{"topic":"ai-weather-climate","sort":"top","limit":25,"offset":0,"count":25,"papers":[{"id":"arxiv:1506.04214","kind":"preprint","title":"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting","authors":["Xingjian Shi","Zhourong Chen","Hao Wang","Dit‐Yan Yeung","Wai Kin Wong","Wang‐chun Woo"],"author_count":6,"year":2015,"published":"2015-06-13","venue":null,"cited_by":7708,"doi":null,"arxiv_id":"1506.04214","nct_id":null,"source_url":"http://arxiv.org/abs/1506.04214","archive_url":"https://space-kitty.com/papers/arxiv:1506.04214","updated":"2015-09-19","pmid":null,"pmcid":null,"openalex_id":"W1485009520","pdf_url":"https://arxiv.org/pdf/1506.04214","abstract":"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 propose the convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model for the precipitation nowcasting problem. Experiments show that our ConvLSTM network captures spatiotemporal correlations better and consistently outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm for precipitation nowcasting.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":false,"categories":["Hydrological Forecasting Using AI"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1126/science.adi2336","kind":"article","title":"Learning skillful medium-range global weather forecasting","authors":["Rémi Lam","Álvaro Sánchez‐González","Matthew Willson","Peter Wirnsberger","Meire Fortunato","Ferran Alet","Suman V. Ravuri","Timo Ewalds","Zach Eaton-Rosen","Weihua Hu","Alexander Merose","Stephan Hoyer","George Holland","Oriol Vinyals","Jacklynn Stott","Alexander Pritzel","Shakir Mohamed","Peter Battaglia"],"author_count":18,"year":2023,"published":"2023-11-14","venue":"Science","cited_by":1463,"doi":"10.1126/science.adi2336","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1126/science.adi2336","archive_url":"https://space-kitty.com/papers/doi:10.1126/science.adi2336","updated":null,"pmid":"37962497","pmcid":null,"openalex_id":"W4388654737","pdf_url":null,"abstract":"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 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclone tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting and helps realize the promise of machine learning for modeling complex dynamical systems.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Meteorological Phenomena and Simulations"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Learning skillful medium-range global weather forecasting\" by Rémi Lam, Álvaro Sánchez‐González, Matthew Willson et al., https://doi.org/10.1126/science.adi2336, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.38124/ijisrt/ijisrt24jul073","kind":"article","title":"Prediction of Daily Climate Using Long Short-Term Memory (LSTM) Model","authors":["Jinxin Xu","Zhuoyue Wang","Xinjin Li","Zichao Li","Zhenglin Li"],"author_count":5,"year":2024,"published":"2024-07-12","venue":"International Journal of Innovative Science and Research Technology (IJISRT)","cited_by":975,"doi":"10.38124/ijisrt/ijisrt24jul073","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.38124/ijisrt/ijisrt24jul073","archive_url":"https://space-kitty.com/papers/doi:10.38124/ijisrt/ijisrt24jul073","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W4400591407","pdf_url":"https://www.ijisrt.com/assets/upload/files/IJISRT24JUL073.pdf","abstract":null,"abstract_license":null,"license":"cc-by-nc","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Hydrological Forecasting Using AI"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:2004.13408","kind":"article","title":"Time-series forecasting with deep learning: a survey","authors":["Bryan Y. Lim","Stefan Zohren"],"author_count":2,"year":2021,"published":"2021-02-15","venue":"Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences","cited_by":1848,"doi":"10.1098/rsta.2020.0209","arxiv_id":"2004.13408","nct_id":null,"source_url":"https://doi.org/10.1098/rsta.2020.0209","archive_url":"https://space-kitty.com/papers/arxiv:2004.13408","updated":"2020-09-27","pmid":"33583273","pmcid":null,"openalex_id":"W3022643593","pdf_url":"https://arxiv.org/pdf/2004.13408","abstract":"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 can also facilitate decision support with time series data.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Time Series Analysis and Forecasting"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:2003.00646","kind":"article","title":"A review of machine learning applications in wildfire science and management","authors":["Piyush Jain","Sean C.P. Coogan","Sriram Ganapathi Subramanian","Mark Crowley","Steve Taylor","Mike Flannigan"],"author_count":6,"year":2020,"published":"2020-07-28","venue":"Environmental Reviews","cited_by":848,"doi":"10.1139/er-2020-0019","arxiv_id":"2003.00646","nct_id":null,"source_url":"https://doi.org/10.1139/er-2020-0019","archive_url":"https://space-kitty.com/papers/arxiv:2003.00646","updated":"2020-08-19","pmid":null,"pmcid":null,"openalex_id":"W3008626511","pdf_url":"https://arxiv.org/pdf/2003.00646","abstract":"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 popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Fire effects on