| Data-Driven Cloud Clustering via a Rotationally Invariant Autoencoder T Kurihana, E Moyer, R Willett, D Gilton, I Foster IEEE Transactions on Geoscience and Remote Sensing, 1-25, 2021 | 13 | 2021 |
| AICCA: AI-driven cloud classification atlas T Kurihana, EJ Moyer, IT Foster Remote Sensing 14 (22), 5690, 2022 | 9 | 2022 |
| Cloud characterization with unsupervised deep learning, T Kurihana, I Foster, R Willett, S Jenkins, K Koenig, R Werman, ... Proceedings for Climate Informatics Workshop 2019 Paris, 2019 | 6* | 2019 |
| Perturbations by the ensemble transform K Saito, L Duc, T Matsunobu, T Kurihana Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol …, 2022 | 3 | 2022 |
| Developing unsupervised learning models for cloud classification S Jenkins, EJ Moyer, I Foster, T Kurihana, R Willett, M Maire, K Koenig, ... AGU Fall Meeting Abstracts 2019, A51U-2673, 2019 | 2 | 2019 |
| Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models L Wang, T Kurihana, A Meray, I Mastilovic, S Praveen, Z Xu, ... arXiv preprint arXiv:2211.10884, 2022 | 1 | 2022 |
| A data-driven cloud classification framework based on a rotationally invariant autoencoder T Kurihana, I Foster, R Willett, M Maire, S Jenkins, A Matai, EJ Moyer AGU Fall Meeting Abstracts 2020, A059-0003, 2020 | 1 | 2020 |
| Cloud Characterization With Deep Learning II T Kurihana, I Foster, EJ Moyer, R Willett, M Maire, S Jenkins, K Koenig, ... AGU Fall Meeting Abstracts 2019, A53H-03, 2019 | 1 | 2019 |
| Perturbation Methods for Ensemble Data Assimilation K Saito, M Kunii, L Duc, T Kurihana RIKEN International Symposium on Data Assimilation, Kobe, Japan.[Available …, 2017 | 1 | 2017 |
| Pretraining a foundation model using MODIS observations of the earth’s atmosphere V Anantharaj, T Kurihana, G Padovani, A Kumar, A Tsaris, U Nair, S Fiore, ... EGU24, 2024 | | 2024 |
| Democratizing Access to Extensive Climate Datasets via Deep Learning-Powered Techniques T Kurihana The University of Chicago, 2024 | | 2024 |
| Physics-informed surrogate modeling for supporting climate resilience at groundwater contamination sites A Meray, L Wang, T Kurihana, I Mastilovic, S Praveen, Z Xu, ... Computers & Geosciences 183, 105508, 2024 | | 2024 |
| A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network T Kurihana, K Yeo, D Szwarcman, B Elmegreen, K Mukkavilli, J Schmude, ... arXiv preprint arXiv:2312.13212, 2023 | | 2023 |
| Automated cloud classification via vision transformers for self-supervised semantic segmentation JA Franke, T Kurihana, EJ Moyer, IT Foster AGU23, 2023 | | 2023 |
| SCuBA: Self-supervised Cloud Bias Assessment for evaluating cloud representations from high-resolution climate models against MODIS cloud images T Kurihana, JA Franke, VG Anantharaj, I Foster, EJ Moyer AGU23, 2023 | | 2023 |
| Deep Learning in Climate, Weather, and Earth Sciences II Oral DD Lucas, T Kurihana, D Watson-Parris, JA Franke, EJ Moyer, I Foster AGU23, 2023 | | 2023 |
| A 3D spatial self-attention module on a non-uniform vertical coordinate for super-resolution wind fields T Kurihana, K Yeo, D Szwarcman, B Elmegreen, SK Mukkavilli AGU23, 2023 | | 2023 |
| Estimating the Impact of Large-scale Natural Aerosol Injections on Marine Cloud Populations with Unsupervised Classification in MODIS Images JA Franke, T Kurihana, Z Wang, I Foster, EJ Moyer AGU Fall Meeting Abstracts 2022, A55N-1294, 2022 | | 2022 |
| Global-scale unsupervised cloud classification to construct a novel AI-driven Cloud Classification Atlas (AICCA) T Kurihana, EJ Moyer, Z Wang, JA Franke, T Monkman, J Kuntzleman, ... AGU Fall Meeting Abstracts 2022, GC16C-06, 2022 | | 2022 |
| Physics-informed surrogate modeling for supporting climate resilience at groundwater contamination sites L Wang, T Kurihana, A Meray, I Mastilovic, S Praveen, Z Xu, ... AGU Fall Meeting Abstracts 2022, H45L-1534, 2022 | | 2022 |
Ian T. FosterUniversity of Chicago and Argonne National LaboratoryVerified email at anl.gov
