PUBLICATIONS
2023
- 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, -1, In Press, https://arxiv.org/abs/2301.02757.
- 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., -1, In Press, 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 2022: Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy. Wind Energ. Sci. Discuss, in review, [preprint], https://doi.org/10.5194/wes-2022-113.
- 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.
- 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.
- Sha, Y., D. J. Gagne, G. West, R. Stull 2020b: 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.
- Burke, A., N. Snook, D. J. Gagne, S. McCorkle, A. McGovern 2020b: 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.
- Sha, Y., D. J. Gagne, G. West, R. Stull 2020a: 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 2020a: 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.