ecosystems"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1007/s00500-020-04954-0","kind":"article","title":"Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station","authors":["Pradeep Hewage","Ardhendu Behera","Marcello Trovati","Ella Pereira","Morteza Ghahremani","Francesco Palmieri","Yonghuai Liu"],"author_count":7,"year":2020,"published":"2020-04-23","venue":"Soft Computing","cited_by":741,"doi":"10.1007/s00500-020-04954-0","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1007/s00500-020-04954-0","archive_url":"https://space-kitty.com/papers/doi:10.1007/s00500-020-04954-0","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3019433526","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00500-020-04954-0.pdf","abstract":"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 evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Hydrological Forecasting Using AI"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station\" by Pradeep Hewage, Ardhendu Behera, Marcello Trovati et al., https://doi.org/10.1007/s00500-020-04954-0, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:1706.06279","kind":"article","title":"Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach","authors":["Jintao Ke","Hongyu Zheng","Hai Yang","Xiqun Michael Chen"],"author_count":4,"year":2017,"published":"2017-10-24","venue":"Transportation Research Part C Emerging Technologies","cited_by":728,"doi":"10.1016/j.trc.2017.10.016","arxiv_id":"1706.06279","nct_id":null,"source_url":"https://doi.org/10.1016/j.trc.2017.10.016","archive_url":"https://space-kitty.com/papers/arxiv:1706.06279","updated":"2017-06-20","pmid":null,"pmcid":null,"openalex_id":"W2695874637","pdf_url":"https://arxiv.org/pdf/1706.06279","abstract":"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 and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Traffic Prediction and Management Techniques"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:2002.00469","kind":"article","title":"WeatherBench: A benchmark dataset for data-driven weather forecasting","authors":["Stephan Rasp","Peter D. Dueben","Sebastian Scher","Jonathan A. Weyn","Soukayna Mouatadid","Nils Thuerey"],"author_count":6,"year":2020,"published":"2020-02-02","venue":"Journal of Advances in Modeling Earth Systems","cited_by":662,"doi":"10.1029/2020ms002203","arxiv_id":"2002.00469","nct_id":null,"source_url":"https://arxiv.org/abs/2002.00469","archive_url":"https://space-kitty.com/papers/arxiv:2002.00469","updated":"2020-06-11","pmid":null,"pmcid":null,"openalex_id":"W3003709258","pdf_url":"https://arxiv.org/pdf/2002.00469","abstract":"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 been processed to facilitate the use in machine learning models. We propose simple and clear evaluation metrics which will enable a direct comparison between different methods. Further, we provide baseline scores from simple linear regression techniques, deep learning models, as well as purely physical forecasting models. The dataset is publicly available at https://github.com/pangeo-data/WeatherBench and the companion code is reproducible with tutorials for getting started. We hope that this dataset will accelerate research in data-driven weather forecasting.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["physics.ao-ph","stat.ML"],"details":null,"provenance":"arxiv+openalex","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1016/j.compag.2018.09.040","kind":"article","title":"An IoT based smart irrigation management system using Machine learning and open source technologies","authors":["Amarendra Goap","Deepak Sharma","Alok Kumar Shukla","C. Rama Krishna"],"author_count":4,"year":2018,"published":"2018-10-09","venue":"Computers and Electronics in Agriculture","cited_by":679,"doi":"10.1016/j.compag.2018.09.040","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.compag.2018.09.040","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.compag.2018.09.040","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2896625020","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Smart Agriculture and AI"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1098/rsta.2020.0097","kind":"article","title":"Can deep learning beat numerical weather prediction?","authors":["Martin G. Schultz","Clara Betancourt","Bing Gong","Felix Kleinert","Michael Langguth","Lukas Hubert Leufen","Amirpasha Mozaffari","Scarlet Stadtler"],"author_count":8,"year":2021,"published":"2021-02-15","venue":"Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences","cited_by":568,"doi":"10.1098/rsta.2020.0097","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1098/rsta.2020.0097","archive_url":"https://space-kitty.com/papers/doi:10.1098/rsta.2020.0097","updated":null,"pmid":"33583266","pmcid":null,"openalex_id":"W3132280960","pdf_url":null,"abstract":"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 entails a review of state-of-the-art machine learning concepts and their applicability to weather data with its pertinent statistical properties. We think that it is not inconceivable that numerical weather models may one day become obsolete, but a number of fundamental breakthroughs are needed before this goal comes into reach. This article is part of the theme issue 'Machine learning for weather and climate modelling'.