DWFH: An improved data-driven deep weather forecasting hybrid model using Transductive Long Short Term Memory (T-LSTM)

Article Geosciences, Multidisciplinary

Computing the Ensemble Spread From Deterministic Weather Predictions Using Conditional Generative Adversarial Networks

Ruediger Brecht et al.

Summary: Ensemble prediction systems are valuable tools for weather forecasting, but they have high computational costs and require statistical post-processing. This study proposes using deep-learning-based algorithms to learn the statistical properties of an ensemble prediction system, specifically the ensemble spread, from a single deterministic forecast. The results show that the trained models can accurately predict the ensemble spread based on the control forecast.

GEOPHYSICAL RESEARCH LETTERS (2023)

添加到收藏夹

Article Meteorology & Atmospheric Sciences

Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning

William E. Chapman et al.

Summary: This study examines the use of deep-learning methods for postprocessing weather predictions, finding that these methods can generate reliable and accurate probabilistic forecasts. The deep-learning methods outperform the calibrated Global Ensemble Forecast System in specific meteorological events. Additionally, the results show that deep-learning methods learn quickly and require a relatively short hindcast dataset to compete with the Global Ensemble Forecast System.

MONTHLY WEATHER REVIEW (2022)

添加到收藏夹

Article Green & Sustainable Science & Technology

Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction

David Markovics et al.

Summary: This study compares 24 machine learning models for deterministic day-ahead power forecasting and finds that kernel ridge regression and multilayer perceptron are the most accurate models. Supplementary inputs like Sun position angles and irradiance values can significantly reduce the prediction error. Hyperparameter tuning is essential for optimizing the models' performance.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2022)

添加到收藏夹

Editorial Material Multidisciplinary Sciences

Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI

Matthew Chantry et al.

Summary: The workshop in September 2019 highlighted the growing area of applying machine learning to improve weather and climate prediction, outlining motivations, opportunities, and challenges ahead. This article is part of the theme issue on 'Machine learning for weather and climate modelling'.

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2021)

添加到收藏夹

Article Multidisciplinary Sciences

Can deep learning beat numerical weather prediction?

M. G. Schultz et al.

Summary: The recent hype around artificial intelligence has renewed interest in applying successful deep learning methods in the field of meteorology. Evidence suggests that better weather forecasts can be achieved with the introduction of big data mining and neural networks. However, fundamental breakthroughs are needed before numerical weather models can be completely replaced by DL approaches.

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2021)

添加到收藏夹

Article Meteorology & Atmospheric Sciences

Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models

Jonathan A. Weyn et al.

Summary: The study presents an ensemble prediction system using a computationally efficient Deep Learning Weather Prediction (DLWP) model to recursively predict key atmospheric variables with good skill globally. The model performs well in simulating mid-latitude weather systems and generating tropical cyclones, showing decent predictive capabilities.

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS (2021)

添加到收藏夹

Article Energy & Fuels

A hybrid deep learning model for short-term PV power forecasting

Pengtao Li et al.

APPLIED ENERGY (2020)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Weather forecasting model using Artificial Neural Network

Kumar Abhishek et al.

2ND INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION, CONTROL AND INFORMATION TECHNOLOGY (C3IT-2012) (2012)

添加到收藏夹

DWFH: An improved data-driven deep weather forecasting hybrid model using Transductive Long Short Term Memory (T-LSTM)

相关参考文献

导出引文

分享论文