Michael Baldauf

The implementation of a “classical” Discontinuous Galerkin (DG) solver for atmospheric flows is presented that is designed for efficient use in numerical weather prediction, climate simulations, and meteorological research both on the whole sphere and for limited area modeling. To this purpose the horizontally explicit, vertically implicit (HEVI) approach is used together with implicit-explicit (IMEX)-Runge-Kutta (RK) time integration schemes and a moderate spatial approximation order (order 4 or 5). Two Euler equation sets using mass, momentum and either density weighted potential temperature θ or total energy E as prognostic variables are compared by several idealised test cases. Details of the formulation of the Euler equations in covariant form using the Ricci tensor calculus, the linearisations needed for HEVI (especially for the total energy set), boundary conditions for an IMEX-RK scheme, and filtering for numerical stabilisation are given. Furthermore, the implementation of distributed memory parallelisation, the tensor product representation for prismatic grid cells, and optimisations for the HEVI formulation, are outlined. These developments lead to the so-called BRIDGE code, which will serve as a code base for a later DG extension of the well established ICON model. From the used idealised test cases, which are standard benchmarks for dynamical core development for the atmosphere, we conclude that the equation set using total energy E has better well-balancing properties than the set using θ . This result can be confirmed by a normal mode stability analysis. However, in some tests the set using E suffers more from non-linear instabilities that can only be partially solved by filtering.

The implementation of a 'classical' Discontinuous Galerkin (DG) solver for atmospheric flows is presented that is designed for efficient use in numerical weather prediction, climate simulations, and meteorological research both on the whole sphere and for limited area modeling. To this purpose the horizontally explicit, vertically implicit (HEVI) approach is used together with implicit-explicit (IMEX)-Runge-Kutta (RK) time integration schemes and a moderate spatial approximation order (order 4 or 5). Two Euler equation sets using mass, momentum and either density weighted potential temperature θ or total energy E as prognostic variables are compared by several idealised test cases. Details of the formulation of the Euler equations in covariant form using the Ricci tensor calculus, the linearisations needed for HEVI (especially for the total energy set), boundary conditions for an IMEX-RK scheme, and filtering for numerical stabilisation are given. Furthermore, the implementation of distributed memory parallelisation, the tensor product representation for prismatic grid cells, and optimisations for the HEVI formulation, are outlined. These developments lead to the so-called BRIDGE code, which will serve as a code base for a later DG extension of the well established ICON model. From the used idealised test cases, which are standard benchmarks for dynamical core development for the atmosphere, we conclude that the equation set using total energy E has better well-balancing properties than the set using θ. This result can be confirmed by a normal mode stability analysis. However, in some tests the set using E suffers more from non-linear instabilities that can only partially solved by filtering.

The implementation of a 'classical' Discontinuous Galerkin (DG) solver for atmospheric flows is presented that is designed for efficient use in numerical weather prediction, climate simulations, and meteorological research both on the whole sphere and for limited area modeling. To this purpose the horizontally explicit, vertically implicit (HEVI) approach is used together with implicit-explicit (IMEX)-Runge-Kutta (RK) time integration schemes and a moderate spatial approximation order (order 4 or 5). Two Euler equation sets using mass, momentum and either density weighted potential temperature θ or total energy E as prognostic variables are compared by several idealised test cases. Details of the formulation of the Euler equations in covariant form using the Ricci tensor calculus, the linearisations needed for HEVI (especially for the total energy set), boundary conditions for an IMEX-RK scheme, and filtering for numerical stabilisation are given. Furthermore, the implementation of distributed memory parallelisation, the tensor product representation for prismatic grid cells, and optimisations for the HEVI formulation, are outlined. These developments lead to the so-called BRIDGE code, which will serve as a code base for a later DG extension of the well established ICON model. From the used idealised test cases, which are standard benchmarks for dynamical core development for the atmosphere, we conclude that the equation set using total energy E has better well-balancing properties than the set using θ. This result can be confirmed by a normal mode stability analysis. However, in some tests the set using E suffers more from non-linear instabilities that can only partially solved by filtering.

