Parametrization of surface turbulent exchange in almost all Earth System Models rely on statistical representations valid only in highly restrictive conditions not often encountered in the atmosphere (flat and horizontally homogeneous terrain, stationarity, moderate stratification). Under strong stratification over flat and homogeneous terrain, and over complex terrain the parametrizations of...
Atmospheric processes cover a wide range of spatial and temporal scales, where turbulence occurs at the lowest range of this spectrum of motions. The scale determines whether a process may be directly solved in a weather and climate model, or needs to be represented by a simplified empirical formulation due to computational limits.
Existing formulations of near-surface turbulence were...
The atmospheric kinetic energy is affected by two distinct sources at widely separated horizontal length-scales: baroclinic instability at wavelengths around 3000 km (synoptic scales), and convection at wavelengths between hundreds and a few thousand meters (mesoscales). Nastrom and Gage (1985) analyzed observations of the atmospheric energy spectrum as a function of horizontal wavenumber...
A Lagrangian gravity-wave parameterization (MS-GWaM, Multi-Scale Gravity-Wave Model) that allows for fully transient wave-mean-flow interaction and horizontal propagation is applied to orographic gravity waves for the first time. Both linear and nonlinear mountain waves are modeled in idealized simulations within the pseudo-incompressible flow solver PincFlow. Two-dimensional flows over...
The large-scale transport of tracers such as ozone and water vapor is primarily governed by the Brewer-Dobson circulation. However, this transport is modified by small-scale gravity waves (GWs) and turbulence from GW breaking. Since these dynamics are not fully resolved in weather and climate models, parameterization is necessary. Tracers significantly influence the Earth's energy budget and...
The microphysical properties of cirrus clouds and their interactions with local dynamics, such as gravity waves (GW), are not well understood (Gasparini et al. [2018], Joos et al. [2014]), leading to significant uncertainties in climate effect estimations. Accurate representation of ice formation processes and ice number concentration prediction is crucial for understanding the cirrus...
To improve our understanding of the mechanisms that drive the intensification of tropical cyclones (TCs), researchers are pushing the boundaries of simulation resolution, leaning towards scales as fine as a few meters. Increasing the model resolution and numerical accuracy are the primary means to reduce both the model error from parameterization and the error from excessive diffusion....
Adaptive meshes are pivotal in numerical modeling and simulation, offering a means to efficiently, precisely, and flexibly represent intricate physical phenomena, particularly when grappling with their intricacies and varying scales. However, traditional adaptive mesh generation and optimisation algorithms tend to consume a large amount of computational cost, leading to a significant reduction...
Data-driven multivariate deterministic and stochastic subgrid modelling schemes for atmosphere and ocean models are discussed. A pattern-based approach is taken where pairs of patterns in the space of resolved variables (or functions of these) and in the space of the subgrid forcing are identified and linked in a predictive manner. On top of this deterministic part of the subgrid scheme the...
AI weather models have shown remarkable success in short- and medium-range forecasting. However, major questions remain about their ability to predict the most extreme events, particularly those that are so rare they were absent from the training set (so-called gray swans). There are also already documented challenges for these models in learning multi-scale dynamics. We will show in-depth...
A deep-learning model using convolutional neural nets is shown to produce physically realistic simulations of atmospheric and ocean circulations for the current climate state over 100-year autogressive rollouts. The model employs 10 prognostic variables above each cell on a 110-km resolution HEALPix mesh. The atmosphere and ocean are coupled asynchronously, with 6-hour and 2-day...
Techniques of machine learning (ML) and what is called “artificial intelligence” (AI) today find a rapidly increasing range of applications touching upon social, economic, and technological aspects of everyday life. They are also being used increasingly and with great enthusiasm to fill in gaps in our scientific knowledge by data-based modeling approaches. I have followed these developments...
Developing a hybrid model that integrates physics-based principles with advanced artificial intelligence (AI) techniques is a promising strategy for achieving accurate and efficient environmental prediction. This hybrid approach harnesses the strengths of both disciplines to enhance the precision and versatility of predictive modelling in the realm of environmental sciences. In this framework,...
Finite element methods have conventionally focused running on central processing units (CPUs). However, hardware is advancing rapidly, partly driven by machine learning applications. Representing numerical solvers with neural networks and implementing them with machine learning packages can bring advantages such as hardware agnosticism, automatic differentiation, and easy integration with...