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Meteorological Phenomena and Simulations"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Can deep learning beat numerical weather prediction?\" by Martin G. Schultz, Clara Betancourt, Bing Gong et al., https://doi.org/10.1098/rsta.2020.0097, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:2311.07222","kind":"article","title":"Neural General Circulation Models for Weather and Climate","authors":["Dmitrii Kochkov","Janni Yuval","Ian Langmore","Peter Norgaard","Jamie Smith","Griffin Mooers","Milan Klöwer","James Lottes","Stephan Rasp","Peter Düben","Sam Hatfield","Peter Battaglia","Alvaro Sanchez-Gonzalez","Matthew Willson","Michael P. Brenner","Stephan Hoyer"],"author_count":16,"year":2023,"published":"2023-11-13","venue":"Nature","cited_by":452,"doi":"10.1038/s41586-024-07744-y","arxiv_id":"2311.07222","nct_id":null,"source_url":"https://arxiv.org/abs/2311.07222","archive_url":"https://space-kitty.com/papers/arxiv:2311.07222","updated":"2024-03-08","pmid":"39039241","pmcid":null,"openalex_id":"W4400880443","pdf_url":"https://arxiv.org/pdf/2311.07222","abstract":"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 combines a differentiable solver for atmospheric dynamics with ML components, and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best ML and physics-based methods. NeuralGCM is competitive with ML models for 1-10 day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for 1-15 day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics such as global mean temperature for multiple decades, and climate forecasts with 140 km resolution exhibit emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs, and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["physics.ao-ph","cs.LG","physics.comp-ph"],"details":null,"provenance":"arxiv+openalex","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1016/j.rser.2022.112364","kind":"article","title":"Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction","authors":["Dávid Markovics","Martin János Mayer"],"author_count":2,"year":2022,"published":"2022-03-23","venue":"Renewable and Sustainable Energy Reviews","cited_by":499,"doi":"10.1016/j.rser.2022.112364","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.rser.2022.112364","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.rser.2022.112364","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W4220726206","pdf_url":"https://www.sciencedirect.com/science/article/pii/S136403212200274X/pdf","abstract":"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 regression and multilayer perceptron with an up to 44.6% forecast skill score over persistence. Supplementing the basic NWP data with Sun position angles and statistically processed irradiance values as the inputs of the learning models results in a 13.1% decrease of the root mean square error (RMSE), which underlines the importance of the predictor selection. The hyperparameter tuning is essential to exploit the full potential of the models, especially for the less robust models, which are prone to under or overfitting without proper tuning. The overall best forecasts have a 13.9% lower RMSE compared to the baseline scenario of using linear regression. Moreover, the power forecasts based on only daily average irradiance forecasts and the Sun position angles have only a 1.5% higher RMSE than the best scenario, which demonstrates the effectiveness of machine learning even for limited data availability. The results of this paper can support both researchers and practitioners in constructing the best data-driven techniques for NWP-based PV power forecasting.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Solar Radiation and Photovoltaics"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction\" by Dávid Markovics, Martin János Mayer, https://doi.org/10.1016/j.rser.2022.112364, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-024-08252-9","kind":"article","title":"Probabilistic weather forecasting with machine learning","authors":["Ilan Price","Álvaro Sánchez‐González","Ferran Alet","Tom R. Andersson","Andrew El-Kadi","Dominic A. Masters","Timo Ewalds","Jacklynn Stott","Shakir Mohamed","Peter Battaglia","Rémi Lam","Matthew Willson"],"author_count":12,"year":2024,"published":"2024-12-04","venue":"Nature","cited_by":433,"doi":"10.1038/s41586-024-08252-9","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-024-08252-9","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-024-08252-9","updated":null,"pmid":"39633054","pmcid":null,"openalex_id":"W4405031167","pdf_url":null,"abstract":"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, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range Weather Forecasts 4 . GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude–longitude resolution, for more than 80 surface and atmospheric variables, in 8 min. It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Meteorological Phenomena and Simulations"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Probabilistic weather forecasting with machine learning\" by Ilan Price, Álvaro Sánchez‐González, Ferran Alet et