PUBLICATIONS
2024
- Da Fan, Steven J. Greybush, Eugene E. Clothiaux, David John Gagne II 2024: Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations. Artificial Intelligence for the Earth Systems, -1, In Press, https://doi.org/10.1175/AIES-D-23-0098.1.
- John Schreck, David John Gagne, Charlie Becker, William Chapman, Kimberly Elmore, Da Fan, Gabrielle Gantos, Eliot Kim, Dhamma Kimpara, Thomas Martin, Maria J. Molina, Vanessa M. Pryzbylo, Jacob Radford, Belen Saavedra, Justin Willson, Christopher Wirz 2024: Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications. Artificial Intelligence for the Earth Systems, , Under Review, https://arxiv.org/abs/2309.13207.
- Colfescu, Ioana, Christensen, Hannah, Gagne, David John 2024: A Machine Learning-Based Approach to Quantify ENSO Sources of Predictability. Geophysical Research Letters, 51, e2023GL105194, https://doi.org/10.1029/2023GL105194.
- McGovern, A., R. J. Chase, M. Flora, D. J. Gagne, R. Lagerquist, C. Potvin, N. Snook, E. Loken 2023: A review of machine learning for convective weather. Artificial Intelligence for the Earth Systems, 2, e220077, https://doi.org/10.1175/AIES-D-22-0077.1.
- McGovern, A., D. J. Gagne, C. D. Wirz, I. Ebert-Uphoff, A. Bostrom, Y. Rao, A. Schumacher, R. Chase, M. Flora, A. Mamalakis, M. McGraw, R. Lagerquist, R. Redmon, T. Peterson 2023: Trustworthy artificial intelligence for environmental sciences: An innovative approach for summer school. Bull. Amer. Meteor. Soc, 104, E1222–E1231, https://doi.org/10.1175/BAMS-D-22-0225.1.
- Schreck, J. S., M. Hayman, G. Gantos, A. Bansemer, D. J. Gagne 2023: Mimicking non-ideal instrument behavior for hologram processing using neural style translation. Optics Express, 31, 20049-20067, https://doi.org/10.1364/OE.486741.
- Sobash, R. A., D. J. Gagne, C. L. Becker, D. Ahijevych, G. Gantos, C. S. Schwartz 2023: Diagnosing storm mode with deep learning in convection-allowing models. Mon. Wea. Rev., 151, 2009–2027, https://doi.org/10.1175/MWR-D-22-0342.1.
- Haupt, S. E., B. Kosovic, L. K. Berg, C. M. Kaul, M. Churchfield, J. Mirocha, D. Allaerts, T. Brummet, S. Davis, A. DeCastro, S. Dettling, C. Draxl, D. J. Gagne, P. Hawbecker, P. Jha, T. Juliano, W. Lassman, E. Quon, R. Rai, M. Robinson, W. Shaw, R. Thedin 2023: Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy. Wind Energ. Sci. Discuss, 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023.
- Dueben, P., M. G. Schultz, M. Chantry, D. J. Gagne, D. M. Hall, A. McGovern 2022: Challenges and benchmark datasets for machine learning in the atmospheric sciences: Definition, status and outlook. Artificial Intelligence for the Earth Systems, 1, e210002, https://doi.org/10.1175/AIES-D-21-0002.1.
- Schreck, J. S., G. Gantos, M. Hayman, A. Bansemer, D. J. Gagne 2022: Neural network processing of holographic images. Atmospheric Measurement Technologies, 2022, 1--38, https://doi.org/10.5194/amt-15-5793-2022.
- Schreck, J. S., C. Becker, D. J. Gagne, K. Lawrence, S. Wang, C. Mouchel-Vallon, J. Choi, A. Hodzic 2022: Neural network emulation of the formation of organic aerosols based on the explicit gecko-a chemistry model. Journal of Advances in Modeling Earth Systems, , e2021MS002974, https://doi.org/10.1029/2021MS002974.
- T. McCandless, D. J. Gagne, B. Kosovic, S. E. Haupt, B. Yang, C. Becker, J. Schreck 2022: Machine Learning for Improving Surface Layer Flux Estimates. Boundary Layer Meteorology, 185, 199--228, https://doi.org/10.1007/s10546-022-00727-4.
- Muñoz-Esparza, D., C. Becker, J. Sauer, D. J. Gagne, J. Schreck, B. Kosovic 2022: On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model. JGR Atmospheres, 127, e2021JD036214, https://doi.org/10.1029/2021JD036214.
