Bing Gong

• PostDoc Position at Forschungszentrum Jülich

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 for the well-being of our and future generations. Here, we propose AtmoRep, a novel, task-independent stochasti...

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 are aiming to provide an efficient and easy-to-understand deep neural network – CLGAN (convolutional long short-t...

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 expensive. Recently, the potential of deep neural networks to generate bespoke weather forecasts has been explore...

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, we are aiming to provide an efficient and easy-to-understand model - CLGAN, to improve the nowcasting skills o...

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 significant challenges for Earth system researchers. Even though the FAIR principle is well known to many scientists, r...

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 expensive. Recently, the potential of deep neural networks to generate bespoken weather forecasts has been explor...

Artificial intelligence for air quality IntelliAQ is an ERC Advanced Grant project to explore the application of cutting-edge machine learning techniques to global air quality data in combination with high resolution geospatial and weather data. It combines novel data management and data science approaches to build the foundation for innovative air...

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 Units (GPUs) and its fast interconnect via InfiniBand, it is an ideal machine for large-scale Artificial Intelligen...

In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the J\"ulich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Units (GPUs) and its fast interconnect via InfiniBand, it is an ideal machine for large-scale Artificial Intellig...

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 mi...

As knowledge about the cirrus clouds in the lower stratosphere is limited, reliable long-term measurements are needed to assess their characteristics, radiative impact and important role in upper troposphere and lower stratosphere (UTLS) chemistry. We used 6 years (2006–2012) of Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) meas...

Abstract. As knowledge about the cirrus clouds in the lower stratosphere is limited, reliable long-term measurements are needed to assess their characteristics, radiative impact and important role in upper troposphere and lower stratosphere (UTLS) chemistry. To investigate the global and seasonal distribution of stratospheric cirrus clouds, we used...

Recent advances in technology have empowered the widespread application of cyber–physical systems in manufacturing and fostered the Industry 4.0 paradigm. In the factories of the future, it is possible that all items, including operators, will be equipped with integrated communication and data processing capabilities. Operators can become part of t...

Containers stock a single code along with its dependencies so it can run reliably and efficiently in different computing environments. They promise the same level of isolation and security as a virtual machine and a higher degree of integration with the host operating system (OS). The main benefits of containers are, from a user perspective: greate...

This paper proposes a framework for an Air Quality Decision Support System (AQDSS), and as a proof of concept, develops an Internet of Things (IoT) application based on this framework. This application was assessed by means of a case study in the City of Madrid. We employed different sensors and combined outdoor and indoor data with spatiotemporal...

This paper explores the effects of enforced Chinese policies regarding environmental matters at the provincial level. The paper proposes several features and explores datasets from the last four Five-Year Plans, which extensively covered the period from 1995 to 2015. As the datasets are multidimensional, dimensional reduction is applied with a cont...

Although differential evolution (DE) algorithms have been widely proposed for tackling various of problems, the trade-off among population diversity, global and local exploration ability, and convergence rate is hard to maintain with the existing strategies. From this respective, this paper presents some new mutation strategies of DE by applying th...

The objective of this study is to provide a framework for relocating or reconfiguring existing pollution monitoring station networks by using feature selection and data mining techniques. This methodology enables a partial redesign based on the maximization of the available information that is gathered by the pollution networks by the optimal data...

Rare events, especially those that could potentially negatively impact society, often require humans’ decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In thi...

To ensure green manufacturing, the energy consumption of production processes should be transparent and minimized. Also, to achieve the desired level of energy consumption awareness and efficiency improvements, energy use should be measured in more detail and linked to production data. In this scenario, real-time monitoring of energy consumption re...

The objective of this study was to apply preprocessing and ensemble artificial intelligence classifiers to forecast daily maximum ozone threshold exceedances in the Hong Kong area. Preprocessing methods, including over-sampling, under-sampling, and the synthetic minority over-sampling technique, were employed to address the imbalance data problem....

Ozone is one of the worst harmful pollutants nowadays which affects the public health, so it is necessary to predict ozone level accurately in order to prevent the public from exposing to the pollution when it exceeds the limits. This study aims to predict daily maximum ozone concentrations in the metropolitan area of Mexico City by using four indi...

Using structural equation modelling (SEM), this paper empirically tests the relationships between learning, participation, capacity building, project performance and project sustainability with reference to anti-poverty projects in rural China. The findings show that (a) project stakeholders' learning behaviour has a significant influence on capaci...