Education
PhD Atmospheric Sciences -- University of Washington (2020)MS Applied Mathematics -- University of Washington (2019)
MS Atmospheric Sciences -- University of Washington (2016)
BS Applied Computational Mathematics in Science -- University of Washington (2012)
BS Atmospheric Sciences, Atmospheric Chemistry -- University of Washington (2012)
Employment
2022-Present: Data Scientist, PNNL, Richland, Washington2020-2021: Postdoctoral Research Associate, PNNL, Richland, Washington
2013-2020: Research Assistant, University of Washington, Seattle, Washington
2007-2013: Research Assistant, NorthWest Research Associates, Redmond, Washington
Peer Reviewed Publications
Geiss, A., M. W. Christensen, A. C. Varble, T. Yuan, and H. Song (2024) Self-Supervised Cloud Classification, Artif. Intell. Earth Syst., 3, e230036, https://doi.org/10.1175/AIES-D-23-0036.1.
Geiss, A., Ma, P.-L., Singh, B., and Hardin, J. C.: Emulating Aerosol Optics with Randomly Generated Neural Networks, EGUsphere, (preprint - in review), https://doi.org/10.5194/egusphere-2022-559, 2022.
Geiss, A., Silva, S., and Hardin, J.: Downscaling Atmospheric Chemistry Simulations with Physically Consistent Deep Learning, Geosci. Model Dev. Discuss., (preprint - accepted), https://doi.org/10.5194/gmd-2022-76, 2022.
Geiss, A. and Hardin, J. C.: Inpainting radar missing data regions with deep learning, Atmos. Meas. Tech., 14, 7729–7747, https://doi.org/10.5194/amt-14-7729-2021, 2021.
Geiss, A. and Hardin, J. C. (2020) Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets arXiv, (preprint - in review), https://arxiv.org/abs/2011.05586
Geiss, A. and Hardin, J. C. (2020) Radar Super Resolution using a Deep Convolutional Neural Network. Journal of Atmospheric and Oceanic Technology, 37-12: 2197-2207, https://doi.org/10.1175/JTECH-D-20-0074.1
Geiss, A., Marchand, R., and Thompson, L. (2020) The Influence of Sea Surface Temperature Reemergence on Marine Stratiform Cloud. Geophysical Research Letters, https://doi.org/10.1029/2020GL086957
Geiss, A. and Marchand, R. (2019) Cloud responses to climate variability over the extratropical oceans as observed by MISR and MODIS. Atmospheric Chemistry and Physics, https://doi.org/10.5194/acp-19-7547-2019
Geiss, A., and Mahrt, L. (2015) Decomposition of Spatial Structure of Nocturnal Flow over Gentle Terrain. Boundary Layer Meteorology, 156-3: 337-347, https://doi.org/10.1007/s10546-015-0043-7
Geiss, A., and Levy, G. (2012) The use of automated feature extraction for diagnosing double inter-tropical convergence zones. Computers & Geosciences, 46:73-76, https://doi.org/10.1016/j.cageo.2012.03.024
Levy, G., Geiss, A., Kumar, M-R. (2011) Near-Equatorial Convective Regimes over the Indian Ocean as Revealed by Synergistic Analysis of Satellite Observations. Advances in Geosciences, 22: 101-114, http://doi.org/10.1142/9789814355315_0008
Other
Geiss, A., Hardin, J. C., Silva, S., Gustafson, W. I., Varble, A., and Fan, J. (2020) Deep Learning for Ensemble Forecasting. (DOE White Paper), https://www.ai4esp.org/
Geiss, A., and Hardin J. C. (2021) Papers of Note: Radar Super Resolution with Deep Learning. (Paper Highlight), Bulletin of the American Meteorological Society, 102-2: 100-101
Geiss, A. (2020) Observed and Modeled Cloud Responses to Climate Variability. (PhD Thesis), University of Washington, University of Washington Libraries
Geiss, A. (2016) Multi-year Trends in MODIS and MISR Observed Cloud Fraction over the Extratropical Oceans. (M.S. Thesis), University of Washington, University of Washington Libraries