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As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric sciences is increasingly adopting data-driven models, powered by progressive developments in deep learning (DL).
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J. G. Fernández and S. Mehrkanoon, “Broad-unet: Multi-scale feature learning for nowcasting tasks,” Neural Networks , vol. 144, pp. 419–427, 2021
2021
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J. Wang, Z. Liu, I. Foster, W. Chang, R. Kettimuthu, and V. R. Kotamarthi, “Fast and accurate learned multiresolution dynamical downscaling for precipitation,” Geoscientific Model Development , vol. 14, no. 10, pp. 6355–6372, 2021
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A. J. Geer, “Learning earth system models from observations: machine learning or data assimilation?” Philosophical Transactions of the Royal Society A , vol. 379, no. 2194, p. 20200089, 2021
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F. Liang, B. Wu, X. Dai, K. Li, Y. Zhao, H. Zhang, P. Zhang, P. Vajda, and D. Marculescu, “Open-vocabulary semantic segmentation with mask-adapted clip,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 7061–7070
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N. Matzakos, S. Doukakis, and M. Moundridou, “Learning mathematics with large language models: A comparative study with computer algebra systems and other tools.” International Journal of Emerging Technologies in Learning , vol. 18, no. 20, 2023
2023
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K. Bi, L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian, “Accurate medium-range global weather forecasting with 3d neural networks,” Nature , vol. 619, no. 7970, pp. 533–538, 2023
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Y. Liu, K. Duffy, J. G. Dy, and A. R. Ganguly, “Explainable deep learning for insights in el niño and river flows,” Nature Communications , vol. 14, no. 1, p. 339, 2023
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H. Wang, S. Hu, and X. Li, “An interpretable deep learning enso forecasting model,” Ocean-Land-Atmosphere Research , vol. 2, p. 0012, 2023
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L. Chen, B. Han, X. Wang, J. Zhao, W. Yang, and Z. Yang, “Machine learning methods in weather and climate applications: A survey,” Applied Sciences , vol. 13, no. 21, p. 12019, 2023
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A. Jones, J. Kuehnert, P. Fraccaro, O. Meuriot, T. Ishikawa, B. Edwards, N. Stoyanov, S. L. Remy, K. Weldemariam, and S. Assefa, “Ai for climate impacts: applications in flood risk,” npj Climate and Atmospheric Science , vol. 6, no. 1, p. 63, 2023
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M. J. Molina, T. A. O’Brien, G. Anderson, M. Ashfaq, K. E. Bennett, W. D. Collins, K. Dagon, J. M. Restrepo, and P. A. Ullrich, “A review of recent and emerging machine learning applications for climate variability and weather phenomena,” Artificial Intelligence for the Earth Systems , pp. 1–46, 2023
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W. Dai, J. Li, D. Li, A. M. H. Tiong, J. Zhao, W. Wang, B. Li, P. Fung, and S. Hoi, “Instructblip: Towards general-purpose vision-language models with instruction tuning,” 2023
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2023
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H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D. Bikel, L. Blecher, C. C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I. Kloumann, A. Korenev, P. S. Koura, M.-A. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E. M. Smith, R. Subramanian, X. E. Tan, B. Tang, R. Taylor, A. Williams, J. X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov, and T. Scialom, “Llama 2: Open foundation and fine-tuned chat models,” 2023
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J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” 2023
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S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,” 2023
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M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, L. Gianinazzi, J. Gajda, T. Lehmann, M. Podstawski, H. Niewiadomski, P. Nyczyk, and T. Hoefler, “Graph of thoughts: Solving elaborate problems with large language models,” 2023
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S. Chen, T. Shu, H. Zhao, G. Zhong, and X. Chen, “Tempee: Temporal–spatial parallel transformer for radar echo extrapolation beyond autoregression,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–14, 2023. [Online]. Available: https://doi.org/10.1109%2Ftgrs.2023.3311510
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A. Lazcano, P. J. Herrera, and M. Monge, “A combined model based on recurrent neural networks and graph convolutional networks for financial time series forecasting,” Mathematics , vol. 11, no. 1, p. 224, 2023