- McGovern, A., I. Ebert-Uphoff, D. J. Gagne, A. Bostrom 2022: Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science. Environmental Data Science, 1, e6, https://doi.org/10.1017/eds.2022.5.
- McGovern, A., A. Bostrom, P. Davis, J. L. Demuth, I. Ebert-Uphoff, R. He, J. Hickey, D. J. Gagne, N. Snook, J. Q. Stewart, C. Thorncroft, P. Tissot, J. Williams 2022: NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). Bulletin of the American Meteorological Society, 103, E1658--E1668, https://doi.org/10.1175/BAMS-D-21-0020.1.
- Y. Sha, D. J. Gagne, G. West, R. Stull 2022: A hybrid analog-ensemble, convolutional-neural-network method for post-processing precipitation forecasts. Monthly Weather Review, 150, 1495--1515, https://doi.org/10.1175/MWR-D-21-0154.1.
- Haupt, S. E., D. J. Gagne, W. Hsieh, V. Krasnopolsky, V. Lakshmanan, A. McGovern, C. Marzban, W. Moninger, P. Tissot, J. Williams 2022: The History and Practice of AI in the Environmental Sciences. Bulletin of the American Meteorological Society, 103, E1351--E1370, https://doi.org/10.1175/BAMS-D-20-0234.1.
- Foster, D., D. J. Gagne, D. B. Whitt 2021: Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations. Journal of Advances in Modeling Earth Systems, 13, e2021MS002474, https://doi.org/10.1029/2021MS002474.
- Molina, M., D. J. Gagne, A. Prein 2021: Deep learning classification of potentially severe convective storms in a changing climate. Earth and Space Science, 8, e2020EA001 490, https://doi.org/10.1029/2020EA001490.
- Sha, Y., D. J. Gagne, G. West, R. Stull 2021: Deep-learning-based precipitation observation quality control. Journal of Atmospheric and Oceanic Technology. Earth and Space Science, 38, 1075–-1091, https://doi.org/10.1175/JTECH-D-20-0081.1.
- Gettelman, A., D. J. Gagne, C.-C. Chen, M. Christensen, Z. Lebo, H. Morrison, G. Gantos 2021: Machine Learning the Warm Rain Process. Journal of Advances in Modeling Earth Systems, 13, e2020MS002 268, https://doi.org/10.1029/2020MS002268.
- Sha, Y., D. J. Gagne, G. West, R. Stull 2020: Deep-learning-based gridded downscaling of surface meteorological variables in complex terrain. part i: daily maximum and minimum 2-m temperature. Journal of Applied Meteorology and Climatology, 59, 2057–-2073, https://doi.org/10.1175/JAMC-D-20-0057.1.
- Sha, Y., D. J. Gagne, G. West, R. Stull 2020: Deep-learning-based gridded downscaling of surface meteorological variables in complex terrain - part ii: daily precipitation. Journal of Applied Meteorology and Climatology, 59, 2075–-2092, https://doi.org/10.1175/JAMC-D-20-0058.1.
- Gagne, D. J., H. Christensen, A. Subramanian, A. Monahan 2020: Machine learning for stochastic parameterization: Generative adversarial networks in the lorenz ’96 model. Journal of Advances in Modeling Earth Systems, 12, 2075–-2092, https://doi.org/10.1029/2019MS001896.
- Lagerquist, R., A. McGovern, C. Homeyer, D. J. Gagne, T. Smith 2020: Deep learning on three-dimensional multiscale data for next-hour tornado prediction. Mon. Wea. Rev., 148, 2837–-2861, https://doi.org/10.1175/MWR-D-19-0372.1.
- Burke, A., N. Snook, D. J. Gagne, S. McCorkle, A. McGovern 2020: Calibration of machine learning-based probabilistic hail predictions for operational forecasting. Wea. Forecasting, 35, 149–-168, https://doi.org/10.1175/WAF-D-19-0105.1.
- McGovern, A., R. Lagerquist, D. J. Gagne, G. E. Jergensen, K. Elmore, C. Homeyer, T. Smith 2019: Making the black box more transparent: Understanding the physical implications of machine learning. Bull. Amer. Meteor. Soc., 100, 2175–-2199, https://doi.org/ 10.1175/BAMS-D-18-0195.1.
- Lagerquist, R., A. McGovern, D. J. Gagne 2019: Deep learning for spatially explicit prediction of synoptic-scale fronts. Wea. Forecasting, 34, 1137–-1160, https://doi.org/10.1175/ WAF-D-18-0183.1.