Weather prediction
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Accurate medium-range global weather forecasting with 3D neural networks Open
Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves parti…
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Learning skillful medium-range global weather forecasting Open
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 us…
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Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station Open
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 com…
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Can deep learning beat numerical weather prediction? Open
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 …
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Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data Open
We develop elementary weather prediction models using deep convolutional neural networks (CNNs) trained on past weather data to forecast one or two fundamental meteorological fields on a Northern Hemisphere grid with no explicit knowledge …
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Challenges and design choices for global weather and climate models based on machine learning Open
Can models that are based on deep learning and trained on atmospheric data compete with weather and climate models that are based on physical principles and the basic equations of motion? This question has been asked often recently due to …
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Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning Open
It is shown that it is possible to emulate the dynamics of a simple general circulation model with a deep neural network. After being trained on the model, the network can predict the complete model state several time steps ahead—which con…
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Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks Open
The aim of this paper is to present a deep neural network architecture and use it in time series weather prediction.It uses multi stacked LSTMs to map sequences of weather values of the same length.The final goal is to produce two types of…
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Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives Open
In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine.…
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Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting Open
Weather forecasting has gained attention many researchers from various research communities due to its effect to the global human life. The emerging deep learning techniques in the last decade coupled and the wide availability of massive w…
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Predicting weather forecast uncertainty with machine learning Open
Weather forecasts are inherently uncertain. Therefore, for many applications forecasts are only considered valuable if an uncertainty estimate can be assigned to them. Currently, the best method to provide a confidence estimate for individ…
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FuXi: a cascade machine learning forecasting system for 15-day global weather forecast Open
Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has led to state-of-the-art ML models that have superior performance compared to the European Centre for Medium-Range Weather Forecasts …
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Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning Open
Numerical weather prediction models require ever‐growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data‐driven framework that is based on analog forecasting (predi…
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Attribution of Weather and Climate Events Open
Within the past decade, the attribution of extreme weather and climate events has emerged from a theoretical possibility into a subfield of climate science in its own right, providing scientific evidence on the role of anthropogenic climat…
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Scientific Challenges of Convective-Scale Numerical Weather Prediction Open
After extensive efforts over the course of a decade, convective-scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (…
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Progress in subseasonal to seasonal prediction through a joint weather and climate community effort Open
Public expectations have been set for the development of skillful meteorological forecasts of unprecedented leads out to a month or two, filling the so-called subseasonal to seasonal prediction gap. While both the weather and climate commu…
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Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas Open
Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air …
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GraphCast: Learning skillful medium-range global weather forecasting Open
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 cannot directly use…
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Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground Open
Recently, there has been growing interest in the possibility of using neural networks for both weather forecasting and the generation of climate datasets. We use a bottom–up approach for assessing whether it should, in principle, be possib…
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Probabilistic weather forecasting with machine learning Open
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, weath…
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FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators Open
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy and resolution due to high computational cost and…
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FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead Open
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 fro…
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A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning Open
Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has severa…
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Robust prediction of hourly PM2.5 from meteorological data using LightGBM Open
Retrieving historical fine particulate matter (PM2.5) data is key for evaluating the long-term impacts of PM2.5 on the environment, human health and climate change. Satellite-based aerosol optical depth has been used to estimate PM2.5, but…
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Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast Open
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 …
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Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction Open
Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heteroge…
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Introduction to the special issue on “25 years of ensemble forecasting” Open
Twenty‐five years ago the first operational, ensemble forecasts were issued by the European Centre for Medium‐Range Weather Forecasts and the National Centers for Environmental Prediction. These centres were followed in 1996 by the Meteoro…
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Machine learning for numerical weather and climate modelling: a review Open
Machine learning (ML) is increasing in popularity in the field of weather and climate modelling. Applications range from improved solvers and preconditioners, to parameterization scheme emulation and replacement, and more recently even to …
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The Rise of Data-Driven Weather Forecasting: A First Statistical Assessment of Machine Learning–Based Weather Forecasts in an Operational-Like Context Open
Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game changer for the…
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Weakly Coupled Ocean–Atmosphere Data Assimilation in the ECMWF NWP System Open
Numerical weather prediction models are including an increasing number of components of the Earth system. In particular, every forecast now issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) runs with a 3D ocean model…