AI Weather & Climate Modeling
Machine-learning weather prediction (GraphCast, GenCast, Aurora), hybrid climate models (NeuralGCM) and kilometer-scale simulation.
Most influential
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
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 for…
Preprint7,708 citationsarXiv:1506.04214 - Learning skillful medium-range global weather forecasting
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 …
Abstract excerpt by Rémi Lam et al., CC-BY, https://doi.org/10.1126/science.adi2336; truncated.
Article1,463 citationsdoi:10.1126/science.adi2336 - Prediction of Daily Climate Using Long Short-Term Memory (LSTM) ModelArticle975 citationsdoi:10.38124/ijisrt/ijisrt24jul073
- Time-series forecasting with deep learning: a survey
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,…
Article1,848 citationsarXiv:2004.13408doi:10.1098/rsta.2020.0209 - A review of machine learning applications in wildfire science and management
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 an…
Article848 citationsarXiv:2003.00646doi:10.1139/er-2020-0019 - Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station
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 u…
Abstract excerpt by Pradeep Hewage et al., CC-BY, https://doi.org/10.1007/s00500-020-04954-0; truncated.
Article741 citationsdoi:10.1007/s00500-020-04954-0 - Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
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 forecas…
Article728 citationsarXiv:1706.06279doi:10.1016/j.trc.2017.10.016 - WeatherBench: A benchmark dataset for data-driven weather forecasting
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 stud…
Article662 citationsarXiv:2002.00469doi:10.1029/2020ms002203 - An IoT based smart irrigation management system using Machine learning and open source technologiesArticle679 citationsdoi:10.1016/j.compag.2018.09.040
- Can deep learning beat numerical weather prediction?
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 n…
Abstract excerpt by Martin G. Schultz et al., CC-BY, https://doi.org/10.1098/rsta.2020.0097; truncated.
Article568 citationsdoi:10.1098/rsta.2020.0097 - Neural General Circulation Models for Weather and Climate
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 …
Article452 citationsarXiv:2311.07222doi:10.1038/s41586-024-07744-y - Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction
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 tw…
Abstract excerpt by Dávid Markovics et al., CC-BY, https://doi.org/10.1016/j.rser.2022.112364; truncated.
Article499 citationsdoi:10.1016/j.rser.2022.112364 - Probabilistic weather forecasting with machine learning
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…
Abstract excerpt by Ilan Price et al., CC-BY, https://doi.org/10.1038/s41586-024-08252-9; truncated.
Article433 citationsdoi:10.1038/s41586-024-08252-9 - Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regionsArticle565 citationsdoi:10.1016/j.jhydrol.2019.04.085
- Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracyArticle602 citationsdoi:10.1016/j.apenergy.2014.02.057
Newest work
- Scale-aware and spatially-localised optimisation of kilometre-scale MLWP modelsPreprintarXiv:2610.37325
- Machine learning applications in the heliospherePreprintarXiv:2610.37927
- Self-Supervised Latent Action-Conditioned World Modeling for Time SeriesPreprintarXiv:2610.37963
- Efficient probabilistic weather forecasting at quarter-degree resolution with two-sided transport attentionPreprintarXiv:2610.35545
- GNN-VIEW: Visual Inspection and Explanation of Graph-based Earth-System ModelsPreprintarXiv:2610.35693
- Towards operational data-driven regional weather forecasting: extreme-event skill and near-real-time verification with HRRRCastPreprintarXiv:2610.36186
- FASST: A Compact Stateless Atmosphere Model for Long-Term Ocean CouplingPreprintarXiv:2610.36297
- Climate downscaling with likelihood functional flow matchingPreprintarXiv:2610.33482
- Batched NN 4D-Var: a performant hybrid model framework for the Integrated Forecasting SystemPreprintarXiv:2610.32582
- ARENA: Active Region attENtion Aggregation for interpretable solar flare predictionPreprintarXiv:2610.32758