al., https://doi.org/10.1038/s41586-024-08252-9, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.jhydrol.2019.04.085","kind":"article","title":"Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions","authors":["Guomin Huang","Lifeng Wu","Xin Ma","Weiqiang Zhang","Junliang Fan","Xiang Jun Yu","Wenzhi Zeng","Hanmi Zhou"],"author_count":8,"year":2019,"published":"2019-04-29","venue":"Journal of Hydrology","cited_by":565,"doi":"10.1016/j.jhydrol.2019.04.085","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.jhydrol.2019.04.085","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.jhydrol.2019.04.085","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2942851257","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Hydrological Forecasting Using AI"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1016/j.apenergy.2014.02.057","kind":"article","title":"Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy","authors":["Rishee Kumar Jain","Kevin M. Smith","Patricia J. Culligan","John Eric Taylor"],"author_count":4,"year":2014,"published":"2014-03-19","venue":"Applied Energy","cited_by":602,"doi":"10.1016/j.apenergy.2014.02.057","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.apenergy.2014.02.057","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.apenergy.2014.02.057","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2064469609","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Building Energy and Comfort Optimization"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1016/j.apenergy.2020.114683","kind":"article","title":"Building thermal load prediction through shallow machine learning and deep learning","authors":["Zhe Wang","Tianzhen Hong","Mary Ann Piette"],"author_count":3,"year":2020,"published":"2020-02-19","venue":"Applied Energy","cited_by":507,"doi":"10.1016/j.apenergy.2020.114683","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.apenergy.2020.114683","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.apenergy.2020.114683","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3008571545","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Building Energy and Comfort Optimization"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1016/j.agrformet.2018.08.019","kind":"article","title":"Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China","authors":["Junliang Fan","Wenjun Yue","Lifeng Wu","Fucang Zhang","Huanjie Cai","Xiukang Wang","Xianghui Lu","Youzhen Xiang"],"author_count":8,"year":2018,"published":"2018-09-01","venue":"Agricultural and Forest Meteorology","cited_by":550,"doi":"10.1016/j.agrformet.2018.08.019","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.agrformet.2018.08.019","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.agrformet.2018.08.019","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2889246260","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Hydrological Forecasting Using AI"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.3390/fi15080255","kind":"article","title":"A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks","authors":["Vaia I. Kontopoulou","Athanasios D. Panagopoulos","Iοannis Kakkos","George K. Matsopoulos"],"author_count":4,"year":2023,"published":"2023-07-30","venue":"Future Internet","cited_by":436,"doi":"10.3390/fi15080255","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3390/fi15080255","archive_url":"https://space-kitty.com/papers/doi:10.3390/fi15080255","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W4385399619","pdf_url":"https://www.mdpi.com/1999-5903/15/8/255/pdf?version=1690707040","abstract":"In the broad scientific field of time series forecasting, the ARIMA models and their variants have been widely applied for half a century now due to their mathematical simplicity and flexibility in application. However, with the recent advances in the development and efficient deployment of artificial intelligence models and techniques, the view is rapidly changing, with a shift towards machine and deep learning approaches becoming apparent, even without a complete evaluation of the superiority of the new approach over the classic statistical algorithms. Our work constitutes an extensive review of the published scientific literature regarding the comparison of ARIMA and machine learning algorithms applied to time series forecasting problems, as well as the combination of these two approaches in hybrid statistical-AI models in a wide variety of data applications (finance, health, weather, utilities, and network traffic prediction). Our review has shown that the AI algorithms display better prediction performance in most applications, with a few notable exceptions analyzed in our Discussion and Conclusions sections, while the hybrid statistical-AI models steadily outperform their individual parts, utilizing the best algorithmic features of both worlds.