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Atmospheric Science & Global Change

Staff information

Andrew Geiss

Computational Climate Science
Data Scientist

PNNL Publications


  • King F., C. Pettersen, C.G. Fletcher, and A.V. Geiss. 2024. "Development of a full-scale connected U-Net for reflectivity inpainting in spaceborne radar blind zones." Artificial Intelligence for the Earth Systems 3, no. 2:e230063. PNNL-SA-190355. doi:10.1175/AIES-D-23-0063.1


  • Geiss A.V., and J.C. Hardin. 2023. "Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets." Artificial Intelligence for the Earth Systems 2, no. 1:e210012. PNNL-SA-157590. doi:10.1175/AIES-D-21-0012.1
  • Geiss A.V., P. Ma, B. Singh, and J.C. Hardin. 2023. "Emulating Aerosol Optics with Randomly Generated Neural Networks." Geoscientific Model Development 16, no. 9:2355-2370. PNNL-SA-173125. doi:10.5194/gmd-16-2355-2023


  • Geiss A.V., S.J. Silva, and J.C. Hardin. 2022. "Downscaling Atmospheric Chemistry Simulations with Physically Consistent Deep Learning." Geoscientific Model Development 15, no. 17:6677-6694. PNNL-SA-172996. doi:10.5194/gmd-15-6677-2022


  • Geiss A.V., and J.C. Hardin. 2021. "Inpainting Radar Missing Data Regions with Deep Learning." Atmospheric Measurement Techniques 14, no. 12:7729-7747. PNNL-SA-160975. doi:10.5194/amt-14-7729-2021


  • Geiss A.V., and J.C. Hardin. 2020. "Radar Super Resolution using a Deep Convolutional Neural Network." Journal of Atmospheric and Oceanic Technology 37, no. 12:2197-2207. PNNL-SA-152524. doi:10.1175/JTECH-D-20-0074.1

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