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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’.

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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 the smart manufacturing systems, and this fosters a paradigm shift from independent automated and human activities to human–cyber–physical systems (HCPSs). In this context, a Healthy Operator 4.0 (HO4.0) concept was proposed, based on a systemic view of the Industrial Internet of Things (IIoT) and wearable technology. For the implementation of this relatively new concept, we constructed a unified architecture to support the integration of different enabling technologies. We designed an implementation model to facilitate the practical application of this concept in industry. The main enabling technologies of the model are introduced afterward. In addition, a prototype system was developed, and relevant experiments were conducted to demonstrate the feasibility of the proposed system architecture and the implementation framework, as well as some of the derived benefits.

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. We used 6 years (2006–2012) of Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) measurements to investigate the global and seasonal distribution of stratospheric cirrus clouds and compared the MIPAS results with results derived from the latest version (V4.x) of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. For the identification of stratospheric cirrus clouds, precise information on both the cloud top height (CTH) and the tropopause height is crucial. Here, we used lapse rate tropopause heights estimated from the ERA-Interim global reanalysis. Considering the uncertainties of the tropopause heights and the vertical sampling grid, we define CTHs more than 0.5 km above the tropopause as stratospheric for CALIPSO data. For MIPAS data, we took into account the coarser vertical sampling grid and the broad field of view so that we considered cirrus CTHs detected more than 0.75 km above the tropopause as stratospheric. Further sensitivity tests were conducted to rule out sampling artefacts in MIPAS data. The global distribution of stratospheric cirrus clouds was derived from night-time measurements because of the higher detection sensitivity of CALIPSO. In both data sets, MIPAS and CALIPSO, the stratospheric cirrus cloud occurrence frequencies are significantly higher in the tropics than in the extra-tropics. Tropical hotspots of stratospheric cirrus clouds associated with deep convection are located over equatorial Africa, South and Southeast Asia, the western Pacific, and South America. Stratospheric cirrus clouds were more often detected in December–February (15 %) than June–August (8 %) in the tropics (±20∘). At northern and southern middle latitudes (40–60∘), MIPAS observed about twice as many stratospheric cirrus clouds (occurrence frequencies of 4 %–5 % for MIPAS rather than about 2 % for CALIPSO). We attribute more frequent observations of stratospheric cirrus clouds with MIPAS to the higher detection sensitivity of the instrument to optically thin clouds. In contrast to the difference between daytime and night-time occurrence frequencies of stratospheric cirrus clouds by a factor of about 2 in zonal means in the tropics (4 % and 10 %, respectively) and at middle latitudes for CALIPSO data, there is little diurnal cycle in MIPAS data, in which the difference of occurrence frequencies in the tropics is about 1 percentage point in zonal mean and about 0.5 percentage point at middle latitudes. The difference between CALIPSO day and night measurements can also be attributed to their differences in detection sensitivity. Future work should focus on better understanding the origin of the stratospheric cirrus clouds and their impact on radiative forcing and climate.

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