Savini Samarasinghe
Savini Samarasinghe
Illinois State Water Survey, University of Illinois Urbana-Champaign
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Cited by
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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
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
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
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
Thoughtfully using artificial intelligence in Earth science
I Ebert-Uphoff, SM Samarasinghe, EA Barnes
Eos Transactions 100 (, 2019
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
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
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
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
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
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
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
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
Causal Inference Using Observational Data-Case Studies in Climate Science
SM Samarasinghe
Colorado State University, 2020
PC stable example
S Samarasinghe
Using Global Deep-Learning Forecast Systems to Explain Sources of Weather and Climate Predictability
B Toms, S Samarasinghe
103rd AMS Annual Meeting, 2023
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
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
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
The Madden–Julian oscillation strengthens its reach
B Langenbrunner
Nature Climate Change 11 (3), 183-183, 2021
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