Nicole Riemer
Machine learning for error quantification in simulating the climate impacts of atmospheric aerosols
(basl)Jun 2018 - Dec 2019
2021
Zhonghua Zheng, Jeffrey H. Curtis, Yu Yao, Jessica T. Gasparik, Valentine G. Anantharaj, Lei Zhao, Matthew West, and Nicole Riemer (2021): Estimating Submicron Aerosol Mixing State at the Global Scale with Machine Learning and Earth System Modeling, Earth and Space Science, American Geophysical Union, Vol 8, Num 2, ppe2020EA001500
Zhonghua Zheng, Matthew West, Lei Zhao, Po-Lun Ma, Xiaohong Liu, and Nicole Riemer (2021): Quantifying the Structural Uncertainty of the Aerosol Mixing State Representation in a Modal Model, Atmospheric Chemistry and Physics, Copernicus GmbH, Vol 21, Num 23, pp17727--17741
2020
J. T. Gasparik, Q. Ye, J. H. Curtis, A. A. Presto, N. M. Donahue, R. C. Sullivan, M. West. and N. Riemer (2020): Quantifying errors in the aerosol mixing-state index based on limited particle sample size, Aerosol Science and Technology, Taylor & Francis, Vol 54, Num 12, pp1527-1541
2019
Nicole Riemer (2019): Machine Learning for Error Quantification in Simulating the Climate Impacts of Atmospheric Aerosols, 2019 Blue Waters Annual Report, pp102-103
2018
Matthew West and Nicole Riemer (2018): Using a 3D Particle-Resolved Aerosol Model to Quantify and Reduce Uncertainties in Aerosol-Atmosphere Interactions, 2018 Blue Waters Annual Report, pp74-75
Zhonghua Zheng: Coarse-Graining of Aerosol Mixing State Metrics Empowered by Machine Learning
International Aerosol Modeling Algorithms Conference 2019; Davis, California, U.S.A., Dec 5, 2019
Nicole Riemer: Quantifying Errors in Aerosol Mixing State Metrics due to Limited Particle Sample Size
American Association for Aerosol Research 37th annual conference; Portland, Oregon, U.S.A., Oct 17, 2019
Zhonghua Zheng: Machine Learning to Predict Multi-Aerosol Mixing State Metrics
99th American Meteorological Society Annual Meeting, 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences; Phoenix, Arizona, U.S.A., Jan 7, 2019
Zhonghua Zheng: A Machine Learning Approach to Estimate Multi-Aerosol Mixing State Metrics at a Global Scale in Earth System Models
American Geophysical Union (AGU) Fall 2018 Meeting; Washington, D.C., U.S.A., Dec 10, 2018
