# AI Weather & Climate Modeling Domain: Fusion, Energy & Climate · 249 papers indexed · newest 2026-10-27 Machine-learning weather prediction (GraphCast, GenCast, Aurora), hybrid climate models (NeuralGCM) and kilometer-scale simulation. Key concepts: weather forecasting, machine learning weather, climate model, medium-range forecast, neural general circulation model, ensemble forecast ## Most influential - **[Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://space-kitty.com/papers/arxiv:1506.04214)**. Xingjian Shi, Zhourong Chen, Hao Wang et al. · 2015 (arXiv:1506.04214 · 7,708 citations). Source: > The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (FC-LSTM) to have convolutional structures in both the input-to-state and state-to-state transitions, we … - **[Learning skillful medium-range global weather forecasting](https://space-kitty.com/papers/doi:10.1126/science.adi2336)**. Rémi Lam, Álvaro Sánchez‐González, Matthew Willson et al. · 2023 · Science (doi:10.1126/science.adi2336 · 1,463 citations). Source: > Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy but does not directly use historical weather data to improve the underlying model. Here, we introduce GraphCast, a machine learning-based method trained directly from reanalysis data. It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute. GraphCast significantly outperforms the most accurate operational deterministic systems on… > _Abstract excerpt by Rémi Lam et al., CC-BY, https://doi.org/10.1126/science.adi2336; truncated._ - **[Prediction of Daily Climate Using Long Short-Term Memory (LSTM) Model](https://space-kitty.com/papers/doi:10.38124/ijisrt/ijisrt24jul073)**. Jinxin Xu, Zhuoyue Wang, Xinjin Li et al. · 2024 · International Journal of Innovative Science and Research Technology (IJISRT) (doi:10.38124/ijisrt/ijisrt24jul073 · 975 citations). Source: - **[Time-series forecasting with deep learning: a survey](https://space-kitty.com/papers/arxiv:2004.13408)**. Bryan Y. Lim, Stefan Zohren · 2021 · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (arXiv:2004.13408 · doi:10.1098/rsta.2020.0209 · 1,848 citations). Source: > Numerous deep learning architectures have been developed to accommodate the diversity of time series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time series forecasting -- describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning c… - **[A review of machine learning applications in wildfire science and management](https://space-kitty.com/papers/arxiv:2003.00646)**. Piyush Jain, Sean C.P. Coogan, Sriram Ganapathi Subramanian et al. · 2020 · Environmental Reviews (arXiv:2003.00646 · doi:10.1139/er-2020-0019 · 848 citations). Source: > Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popu… - **[Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station](https://space-kitty.com/papers/doi:10.1007/s00500-020-04954-0)**. Pradeep Hewage, Ardhendu Behera, Marcello Trovati et al. · 2020 · Soft Computing (doi:10.1007/s00500-020-04954-0 · 741 citations). Source: > Abstract Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evalu… > _Abstract excerpt by Pradeep Hewage et al., CC-BY, https://doi.org/10.1007/s00500-020-04954-0; truncated._ - **[Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach](https://space-kitty.com/papers/arxiv:1706.06279)**. Jintao Ke, Hongyu Zheng, Hai Yang et al. · 2017 · Transportation Research Part C Emerging Technologies (arXiv:1706.06279 · doi:10.1016/j.trc.2017.10.016 · 728 citations). Source: > Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked … - **[WeatherBench: A benchmark dataset for data-driven weather forecasting](https://space-kitty.com/papers/arxiv:2002.00469)**. Stephan Rasp, Peter D. Dueben, Sebastian Scher et al. · 2020 · Journal of Advances in Modeling Earth Systems (arXiv:2002.00469 · doi:10.1029/2020ms002203 · 662 citations). Source: > Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data-driven methods could also be used to predict global weather patterns days in advance. First studies show promise but the lack of a common dataset and evaluation metrics make inter-comparison between studies difficult. Here we present a benchmark dataset for data-driven medium-range weather forecasting, a topic of high scientific interest for atmospheric and computer scientists alike. We provide data derived from the ERA5 archive that has b… - **[An IoT based smart irrigation management system using Machine learning and open source technologies](https://space-kitty.com/papers/doi:10.1016/j.compag.2018.09.040)**. Amarendra Goap, Deepak Sharma, Alok Kumar Shukla et al. · 2018 · Computers and Electronics in Agriculture (doi:10.1016/j.compag.2018.09.040 · 679 citations). Source: - **[Can deep learning beat numerical weather prediction?](https://space-kitty.com/papers/doi:10.1098/rsta.2020.0097)**. Martin G. Schultz, Clara Betancourt, Bing Gong et al. · 2021 · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (doi:10.1098/rsta.2020.0097 · 568 citations). Source: > The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather forecasts can be produced by introducing big data mining and neural networks into the weather prediction workflow. Here, we discuss the question of whether it is possible to completely replace the current numerical weather models and data assimilation systems with DL approaches. This discussion entail… > _Abstract excerpt by Martin G. Schultz et al., CC-BY, https://doi.org/10.1098/rsta.2020.0097; truncated._ - **[Neural General Circulation Models for Weather and Climate](https://space-kitty.com/papers/arxiv:2311.07222)**. Dmitrii Kochkov, Janni Yuval, Ian Langmore et al. · 2023 · Nature (arXiv:2311.07222 · doi:10.1038/s41586-024-07744-y · 452 citations). Source: > General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine learning (ML) models trained on reanalysis data achieved comparable or better skill than GCMs for deterministic weather forecasting. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present the first GCM that combin… - **[Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction](https://space-kitty.com/papers/doi:10.1016/j.rser.2022.112364)**. Dávid Markovics, Martin János Mayer · 2022 · Renewable and Sustainable Energy Reviews (doi:10.1016/j.rser.2022.112364 · 499 citations). Source: > The increase of the worldwide installed photovoltaic (PV) capacity and the intermittent nature of the solar resource highlights the importance of power forecasting for the grid integration of the technology. This study compares 24 machine learning models for deterministic day-ahead power forecasting based on numerical weather predictions (NWP), tested for two-year-long 15-min resolution datasets of 16 PV plants in Hungary. The effects of the predictor selection and the benefits of the hyperparameter tuning are also evaluated. The results show that the two most accurate models are kernel ridge … > _Abstract excerpt by Dávid Markovics et al., CC-BY, https://doi.org/10.1016/j.rser.2022.112364; truncated._ - **[Probabilistic weather forecasting with machine learning](https://space-kitty.com/papers/doi:10.1038/s41586-024-08252-9)**. Ilan Price, Álvaro Sánchez‐González, Ferran Alet et al. · 2024 · Nature (doi:10.1038/s41586-024-08252-9 · 433 citations). Source: > Abstract Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP) 1 , which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations 2,3 . However, these advances have focused primarily on single, … > _Abstract excerpt by Ilan Price et al., CC-BY, https://doi.org/10.1038/s41586-024-08252-9; truncated._ - **[Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions](https://space-kitty.com/papers/doi:10.1016/j.jhydrol.2019.04.085)**. Guomin Huang, Lifeng Wu, Xin Ma et al. · 2019 · Journal of Hydrology (doi:10.1016/j.jhydrol.2019.04.085 · 565 citations). Source: - **[Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy](https://space-kitty.com/papers/doi:10.1016/j.apenergy.2014.02.057)**. Rishee Kumar Jain, Kevin M. Smith, Patricia J. Culligan et al. · 2014 · Applied Energy (doi:10.1016/j.apenergy.2014.02.057 · 602 citations). Source: ## Newest - **[Scale-aware and spatially-localised optimisation of kilometre-scale MLWP models](https://space-kitty.com/papers/arxiv:2610.37325)**. James Chappell, Sophie Luise Arana, Jack Bowyer et al. · 2026 (arXiv:2610.37325). Source: - **[Machine learning applications in the heliosphere](https://space-kitty.com/papers/arxiv:2610.37927)**. Vishal Upendran, Banafsheh Ferdousi · 2026 (arXiv:2610.37927). Source: - **[Self-Supervised Latent Action-Conditioned World Modeling for Time Series](https://space-kitty.com/papers/arxiv:2610.37963)**. Chia-Han Kuo, Chengshuai Zhao, Huan Liu et al. · 2026 (arXiv:2610.37963). Source: - **[Efficient probabilistic weather forecasting at quarter-degree resolution with two-sided transport attention](https://space-kitty.com/papers/arxiv:2610.35545)**. Junu Choi, Youngjoon Hong · 2026 (arXiv:2610.35545). Source: - **[GNN-VIEW: Visual Inspection and Explanation of Graph-based Earth-System Models](https://space-kitty.com/papers/arxiv:2610.35693)**. Alessandro Lucatelli, Ferdinand Unterhuber, Katharina Krenzer et al. · 2026 (arXiv:2610.35693). Source: - **[Towards operational data-driven regional weather forecasting: extreme-event skill and near-real-time verification with HRRRCast](https://space-kitty.com/papers/arxiv:2610.36186)**. Daniel Abdi, Isidora Jankov, Vanderlei Vargas et al. · 2026 (arXiv:2610.36186). Source: - **[FASST: A Compact Stateless Atmosphere Model for Long-Term Ocean Coupling](https://space-kitty.com/papers/arxiv:2610.36297)**. Pratham Aggarwal, Duncan Watson-Parris, William E. Chapman · 2026 (arXiv:2610.36297). Source: - **[Climate downscaling with likelihood functional flow matching](https://space-kitty.com/papers/arxiv:2610.33482)**. Zhi-Song Liu, Helmi Toropainen, Ammar Kheder et al. · 2026 (arXiv:2610.33482). Source: - **[Batched NN 4D-Var: a performant hybrid model framework for the Integrated Forecasting System](https://space-kitty.com/papers/arxiv:2610.32582)**. Alban Farchi, Marcin Chrust, Massimo Bonavita et al. · 2026 (arXiv:2610.32582). Source: - **[ARENA: Active Region attENtion Aggregation for interpretable solar flare prediction](https://space-kitty.com/papers/arxiv:2610.32758)**. Luis Bauer, Annalena Kofler, Andrés Asensio Ramos et al. · 2026 (arXiv:2610.32758). Source: --- Space Kitty Archive · https://space-kitty.com · API: https://space-kitty.com/api/v1 · MCP: https://space-kitty.com/mcp · Premium: $0.20/request via x402 (Base Sepolia (testnet)). Metadata: arXiv (CC0), OpenAlex (CC0), Europe PMC (CC BY/CC0 works), ClinicalTrials.gov.