Michael Langguth
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View article: TOAR-classifier v2: A data-driven classification tool for global air quality stations
TOAR-classifier v2: A data-driven classification tool for global air quality stations Open
Accurate characterization of station locations is crucial for reliable air quality assessments such as the Tropospheric Ozone Assessment Report (TOAR). This study introduces a machine learning approach to classify 23,974 stations in the un…
View article: RAINA - High-resolution nowcasting of precipitation and wind extremes with a foundation model for the atmosphere
RAINA - High-resolution nowcasting of precipitation and wind extremes with a foundation model for the atmosphere Open
Data-driven weather prediction models based on deep learning have been on the rise for several years and have outperformed traditional physics-based numerical models in various benchmark forecasting scores. However, a significant challenge…
View article: Applying the DestinE Extremes digital twin to air quality forecasts and emission scenario simulations
Applying the DestinE Extremes digital twin to air quality forecasts and emission scenario simulations Open
Events of extreme air pollution pose threats to humans and the environment. To investigate air quality under extreme atmospheric situations, the DestinE air quality use case developed a comprehensive user interface that enables high-resolu…
View article: Downscaling with the foundation model AtmoRep
Downscaling with the foundation model AtmoRep Open
In recent years, deep neural networks (DNN) to enhance the resolution of meteorological data, known as statistical downscaling, have surpassed classical statistical methods that have been developed previously with respect to several valida…
View article: AtmoRep: large scale representation learning for atmospheric dynamics
AtmoRep: large scale representation learning for atmospheric dynamics Open
The atmosphere affects humans in a multitude of ways, from loss of lives due to adverse weather effects to long-term social and economic impacts. Very recently, AI-based models have shown tremendous potential in reducing the computational …
View article: AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning
AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning Open
The atmosphere affects humans in a multitude of ways, from loss of life due to adverse weather effects to long-term social and economic impacts on societies. Computer simulations of atmospheric dynamics are, therefore, of great importance …
View article: CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting
CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting Open
The prediction of precipitation patterns up to 2 h ahead, also known as precipitation nowcasting, at high spatiotemporal resolutions is of great relevance in weather-dependent decision-making and early warning systems. In this study, we ar…
View article: Deep learning models for generation of precipitation maps based on numerical weather prediction
Deep learning models for generation of precipitation maps based on numerical weather prediction Open
Numerical weather prediction (NWP) models are atmospheric simulations that imitate the dynamics of the atmosphere and provide high-quality forecasts. One of the most significant limitations of NWP is the elevated amount of computational re…
View article: Statistical downscaling of precipitation with deep neural networks
Statistical downscaling of precipitation with deep neural networks Open
Accurate weather predictions are essential for many aspects of social society. Providing a reliable high-resolution precipitation field is essential to capture the finer scale of heavy precipitation events,  which is normally poorly r…
View article: Towards a benchmark dataset for statistical downscaling of meteorological fields
Towards a benchmark dataset for statistical downscaling of meteorological fields Open
The representation of the atmospheric state at high spatial resolution is of particular relevance in various domains of Earth science. While global reanalysis datasets such as ERA5 provide comprehensive repositories of meteorological data,…
View article: Comment on egusphere-2022-859
Comment on egusphere-2022-859 Open
Abstract. The prediction of precipitation patterns at high spatio-temporal resolution up to two hours ahead, also known as precipitation nowcasting, is of great relevance in weather-dependant decision-making and early warning systems. In t…
View article: Comment on egusphere-2022-859
Comment on egusphere-2022-859 Open
Abstract. The prediction of precipitation patterns at high spatio-temporal resolution up to two hours ahead, also known as precipitation nowcasting, is of great relevance in weather-dependant decision-making and early warning systems. In t…
View article: Temperature forecasting by deep learning methods
Temperature forecasting by deep learning methods Open
Numerical weather prediction (NWP) models solve a system of partial differential equations based on physical laws to forecast the future state of the atmosphere. These models are deployed operationally, but they are computationally very ex…
View article: Comment on egusphere-2022-859
Comment on egusphere-2022-859 Open
Abstract. The prediction of precipitation patterns at high spatio-temporal resolution up to two hours ahead, also known as precipitation nowcasting, is of great relevance in weather-dependant decision-making and early warning systems. In t…