The implementation of a 'classical' Discontinuous Galerkin (DG) solver for atmospheric flows is presented that is designed for efficient use in numerical weather prediction, climate simulations, and meteorological research both on the whole sphere and for limited area modeling. To this purpose the horizontally explicit, vertically implicit (HEVI) approach is used together with implicit-explicit (IMEX)-Runge-Kutta (RK) time integration schemes and a moderate spatial approximation order (order 4 or 5). Two Euler equation sets using mass, momentum and either density weighted potential temperature θ or total energy E as prognostic variables are compared by several idealised test cases. Details of the formulation of the Euler equations in covariant form using the Ricci tensor calculus, the linearisations needed for HEVI (especially for the total energy set), boundary conditions for an IMEX-RK scheme, and filtering for numerical stabilisation are given. Furthermore, the implementation of distributed memory parallelisation, the tensor product representation for prismatic grid cells, and optimisations for the HEVI formulation, are outlined. These developments lead to the so-called BRIDGE code, which will serve as a code base for a later DG extension of the well established ICON model. From the used idealised test cases, which are standard benchmarks for dynamical core development for the atmosphere, we conclude that the equation set using total energy E has better well-balancing properties than the set using θ. This result can be confirmed by a normal mode stability analysis. However, in some tests the set using E suffers more from non-linear instabilities that can only partially solved by filtering.

The implementation of a 'classical' Discontinuous Galerkin (DG) solver for atmospheric flows is presented that is designed for efficient use in numerical weather prediction, climate simulations, and meteorological research both on the whole sphere and for limited area modeling. To this purpose the horizontally explicit, vertically implicit (HEVI) approach is used together with implicit-explicit (IMEX)-Runge-Kutta (RK) time integration schemes and a moderate spatial approximation order (order 4 or 5). Two Euler equation sets using mass, momentum and either density weighted potential temperature θ or total energy E as prognostic variables are compared by several idealised test cases. Details of the formulation of the Euler equations in covariant form using the Ricci tensor calculus, the linearisations needed for HEVI (especially for the total energy set), boundary conditions for an IMEX-RK scheme, and filtering for numerical stabilisation are given. Furthermore, the implementation of distributed memory parallelisation, the tensor product representation for prismatic grid cells, and optimisations for the HEVI formulation, are outlined. These developments lead to the so-called BRIDGE code, which will serve as a code base for a later DG extension of the well established ICON model. From the used idealised test cases, which are standard benchmarks for dynamical core development for the atmosphere, we conclude that the equation set using total energy E has better well-balancing properties than the set using θ. This result can be confirmed by a normal mode stability analysis. However, in some tests the set using E suffers more from non-linear instabilities that can only partially solved by filtering.

The implementation of a 'classical' Discontinuous Galerkin (DG) solver for atmospheric flows is presented that is designed for efficient use in numerical weather prediction, climate simulations, and meteorological research both on the whole sphere and for limited area modeling. To this purpose the horizontally explicit, vertically implicit (HEVI) approach is used together with implicit-explicit (IMEX)-Runge-Kutta (RK) time integration schemes and a moderate spatial approximation order (order 4 or 5). Two Euler equation sets using mass, momentum and either density weighted potential temperature θ or total energy E as prognostic variables are compared by several idealised test cases. Details of the formulation of the Euler equations in covariant form using the Ricci tensor calculus, the linearisations needed for HEVI (especially for the total energy set), boundary conditions for an IMEX-RK scheme, and filtering for numerical stabilisation are given. Furthermore, the implementation of distributed memory parallelisation, the tensor product representation for prismatic grid cells, and optimisations for the HEVI formulation, are outlined. These developments lead to the so-called BRIDGE code, which will serve as a code base for a later DG extension of the well established ICON model. From the used idealised test cases, which are standard benchmarks for dynamical core development for the atmosphere, we conclude that the equation set using total energy E has better well-balancing properties than the set using θ. This result can be confirmed by a normal mode stability analysis. However, in some tests the set using E suffers more from non-linear instabilities that can only partially solved by filtering.

