| Evaluating lossy data compression on climate simulation data within a large ensemble AH Baker, DM Hammerling, SA Mickelson, H Xu, MB Stolpe, P Naveau, ... Geoscientific Model Development 9 (12), 4381, 2016 | 81 | 2016 |
| TETRAD- A Toolbox for Causal Discovery JD Ramsey, K Zhang, M Glymour, RS Romero, B Huang, I Ebert-Uphoff, ... Proceedings of the 8th International Workshop on Climate Informatics, 2018 | 77 | 2018 |
| Tropospheric and stratospheric causal pathways between the MJO and NAO EA Barnes, SM Samarasinghe, I Ebert‐Uphoff, JC Furtado Journal of Geophysical Research: Atmospheres, 2019 | 62 | 2019 |
| A study of links between the Arctic and the midlatitude jet stream using Granger and Pearl causality SM Samarasinghe, MC McGraw, EA Barnes, I Ebert‐Uphoff Environmetrics, 2018 | 37 | 2018 |
| Thoughtfully using artificial intelligence in Earth science I Ebert-Uphoff, SM Samarasinghe, EA Barnes Eos Transactions 100 (https://doi.org/10.1029/2019EO135235), 2019 | 15 | 2019 |
| Deep‐learning multiphysics network for imaging CO2 saturation and estimating uncertainty in geological carbon storage ES Um, D Alumbaugh, M Commer, S Feng, E Gasperikova, Y Li, Y Lin, ... Geophysical Prospecting, 2022 | 11 | 2022 |
| Strengthened causal connections between the MJO and the North Atlantic with climate warming SM Samarasinghe, C Connolly, EA Barnes, I Ebert‐Uphoff, L Sun Geophysical Research Letters 48 (5), e2020GL091168, 2021 | 11 | 2021 |
| A Causality-Based View of the Interaction between Synoptic-and Planetary-Scale Atmospheric Disturbances SM Samarasinghe, Y Deng, I Ebert-Uphoff Journal of the Atmospheric Sciences 77 (3), 925-941, 2020 | 11 | 2020 |
| Development of a Multi-Scale Synthetic Data Set for the Testing of Subsurface CO2 Storage Monitoring Strategies DL Alumbaugh, M Commer, D Crandall, E Gasperikova, S Feng, ... AGU Fall Meeting 2021, 2021 | 10 | 2021 |
| Causal Discovery in the Presence of Confounding Latent Variables for Climate Science S Samarasinghe, EA Barnes, I Ebert-Uphoff Proceedings of the 8th International Workshop on Climate Informatics, 2018 | 4 | 2018 |
| The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations D Alumbaugh, E Gasperikova, D Crandall, M Commer, S Feng, W Harbert, ... Geoscience Data Journal, 2023 | 3 | 2023 |
| Structure Learning in Spectral Space with Applications in Climate Science S Samarasinghe, Y Deng, I Ebert-Uphoff Society for Industrial and Applied Mathematics, 17th International …, 2017 | 2 | 2017 |
| A Study of Causal Links between the Arctic and the Midlatitude Jet-Streams S Samarasinghe, M McGraw, EA Barnes, I Ebert-Uphoff Proceedings of the 7th International Workshop on Climate Informatics, 2017 | 2 | 2017 |
| Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets E Gasperikova, D Alumbaugh, D Crandall, M Commer, S Feng, W Harbert, ... National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV …, 2022 | 1 | 2022 |
| Causal Inference Using Observational Data-Case Studies in Climate Science SM Samarasinghe Colorado State University, 2020 | 1 | 2020 |
| PC stable example S Samarasinghe | 1 | 2019 |
| Using Global Deep-Learning Forecast Systems to Explain Sources of Weather and Climate Predictability B Toms, S Samarasinghe 103rd AMS Annual Meeting, 2023 | | 2023 |
| Strengthened causal connections between the MJO and the North Atlantic with climate warming SM Samarasinghe, EA Barnes, C Connolly, I Ebert-Uphoff, L Sun Authorea Preprints, 2022 | | 2022 |
| Limitations of the Gassmann fluid substitution model: a machine learning based investigation SM Samarasinghe, A Kalbekov, J Behura, M Prasad Toward Gigatonnes CO2 Storage — Grand Geophysical Challenge Workshop, 2022 | | 2022 |
| Leveraging laboratory data and explainable machine learning to investigate the limitations of the Gassmann fluid substitution model S Samarasinghe, M Prasad, J Behura Society of Exploration Geophysicists, Machine Learning and AI in Geophysics …, 2022 | | 2022 |
Imme Ebert-UphoffColorado State UniversityDirección de correo verificada de colostate.edu