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Forecasting Techniques and Applications"],"details":null,"provenance":"openalex","attribution":"Abstract of \"A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks\" by Vaia I. Kontopoulou, Athanasios D. Panagopoulos, Iοannis Kakkos et al., https://doi.org/10.3390/fi15080255, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:2008.04060","kind":"article","title":"Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt","authors":["Mohsen Shahhosseini","Guiping Hu","Isaiah Huber","Sotirios V. Archontoulis"],"author_count":4,"year":2021,"published":"2021-01-15","venue":"Scientific Reports","cited_by":452,"doi":"10.1038/s41598-020-80820-1","arxiv_id":"2008.04060","nct_id":null,"source_url":"https://doi.org/10.1038/s41598-020-80820-1","archive_url":"https://space-kitty.com/papers/arxiv:2008.04060","updated":"2021-03-01","pmid":"33452349","pmcid":null,"openalex_id":"W3048168082","pdf_url":"https://www.nature.com/articles/s41598-020-80820-1.pdf","abstract":"This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Smart Agriculture and AI"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1016/j.jclepro.2021.128566","kind":"article","title":"Deep learning models for solar irradiance forecasting: A comprehensive review","authors":["Pratima Kumari","Durga Toshniwal"],"author_count":2,"year":2021,"published":"2021-08-11","venue":"Journal of Cleaner Production","cited_by":447,"doi":"10.1016/j.jclepro.2021.128566","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.jclepro.2021.128566","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.jclepro.2021.128566","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3191161296","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Solar Radiation and Photovoltaics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1088/1748-9326/aae159","kind":"article","title":"Machine learning methods for crop yield prediction and climate change impact assessment in agriculture","authors":["Andrew Crane‐Droesch"],"author_count":1,"year":2018,"published":"2018-09-14","venue":"Environmental Research Letters","cited_by":497,"doi":"10.1088/1748-9326/aae159","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1088/1748-9326/aae159","archive_url":"https://space-kitty.com/papers/doi:10.1088/1748-9326/aae159","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2891765392","pdf_url":"https://iopscience.iop.org/article/10.1088/1748-9326/aae159/pdf","abstract":"Crop yields are critically dependent on weather. A growing empirical literature models this relationship in order to project climate change impacts on the sector. We describe an approach to yield modeling that uses a semiparametric variant of a deep neural network, which can simultaneously account for complex nonlinear relationships in high-dimensional datasets, as well as known parametric structure and unobserved cross-sectional heterogeneity. Using data on corn yield from the US Midwest, we show that this approach outperforms both classical statistical methods and fully-nonparametric neural networks in predicting yields of years withheld during model training. Using scenarios from a suite of climate models, we show large negative impacts of climate change on corn yield, but less severe than impacts projected using classical statistical methods. In particular, our approach is less pessimistic in the warmest regions and the warmest scenarios.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Climate change impacts on agriculture"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Machine learning methods for crop yield prediction and climate change impact assessment in agriculture\" by Andrew Crane‐Droesch, https://doi.org/10.1088/1748-9326/aae159, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:2312.15796","kind":"preprint","title":"GenCast: Diffusion-based ensemble forecasting for medium-range weather","authors":["Ilan Price","Alvaro Sanchez-Gonzalez","Ferran Alet","Tom R. Andersson","Andrew El-Kadi","Dominic Masters","Timo Ewalds","Jacklynn Stott","Shakir Mohamed","Peter Battaglia","Remi Lam","Matthew Willson"],"author_count":12,"year":2023,"published":"2023-12-25","venue":null,"cited_by":66,"doi":null,"arxiv_id":"2312.15796","nct_id":null,"source_url":"https://arxiv.org/abs/2312.15796","archive_url":"https://space-kitty.com/papers/arxiv:2312.15796","updated":"2024-05-01","pmid":null,"pmcid":null,"openalex_id":"W4390306794","pdf_url":"https://arxiv.org/pdf/2312.15796","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. Here, we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, the European Centre for Medium-Range Forecasts (ECMWF)'s ensemble forecast, ENS. Unlike traditional approaches, which are based on numerical weather prediction (NWP), GenCast is a machine learning weather prediction (MLWP) method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-hour steps and 0.25 degree latitude-longitude resolution, for over 80 surface and atmospheric variables, in 8 minutes. It has greater skill than ENS on 97.4% of 1320 targets we evaluated, and better predicts extreme weather, tropical cyclones, and wind power production. This work helps open the next chapter in operational weather forecasting, where critical weather-dependent decisions are made with greater accuracy and efficiency.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":false,"categories":["cs.LG","physics.ao-ph"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:2304.02948","kind":"preprint","title":"FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead","authors":["Kang Chen","Tao Han","Junchao Gong","Lei Bai","Fenghua Ling","Jing-Jia Luo","Xi Chen","Leiming Ma","Tianning Zhang","Rui Su","Yuanzheng Ci","Bin Li","Xiaokang Yang","Wanli Ouyang"],"author_count":14,"year":2023,"published":"2023-04-06","venue":"Commun. Earth Environ. 