Yannic Lops is a Postdoc at the Atmospheric, Earth, and Energy Division (AEED) at LLNL. His main work focuses on the development of Deep Learning model(s) to bias correct precipitation of climate simulations. His research interests include, but not limited to, machine- and deep learning-based Uncertainty Quantification, Forecasting, Downscaling, and Imputation within Climate, Weather, Air Quality, and Remote Sensing.

Professional Background

2022.03 – Present: Postdoctoral Researcher, Lawrence Livermore National Laboratory, Livermore, CA, USA

Ph.D. Atmospheric Science, University of Houston, Houston, Texas

M.S. Sustainability Science, Leuphana University, Lüneburg, Germany

B.S. Environmental Science, Leuphana University, Lüneburg, Germany

Media

Google Scholar: https://scholar.google.com/citations?user=w6AQ954AAAAJ&hl=en

ORCiD: https://orcid.org/0000-0002-2594-7845

 

Selected Publications

Publications associated with institutions other than LLNL

Lops, Y., Pouyaei, A., Choi, Y., Jung, J., Salman, A. K., & Sayeed, A. (2021). Application of a partial convolutional neural network for estimating geostationary aerosol optical depth data. Geophysical Research Letters, 48(15), e2021GL093096.

Ghahremanloo, M., Lops, Y., Choi, Y., & Mousavinezhad, S. (2021). Impact of the COVID-19 outbreak on air pollution levels in East Asia. Science of the Total Environment, 754, 142226.

Sayeed, A., Lops, Y., Choi, Y., Jung, J., & Salman, A. K. (2021). Bias correcting and extending the PM forecast by CMAQ up to 7 days using deep convolutional neural networks. Atmospheric Environment, 253, 118376.

Lops, Y., Choi, Y., Mousavinezhad, S., Salman, A. K., Nelson, D. L., & Singh, D. (2023). Development of Deep Convolutional Neural Network Ensemble Models for 36-Month ENSO Forecasts. Asia-Pacific Journal of Atmospheric Sciences, 1-9.

Lops, Y., Ghahremanloo, M., Pouyaei, A., Choi, Y., Jung, J., Mousavinezhad, S., ... & Hammond, D. (2023). Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network. Neural Computing and Applications, 1-12.