View article: Comment on egusphere-2022-648
Comment on egusphere-2022-648 Open
Abstract. Numerical Weather Prediction models (NWP) are atmospheric simulations that imitate the dynamics of the atmosphere and provide high-quality forecasts. One of the most significant limitations of NWP is the elevated amount of comput…
View article: CLGAN: A GAN-based video prediction model for precipitation nowcasting
CLGAN: A GAN-based video prediction model for precipitation nowcasting Open
The prediction of precipitation patterns at high spatio-temporal resolution up to two hours ahead, also known as precipitation nowcasting, is of great relevance in weather-dependant decision-making and early warning systems. In this study,…
View article: CLGAN: Guizhou ML-AWS precipitation dataset
CLGAN: Guizhou ML-AWS precipitation dataset Open
This repository provides the preprocessed datasets and source codes, which are used in the study 'CLGAN: A GAN-based video prediction model for precipitation nowcasting' by Ji et al. (2022). This allows the user to reproduce the presented …
View article: CLGAN: Guizhou ML-AWS precipitation dataset
CLGAN: Guizhou ML-AWS precipitation dataset Open
This repository provides the preprocessed datasets and source codes, which are used in the study 'CLGAN: A GAN-based video prediction model for precipitation nowcasting' by Ji et al. (2022). This allows the user to reproduce the presented …
View article: Deep learning models for generation of precipitation maps based on Numerical Weather Prediction
Deep learning models for generation of precipitation maps based on Numerical Weather Prediction Open
Numerical Weather Prediction models (NWP) are atmospheric simulations that imitate the dynamics of the atmosphere and provide high-quality forecasts. One of the most significant limitations of NWP is the elevated amount of computational re…
View article: Statistical downscaling of the 2m temperature with a generative adversarial network (GAN)
Statistical downscaling of the 2m temperature with a generative adversarial network (GAN) Open
<p>Despite continuous improvements in numerical atmospheric models and the ever-growing computational resources, global weather forecast and climate models still operate with grid spacings between 10 and 50 km. While a further increa…
View article: Comment on gmd-2021-430
Comment on gmd-2021-430 Open
Abstract. Numerical weather prediction (NWP) models solve a system of partial differential equations based on physical laws to forecast the future state of the atmosphere. These models are deployed operationally, but they are computational…
View article: Comment on gmd-2021-430
Comment on gmd-2021-430 Open
Numerical weather prediction (NWP) models solve a system of partial differential equations based on physical laws to forecast the future state of the atmosphere. These models are deployed operationally, but they are computationally very ex…
View article: GAN-based video prediction model for precipitation nowcasting
GAN-based video prediction model for precipitation nowcasting Open
<p>Detecting and predicting heavy precipitation for the next few hours is of great importance in weather related decision-making and early warning systems. Although great progress has been achieved in convective-permitting numerical …
View article: Stochastic downscaling of the 2m temperature with a generative adversarial network (GAN)
Stochastic downscaling of the 2m temperature with a generative adversarial network (GAN) Open
<p>Inspired by the success of superresolution applications in computer vision, deep neural networks have recently been recognized as an appealing approach for statistical downscaling of meteorological fields. While further increasing…
View article: Temperature forecasting by deep learning methods
Temperature forecasting by deep learning methods Open
Numerical weather prediction (NWP) models solve a system of partial differential equations based on physical laws to forecast the future state of the atmosphere. These models are deployed operationally, but they are computationally very ex…
View article: HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction
HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction Open
Machine learning (ML) applications in weather and climate are gaining momentum as big data and the immense increase in High-performance computing (HPC) power are paving the way. Ensuring FAIR data and reproducible ML practices are signific…
View article: JUWELS Booster -- A Supercomputer for Large-Scale AI Research
JUWELS Booster -- A Supercomputer for Large-Scale AI Research Open
In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the Jülich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Unit…
View article: Can deep learning beat numerical weather prediction?
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 …
View article: Implementing the HYbrid MAss flux Convection Scheme (HYMACS) in ICON – First idealized tests and adaptions to the dynamical core for local mass sources
Implementing the HYbrid MAss flux Convection Scheme (HYMACS) in ICON – First idealized tests and adaptions to the dynamical core for local mass sources Open
In this study, the Hybrid MAss flux Convection Scheme (HYMACS) is implemented in the ICOsahedral Non‐hydrostatic (ICON) weather prediction model. In contrast to conventional convection parametrization schemes, the convective up‐ and downdr…