The parameterization of gravity wave momentum transport remains an active area of research in atmospheric model development. Although small relative to the synoptic flow, un- and under-resolved gravity waves can systematically modify the propagation and breaking of Rossby waves, thereby playing a significant role in the planetary-scale circulation. Parameterizations seek to faithfully...
Ocean models constitute a fundamental component of any Earth system model. Our goal is to capture the effects of submesoscale eddies, which require a resolution below one kilometer. Global simulations over several decades for this resolution are not yet feasible even on state-of-the-art high-performance computers due to excessive runtimes.
In this presentation, we explore two strategies to...
Numerical simulations of large scale geophysical flows typically require unphysically strong dissipation for numerical stability, and the coarse resolution requires subgrid parameterisation. A popular scheme toward restoring energetic balance is horizontal kinetic energy backscatter. We consider a continuum formulation where momentum equations are augmented by a backscatter operator, e.g. in...
Discontinuous Galerkin (DG) / Flux Reconstruction (FR) methods are high order explicit finite element methods for solving advection dominated equations. They are very successful in obtaining reliable, small scale capturing methods that are arithmetically intense and thus suitable for modern memory bound HPC hardware. These methods have been used in various applications, including those...
The currently available computing power limits the resolution in chemistry climate models, even on upcoming exascale machines. This is, for example, due to the much larger number of prognostic variables, including chemical tracers. However, to further enhance the reliability and accuracy of climate projections, smaller scales have to be taken into account.
Adaptive methods offer a solution...
In the Model Uncertainty-MIP (MUMIP) we run single column models from different modelling centres over the same period and domain with a series of 6hr simulations. By constructing the SCM initial and boundary conditions such that they are derived from a common 3D-simulation, a common prescription of dynamics is enforced. Consequently, combined dataset of the array of SCM-simulations will mimic...
Clouds are one of the most important component of the Earth-Atmosphere system. They influence the hydrological cycle and the energy budget of the system via interaction with solar and infrared radiation. For clouds at lower levels consisting of water droplets, these effects are quite well understood, but for clouds containing ice particles (i.e. at lower temperatures) there are still open...
Modern computer systems are more and more diverse and composed of nodes with shared memory and different type of processors, mainly CPU’s and GPU’s. GPU vendors are coming usually with their own language specification like CUDA (Nvidia), ROCm (AMD), or Metal (Mac).
The Julia language is designed for scientific computation and comes with special packages to write individual kernels for...
The formation of ice clouds (cirrus clouds) in the tropopause region
requires moderate or even high vertical velocities up to several m/s
when homogeneous freezing is involved. Such vertical velocities result
from convective updrafts, turbulence or gravity waves. However, all
those processes are only purely represented in the tropopause region
of climate models. This in turn leads to...
Gravity waves are an important component of atmospheric dynamics, causing the transport of momentum and energy to the stratosphere and mesosphere. To make their parameterizations in atmospheric models more accurate, we need to improve our understanding of gravity waves. We address this problem by studying data from a global ICON simulation with a horizontal resolution of approximately 2.5 km....
pyBELLA+ is an innovative atmospheric flow solver and data assimilation engine designed to play a unique role in the landscape of research numerical weather prediction (NWP) models. This project focuses on developing a compact, modular software package that adheres to modern scientific software development principles, enabling researchers to focus on addressing NWP modelling questions with...
The representation of subgrid-scale orography is a challenge in the physical parameterisation of orographic gravity-wave sources in weather forecasting. A significant hurdle is encoding maximum physical information with a simple spectral representation on unstructured geodesic grids with non-quadrilateral cells, such as those used in the German Weather Service's Icosahedral Nonhydrostatic...
Recently, there has been a huge effort focused on developing highly efficient open-source libraries designed for Artificial Intelligence (AI) related computations on different computer architectures (for example, CPUs, GPUs and new AI processors). These advancements have not only made the algorithms based on these libraries highly efficient and portable between different architectures, but...
Jexpresso is a new multi-physics general solver designed to solve, by numerical means, arbitrary systems of PDEs while making it easy for a user to set up one's own specific physical problem. While the version V2.0 presented in this poster relies on spectral elements and finite differences, the code is structured so that a user can add other grid-based numerical methods of choice without...
