bieri, B. S. Barrett, L. B. Aviles, and S. J. Camargo (2023). Commitment to Active Allyship is Required to Address the Lack of Hispanic and Latinx Representation in the Earth and Atmospheric Sciences. Bulletin of the American Meteorological Society. [Link]
Molina, M. J., J. H. Richter, A. A. Glanville, K. Dagon, J. Berner, A. Hu, and G. A. Meehl (2023). Subseasonal Representation and Predictability of North American Weather Regimes using Cluster Analysis. Artificial Intelligence for the Earth Systems. [Link]
2022
Dagon, K., J. Truesdale, J. C. Biard, K. E. Kunkel, G. A. Meehl, and M. J. Molina (2022). Machine learning-based detection of weather fronts and associated extreme precipitation in historical and future climates. Journal of Geophysical Research: Atmospheres. [Link]
Morales, A., L. Medina Luna, D. Zietlow, J. E. LeBeau, and M. J. Molina (2022). Testing the Impact of Culturally-Relevant Communication Style on Engagement with Hispanic and Latinx Adults. Journal of Geoscience Education. [Link]
Yeager, S. G., N. Rosenbloom, A. A. Glanville, X. Wu, I. Simpson, H. Li, M. J. Molina, K. Krumhardt, S. Mogen, K. Lindsay, D. Lombardozzi, W. Wieder, W. Kim, J. H. Richter, M. Long, G. Danabasoglu, D. Bailey, M. Holland, N. Lovenduski, W. G. Strand, and T. King (2022). The Seasonal-to-Multiyear Large Ensemble (SMYLE) Prediction System using the Community Earth System Model Version 2. Geoscientific Model Development. [Link]
Tye, M. R., K. Dagon, M. J. Molina, J. H. Richter, D. Visioni, B. Kravitz, and S. Tilmes (2022). Indices of Extremes: Geographic patterns of change in extremes and associated vegetation impacts under climate intervention. Earth System Dynamics. [Link]
Molina, M. J., A. Hu, and G. A. Meehl (2022). Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations. Journal of Climate. [Link]
2021
Molina, M. J., D. J. Gagne, and A. F. Prein (2021). A benchmark to test generalization capabilities of deep learning methods to classify severe convective storms in a changing climate. Earth and Space Science. [Link]
Hu, A., G. A. Meehl, N. Rosenbloom, M. J. Molina, and W. G. Strand (2021). The influence of variability in meridional overturning on global ocean circulation. Journal of Climate. [Link]
Poujol, B., A. F. Prein, M. J. Molina, and C. Muller (2021). Dynamic and thermodynamic impacts of climate change on organized convection in Alaska. Climate Dynamics, 1-25. [Link]
2020
Molina, M. J., J. T. Allen, and A. F. Prein (2020). Moisture Attribution and Sensitivity Analysis of a Winter Tornado Outbreak. Weather and Forecasting, 35(4), 1263-1288. [Link]
Molina, M. J., and J. T. Allen (2020). Regionally-stratified tornadoes: Moisture source physical reasoning and climate trends. Weather and Climate Extremes, 28, 100244. [Link]
2016-2019
Molina, M. J., and J. T. Allen (2019). On the moisture origins of tornadic thunderstorms. Journal of Climate, 32(14), 4321-4346. [Link]
Molina, M. J., J. T. Allen, and V. A. Gensini (2018). The Gulf of Mexico and ENSO influence on subseasonal and seasonal CONUS winter tornado variability. Journal of Applied Meteorology and Climatology, 57(10), 2439-2463. [Link]
Allen, J. T., M. J. Molina, and V. A. Gensini (2018). Modulation of annual cycle of tornadoes by El Niño–Southern Oscillation. Geophysical Research Letters, 45(11), 5708-5717. [Link]
Molina, M. J., R. P. Timmer, and J. T. Allen (2016). Importance of the Gulf of Mexico as a climate driver for US severe thunderstorm activity. Geophysical Research Letters, 43(23), 12-295. [Link]
☁ Other Technical and Concept Papers
2021-2023
Mayer, K., K. Dagon, and M. J. Molina (2023). Identifying Tropical State-Dependent Bias Relevant to Midlatitude Subseasonal Predictability with Explainable Neural Networks. S2S Newsletter No. 23. [Link]
Molina, M. J., E. Smith, T. Washington, D. Schvartzman, A. Morrison, C. Brinkworth, K. Aponte, K. Goebbert, M. Augustyniak, and K. Putsavage (2023). The AMS Early Career Leadership Academy: Training Leaders for a more Equitable and Inclusive Future, Bulletin of the American Meteorological Society, 45Beacon, EIJ Column. [Link]
Molina, M. J., T. A. O’Brien, G. Anderson, M. Ashfaq, K. E. Bennett, W. Collins, S. Collis, K. Dagon, S. Klein, J. M. Restrepo, and P. A. Ullrich (2022). DOE AI for Earth System Predictability Workshop Report, Chapter 8: Climate Variability and Extremes, 186-201. [Link]
Molina, M. J., J. Richter, J. Berner, A. A. Glanville, K. Dagon, A. Jaye, A. Hu, and G. Meehl (2022). Deep learning for subseasonal precipitation and temperature errors. Climate prediction S&T digest: NWS science & technology infusion climate bulletin supplement. [Link]
Dagon, K., M. J. Molina, G. A. Meehl, J. H. Richter, E. A. Barnes, J. Berner, J. M. Caron, W. Chapman, G. Danabasoglu, D. J. Gagne, S. Glanville, S. E. Haupt, A. Hu, Z. Martin, K. Mayer, K. Pegion, K. Raeder, I. Simpson, A. Subramanian, and S. Yeager (2021). Machine learning to extend and understand the sources and limits of water cycle predictability on subseasonal-to-decadal timescales in the Earth system. DOE Concept Papers to Advance an Integrative Artificial Intelligence Framework for Earth System Predictability: AI4ESP. [Link]
2018-2020
Ahmed, N., M. Slipski, I. Venzor-Cardenas, M. J. Molina, G. Senay, M. Cheung, C. Tillier, S. Edgington, and G. Renard (2020). Leveraging Lightning with Convolutional Recurrent AutoEncoder and ROCKET for Severe Weather Detection. Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), AI4EARTH. [Link]
Slipski, M, I. Venzor-Cardenas, M. J. Molina, N. Ahmed, M. Cheung, C. Tillier, S. Edgington, and G. Renard (2020). Predicting severe thunderstorms with machine learning and the Geostationary Lightning Mapper. Frontier Development Lab Technical Memorandum. [Link]
Molina, M. J., J. T. Allen, and V. A. Gensini (2018). Gulf of Mexico influence on sub-seasonal and seasonal severe thunderstorm frequency. NOAA Climate Prediction S&T Digest: National Weather Service science & technology infusion climate bulletin supplement, 42-45. [Link]