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F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
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J. Park, S. Son, and K. M. Lee, “Content-aware local gan for photo-realistic super-resolution,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 10 585–10 594
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X. Cheng, J. Zhou, J. Song, and X. Zhao, “A highway traffic image enhancement algorithm based on improved gan in complex weather conditions,” IEEE Transactions on Intelligent Transportation Systems , 2023
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K. Chen, T. Han, J. Gong, L. Bai, F. Ling, J.-J. Luo, X. Chen, L. Ma, T. Zhang, R. Su, Y. Ci, B. Li, X. Yang, and W. Ouyang, “Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead,” 2023
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M. Ma, P. Xie, F. Teng, B. Wang, S. Ji, J. Zhang, and T. Li, “Histgnn: Hierarchical spatio-temporal graph neural network for weather forecasting,” Information Sciences , vol. 648, p. 119580, 2023
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L. Chen, F. Du, Y. Hu, Z. Wang, and F. Wang, “Swinrdm: integrate swinrnn with diffusion model towards high-resolution and high-quality weather forecasting,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 1, 2023, pp. 322–330
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Y. Hu, L. Chen, Z. Wang, and H. Li, “Swinvrnn: A data-driven ensemble forecasting model via learned distribution perturbation,” Journal of Advances in Modeling Earth Systems , vol. 15, no. 2, p. e2022MS003211, 2023
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S. Bire, B. Lütjens, D. Newman, and C. Hill, “Oceanfourcast: Emulating ocean models with transformers for adjoint-based data assimilation,” Copernicus Meetings, Tech. Rep., 2023
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I. Prapas, N.-I. Bountos, S. Kondylatos, D. Michail, G. Camps-Valls, and I. Papoutsis, “Televit: Teleconnection-driven transformers improve subseasonal to seasonal wildfire forecasting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3754–3759
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X. Zhong, L. Chen, J. Liu, C. Lin, Y. Qi, and H. Li, “Fuxi-extreme: Improving extreme rainfall and wind forecasts with diffusion model,” 2023
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J. González-Abad, Álex Hernández-García, P. Harder, D. Rolnick, and J. M. Gutiérrez, “Multi-variable hard physical constraints for climate model downscaling,” 2023
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P. Harder, V. Ramesh, A. Hernandez-Garcia, Q. Yang, P. Sattigeri, D. Szwarcman, C. Watson, and D. Rolnick, “Physics-constrained deep learning for downscaling,” Copernicus Meetings, Tech. Rep., 2023
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D. Fuchs, S. C. Sherwood, A. Prasad, K. Trapeznikov, and J. Gimlett, “Torchclim v1. 0: A deep-learning framework for climate model physics,” EGUsphere , vol. 2023, pp. 1–25, 2023
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A. Asperti, F. Merizzi, A. Paparella, G. Pedrazzi, M. Angelinelli, and S. Colamonaco, “Precipitation nowcasting with generative diffusion models,” 2023
2023
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J. Choi, Y. Kim, K.-H. Kim, S.-H. Jung, and I. Cho, “Pct-cyclegan: Paired complementary temporal cycle-consistent adversarial networks for radar-based precipitation nowcasting,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , 2023, pp. 348–358
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Z. Ma, H. Zhang, and J. Liu, “Mm-rnn: A multimodal rnn for precipitation nowcasting,” IEEE Transactions on Geoscience and Remote Sensing , 2023
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Q. Jin, X. Zhang, X. Xiao, G. Meng, S. Xiang, C. Pan et al. , “Spatiotemporal inference network for precipitation nowcasting with multi-modal fusion,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2023
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Q. Jin, X. Zhang, X. Xiao, Y. Wang, S. Xiang, and C. Pan, “Preformer: Simple and efficient design for precipitation nowcasting with transformers,” IEEE Geoscience and Remote Sensing Letters , 2023
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H. Li, N. Zhang, Z. Xu, X. Li, C. Liu, C. Zhao, and J. Wu, “Dk-stn: A domain knowledge embedded spatio-temporal network model for mjo forecast,” Expert Systems With Applications, Forthcoming , 2023
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D. J. Fulton, B. J. Clarke, and G. C. Hegerl, “Bias correcting climate model simulations using unpaired image-to-image translation networks,” Artificial Intelligence for the Earth Systems , vol. 2, no. 2, p. e220031, 2023
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B. Wu, W. Chen, W. Wang, B. Peng, L. Sun, and L. Chen, “Weathergnn: Exploiting complicated relationships in numerical weather prediction bias correction,” 2023