6, 518 (2025)","cited_by":137,"doi":"10.1038/s43247-025-02502-y","arxiv_id":"2304.02948","nct_id":null,"source_url":"https://arxiv.org/abs/2304.02948","archive_url":"https://space-kitty.com/papers/arxiv:2304.02948","updated":"2023-04-06","pmid":null,"pmcid":null,"openalex_id":"W4362706666","pdf_url":"https://arxiv.org/pdf/2304.02948","abstract":"We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI). Different from existing data-driven weather forecast methods, FengWu solves the medium-range forecast problem from a multi-modal and multi-task perspective. Specifically, a deep learning architecture equipped with model-specific encoder-decoders and cross-modal fusion Transformer is elaborately designed, which is learned under the supervision of an uncertainty loss to balance the optimization of different predictors in a region-adaptive manner. Besides this, a replay buffer mechanism is introduced to improve medium-range forecast performance. With 39-year data training based on the ERA5 reanalysis, FengWu is able to accurately reproduce the atmospheric dynamics and predict the future land and atmosphere states at 37 vertical levels on a 0.25° latitude-longitude resolution. Hindcasts of 6-hourly weather in 2018 based on ERA5 demonstrate that FengWu performs better than GraphCast in predicting 80\\% of the 880 reported predictands, e.g., reducing the root mean square error (RMSE) of 10-day lead global z500 prediction from 733 to 651 $m^{2}/s^2$. In addition, the inference cost of each iteration is merely 600ms on NVIDIA Tesla A100 hardware. The results suggest that FengWu can significantly improve the forecast skill and extend the skillful global medium-range weather forecast out to 10.75 days lead (with ACC of z500 > 0.6) for the first time.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":false,"categories":["cs.AI","cs.LG","physics.ao-ph"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:2211.02556","kind":"preprint","title":"Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast","authors":["Kaifeng Bi","Lingxi Xie","Hengheng Zhang","Xin Chen","Xiaotao Gu","Qi Tian"],"author_count":6,"year":2022,"published":"2022-11-03","venue":null,"cited_by":118,"doi":null,"arxiv_id":"2211.02556","nct_id":null,"source_url":"https://arxiv.org/abs/2211.02556","archive_url":"https://space-kitty.com/papers/arxiv:2211.02556","updated":"2022-11-03","pmid":null,"pmcid":null,"openalex_id":"W4308505112","pdf_url":"https://arxiv.org/pdf/2211.02556","abstract":"In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For this purpose, we establish a data-driven environment by downloading $43$ years of hourly global weather data from the 5th generation of ECMWF reanalysis (ERA5) data and train a few deep neural networks with about $256$ million parameters in total. The spatial resolution of forecast is $0.25^\\circ\\times0.25^\\circ$, comparable to the ECMWF Integrated Forecast Systems (IFS). More importantly, for the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy (latitude-weighted RMSE and ACC) of all factors (e.g., geopotential, specific humidity, wind speed, temperature, etc.) and in all time ranges (from one hour to one week). There are two key strategies to improve the prediction accuracy: (i) designing a 3D Earth Specific Transformer (3DEST) architecture that formulates the height (pressure level) information into cubic data, and (ii) applying a hierarchical temporal aggregation algorithm to alleviate cumulative forecast errors. In deterministic forecast, Pangu-Weather shows great advantages for short to medium-range forecast (i.e., forecast time ranges from one hour to one week). Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast (e.g., tropical cyclone tracking) and large-member ensemble forecast in real-time. Pangu-Weather not only ends the debate on whether AI-based methods can surpass conventional NWP methods, but also reveals novel directions for improving deep learning weather forecast systems.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":false,"categories":["physics.ao-ph","cs.AI","cs.CV","cs.LG"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1175/bams-d-16-0123.1","kind":"article","title":"Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather","authors":["Amy McGovern","Kimberly L. Elmore","David John Gagne","Sue Ellen Haupt","Christopher D. Karstens","Ryan Lagerquist","Travis M. Smith","John Kino Yamaguchi Williams"],"author_count":8,"year":2017,"published":"2017-03-27","venue":"Bulletin of the American Meteorological Society","cited_by":444,"doi":"10.1175/bams-d-16-0123.1","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1175/bams-d-16-0123.1","archive_url":"https://space-kitty.com/papers/doi:10.1175/bams-d-16-0123.1","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2601923741","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Meteorological Phenomena and Simulations"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."}],"next":"https://space-kitty.com/api/v1/topics/ai-weather-climate/papers?sort=top&limit=25&offset=25"}