The introduction of Maritime Autonomous Surface Ships will require new competencies for Remote Operators and onboard navigators. However, existing competency frameworks are too general to be used for developing curricula in Maritime Education and Training. This paper examines why current frameworks lack specificity and develops the Sensor Technology Impact on MASS Situational Awareness (STISA) methodology as a structured response. A systematic literature review was conducted, in which 25 articles were analysed using qualitative content analysis. The review identified research gaps concerning missing task and process foundations, unsuitable methods, insufficient consideration of sensor technologies, and weak integration of Modes of Operation. These gaps were translated into methodological requirements and used to develop the STISA methodology abductively. The methodology links sensor information, sensor limitations, operational challenges, situational awareness, and Modes of Operation as a basis for deriving future MASS operator work processes. The resulting STISA model remains conceptual and requires empirical validation in future studies.

Prediction methods and forecast of future developments and trends as well as related decisions or actions and reactions have been playing a vital role in human live and evolution. Human intelligence allowed for a dominating role in the development of our planet using our brains and more and more computers and merging artificial intelligence in the future. In the maritime domain, among others, the forecast of ships motion has been developed: from simple straight forward voyage planning in paper charts based only on rough measurement of estimated positions and ship motions, up to the latest developments using electronic navigation, ECDIS, communication technologies and high sophisticated Fast Time Simulation (FTS) methods. In the paper the already known technologies for supporting the ship handling process will be compared with potential new methods from single prediction up to multiple prediction and even step ahead prediction with unrivalled extension of the decision horizon. It reflects new requirements for preplanning of manoeuvres as part of berth-to-berth voyage planning specifically for arrival and departure segments - and increasing demands for safety and efficiency for the execution of manoeuvres in the conning process. Computer-based systems have been developed for “Simulation Augmented Manoeuvring Design, Monitoring & Conning” and are used to analyse and demonstrate the different prediction methods. Complex models of ship manoeuvring dynamics are implemented in order to forecast the immediate response on commanded control settings or even external effects as wind and shallow water for a suitable time period of the future motion. The paper provides insights into the potential benefits of various prediction methods based on FTS, discussed both for long term preplanning and short-term prediction for real-time support during the manoeuvring process. The benefits for increasing the effectiveness of lecturing and simulator training using these methods are obvious specifically for complex manoeuvring systems and will be demonstrated in this paper. It is a break-through in lecturing and training for immediate knowledge generation and checking ideas, it works perfectly for self-study individual learning and is excellent for the preparation of trainees for Full Mission simulator training, covering not only aspects of safe but also energy efficient and emission-minimized manoeuvres. Finally, potential future applications for wider navigation areas will be addressed.

For a number of years research has been carried out in several centres which has demonstrated the potential benefits of 100‐m scale models for a range of meteorological phenomena. More recently, some meteorological services have started to consider seriously the operational implementation of practical hectometric models. Many, but by no means all, of the applications are likely to relate to urban areas, where the enhanced resolution has obvious benefits. This article is concerned with the issues that need to be addressed to bridge the gap between research at 100‐m scales and practical models. We highlight a number of key issues that need to be addressed, with suggestions of important avenues for future development. An overarching issue is the high computational cost of these models. Although some ideas to reduce this are presented, it will always be a serious constraint. This means that the benefits of these models over lower resolution ones, or other techniques for generating high‐resolution forecasts, will need to be clearly understood, as will the trade‐offs with resolution. We discuss issues with model dynamical cores and physics–dynamics coupling. There are a number of challenges around model parameterisations, where some of the traditional problems (e.g., convection) become easier but a number of new challenges (e.g., around surface parameterisations) appear. Observational data at these scales present a challenge and novel types of observations will need to be considered. Data assimilation will be needed for short‐range forecasts, but there is currently little knowledge of this, although some of the likely issues are clear. An ensemble approach will be essential in many cases (e.g., convection), but research is needed into ensembles at these scales and significant work on post‐processing systems is required to make the best use of models at these grid lengths.