2023
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Z. Bi, N. Zhang, Y. Xue, Y. Ou, D. Ji, G. Zheng, and H. Chen, “Oceangpt: A large language model for ocean science tasks,” 2023
2023
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T. Schimanski, J. Bingler, C. Hyslop, M. Kraus, and M. Leippold, “Climatebert-netzero: Detecting and assessing net zero and reduction targets,” 2023
2023
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E. C. Garrido-Merchán, C. González-Barthe, and M. C. Vaca, “Fine-tuning climatebert transformer with climatext for the disclosure analysis of climate-related financial risks,” 2023
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B. Usharani, “Ilf-lstm: Enhanced loss function in lstm to predict the sea surface temperature,” Soft Computing , vol. 27, no. 18, pp. 13 129–13 141, 2023
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S. Tang, C. Li, P. Zhang, and R. Tang, “Swinlstm: Improving spatiotemporal prediction accuracy using swin transformer and lstm,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023, pp. 13 470–13 479
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2023
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G. P. Høivang, “Diffmet: Diffusion models and deep learning for precipitation nowcasting,” Master’s thesis, 2023
2023
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Y. Ji, B. Gong, M. Langguth, A. Mozaffari, and X. Zhi, “Clgan: a generative adversarial network (gan)-based video prediction model for precipitation nowcasting,” Geoscientific Model Development , vol. 16, no. 10, pp. 2737–2752, 2023
2023
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R. Wang, L. Su, W. K. Wong, A. K. Lau, and J. C. Fung, “Skillful radar-based heavy rainfall nowcasting using task-segmented generative adversarial network,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
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N. J. Annau, A. J. Cannon, and A. H. Monahan, “Algorithmic hallucinations of near-surface winds: Statistical downscaling with generative adversarial networks to convection-permitting scales,” Artificial Intelligence for the Earth Systems , 2023
2023
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E. Balogun, R. Buechler, R. Rajagopal, and A. Majumdar, “Temperaturegan: Generative modeling of regional atmospheric temperatures,” 2023
2023
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J. Sleeman, D. Chung, A. Gnanadesikan, J. Brett, Y. Kevrekidis, M. Hughes, T. Haine, M.-A. Pradal, R. Gelderloos, C. Ashcraft, C. Tang, A. Saksena, and L. White, “A generative adversarial network for climate tipping point discovery (tip-gan),” 2023
2023
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Y. Meng, F. Gao, E. Rigall, R. Dong, J. Dong, and Q. Du, “Physical knowledge-enhanced deep neural network for sea surface temperature prediction,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–13, 2023
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T. Yuan, J. Zhu, W. Wang, J. Lu, X. Wang, X. Li, and K. Ren, “A space-time partial differential equation based physics-guided neural network for sea surface temperature prediction,” Remote Sensing , vol. 15, no. 14, p. 3498, 2023
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F. Lin, X. Yuan, Y. Zhang, P. Sigdel, L. Chen, L. Peng, and N.-F. Tzeng, “Comprehensive transformer-based model architecture for real-world storm prediction,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2023, pp. 54–71
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A. Bojesomo, H. AlMarzouqi, and P. Liatsis, “A novel transformer network with shifted window cross-attention for spatiotemporal weather forecasting,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2023
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Y. Gao, S. Miyata, Y. Matsunami, and Y. Akashi, “Spatio-temporal interpretable neural network for solar irradiation prediction using transformer,” Energy and Buildings , vol. 297, p. 113461, 2023
2023
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S. A. Vaghefi, Q. Wang, V. Muccione, J. Ni, M. Kraus, J. Bingler, T. Schimanski, C. Colesanti-Senni, N. Webersinke, C. Huggel, and M. Leippold, “chatclimate: Grounding conversational ai in climate science,” 2023
2023
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A. Krishnan and V. S. Anoop, “Climatenlp: Analyzing public sentiment towards climate change using natural language processing,” 2023
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A. Auzepy, E. Tönjes, D. Lenz, and C. Funk, “Evaluating tcfd reporting: A new application of zero-shot analysis to climate-related financial disclosures,” 2023
2023
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M. Kraus, J. A. Bingler, M. Leippold, T. Schimanski, C. C. Senni, D. Stammbach, S. A. Vaghefi, and N. Webersinke, “Enhancing large language models with climate resources,” 2023
2023
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P. Li, Y. Yu, D. Huang, Z.-H. Wang, and A. Sharma, “Regional heatwave prediction using graph neural network and weather station data,” Geophysical Research Letters , vol. 50, no. 7, p. e2023GL103405, 2023
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2023
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A. Gong, R. Li, B. Pan, H. Chen, G. Ni, and M. Chen, “Enhancing spatial variability representation of radar nowcasting with generative adversarial networks,” Remote Sensing , vol. 15, no. 13, p. 3306, 2023
2023
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M. Andrychowicz, L. Espeholt, D. Li, S. Merchant, A. Merose, F. Zyda, S. Agrawal, and N. Kalchbrenner, “Deep learning for day forecasts from sparse observations,” 2023
2023
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F. Min, L. Wang, S. Pan, and G. Song, “D 2 unet: Dual decoder u-net for seismic image super-resolution reconstruction,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–13, 2023
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Q. Yu, M. Zhu, Q. Zeng, H. Wang, Q. Chen, X. Fu, and Z. Qing, “Weather radar super-resolution reconstruction based on residual attention back-projection network,” Remote Sensing , vol. 15, no. 8, p. 1999, 2023
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N. P. Juan, J. O. Rodríguez, V. N. Valdecantos, and G. Iglesias, “Data-driven and physics-based approach for wave downscaling: A comparative study,” Ocean Engineering , vol. 285, p. 115380, 2023
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D. Feng, Z. Tan, and Q. He, “Physics-informed neural networks of the saint-venant equations for downscaling a large-scale river model,” Water Resources Research , vol. 59, no. 2, p. e2022WR033168, 2023
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OpenAI, “Gpt-4 technical report,” 2023
2023
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C. Bai, Z. Cai, X. Yin, and J. Zhang, “Lsdssimr: Large-scale dust storm database based on satellite images and meteorological reanalysis data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2023
2023
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R. A. Sobash, D. J. Gagne, C. L. Becker, D. Ahijevych, G. N. Gantos, and C. S. Schwartz, “Diagnosing storm mode with deep learning in convection-allowing models,” Monthly Weather Review , 2023
2023
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D. Song, X. Su, W. Li, Z. Sun, T. Ren, W. Liu, and A.-A. Liu, “Spatial-temporal transformer network for multi-year enso prediction,” Frontiers in Marine Science , vol. 10, p. 1143499, 2023
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M. Liu-Schiaffini, C. E. Singer, N. Kovachki, T. Schneider, K. Azizzadenesheli, and A. Anandkumar, “Tipping point forecasting in non-stationary dynamics on function spaces,” 2023
2023
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A. Gnanadesikan, J. Brett, J. Sleeman, and D. Chung, “Using ai to detect climate tipping points-or why it’s hard to understand rapid changes in the earth system,” 2023
2023
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A. Danandeh Mehr, A. Rikhtehgar Ghiasi, Z. M. Yaseen, A. U. Sorman, and L. Abualigah, “A novel intelligent deep learning predictive model for meteorological drought forecasting,” Journal of Ambient Intelligence and Humanized Computing , vol. 14, no. 8, pp. 10 441–10 455, 2023
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C. O. de Burgh-Day and T. Leeuwenburg, “Machine learning for numerical weather and climate modelling: a review,” EGUsphere , vol. 2023, pp. 1–48, 2023
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E. Vosper, P. Watson, L. Harris, A. McRae, R. Santos-Rodriguez, L. Aitchison, and D. Mitchell, “Deep learning for downscaling tropical cyclone rainfall to hazard-relevant spatial scales,” Journal of Geophysical Research: Atmospheres , p. e2022JD038163, 2023
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Y. Tang, J. Zhou, X. Pan, Z. Gong, and J. Liang, “Postrainbench: A comprehensive benchmark and a new model for precipitation forecasting,” 2023
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J. E. Johnson, Q. Febvre, A. Gorbunova, S. Metref, M. Ballarotta, J. L. Sommer, and R. Fablet, “Oceanbench: The sea surface height edition,” 2023
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P. Bommer, M. Kretschmer, A. Hedström, D. Bareeva, and M. M. C. Höhne, “Finding the right xai method – a guide for the evaluation and ranking of explainable ai methods in climate science,” 2023
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S. Chen, X. Wang, S. Ren, J. Yang, Y. Zhang, and G. Wang, “Collaborative photonic crystal fiber property optimization: A new paradigm for reverse design,” IEEE Photonics Technology Letters , 2023
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