MU-MIP is an international project which seeks to characterise the systematic and random
component of model error across many different atmospheric models. An initiative of the WCRP
Working Group for Numerical Experimentation and the WWRP Predictability, Dynamics and
Ensemble Forecasting Working Group, MU-MIP includes representatives of 12+ institutes spanning
three continents.
MU-MIP...
A comprehensive investigation of the predictability properties in a three-level quasi-geostrophic atmospheric model with realistic mean state and variability is performed. The full spectrum of covariant Lyapunov vectors and associated finite-time Lyapunov exponents (FTLEs) is calculated. The statistical properties of the fluctuations of the FTLEs as well as the spatial localisation and...
Currently, a new dynamical core for the weather and climate forecast model ICON, based on the Discontinuous Galerkin (DG) method, is under development at the Deutscher Wetterdienst (DWD). The DG method combines conservation of the prognostic variable via the finite volume approach with higher order accuracy via the finite element approach. Additionally it allows the use of explicit time...
This presentation will present some modern developments of discontinuous Galerkin (DG) methods such as structure-preserving schemes equipped with properties like entropy stability, kinetic energy as well as pressure equilibrium preservation and their impact on the robustness and efficiency of numerical simulations.
Fluidity-Atmos, representative of a three-dimensional (3D) non-hydrostatic Galerkin compressible atmospheric model, is generated to resolve large-scale and small-scale atmospheric phenomena simultaneously. This achievement is facilitated by the use of non-hydrostatic equations and the adoption of a flexible 3D dynamically adaptive mesh where the mesh is denser in areas with higher gradients of...
At the preceding MoW conference, an experiment reported in Mesinger and Veljovic (JMSJ 2020) was presented showing an advantage of the Eta ensemble over its driver ECMWF members in placing 250 hPa jet stream winds east of the Rockies. However, that Eta ensemble switched to use sigma, also achieved 250 hPa wind speed scores better than its driver members, although to a lesser extent. Thus,...
While with terrain following coordinates the orography follows a line of coordinates, with the cut cell discretisation the points on the lower boundary are positioned in an irregular way. This means that the fields near such mountains is expected also to be irregular. However, idealised test situations use a smooth and often well resolved boundary. When the mountain is also well resolved, we...
TIGAR is a general circulation model aimed at studying Rossby and gravity wave dynamics, which is based on hydrostatic primitive equations. It is a spectral model that employs Hough harmonics, which are eigensolutions of the linearized rotating shallow water equations on the sphere as the basis function set for the horizontal representation of dynamical variables. This leads to the description...
It has long been known that the excitation of fast motion in certain two-scale dynamical systems is linked to the singularity structure in complex time of the slow variables. We demonstrate, in the context of a fast harmonic oscillator forced by one component of the Lorenz 1963 model, that this principle can be used to construct time-discrete surrogate models by numerically extracting...
Physical imbalances introduced by local sequential Bayesian data assimilation pose a significant challenge for numerical weather prediction. Fast-mode acoustic imbalances, for instance, can severely degrade solution quality. We present a novel dynamics-driven method to dynamically suppress these imbalances. Our approach employs a blended numerical model that seamlessly integrates compressible,...
We present a phase-averaging framework for the rotating shallow-water equations and a time-integration methodology for it. Phase averaging consists of averaging the nonlinearity over phase shifts in the exponential of the linear wave operator. Phase averaging aims to capture the slow dynamics in a solution that is smoother in time (in transformed variables), so that larger timesteps may be...
In the atmosphere, fast oscillations such as gravity waves coexist with slow features such as geostrophic vortices. Numerical modelling of the fast and slow dynamics requires a small time step and long simulation time, which is computationally costly. Phase averaging filters out the fast oscillations whilst capturing their effect on the slow features, allowing for larger time steps.
We...
Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We...
Multistability is a frequent feature in the climate system and leads to key challenges for our ability to predict how the system will respond to transient perturbations of the dynamics. As a particular example, the stably stratified atmospheric boundary layer is known to exhibit distinct flow regimes that are believed to be metastable. Numerical weather prediction and climate models encounter...
We present a spatial Bayesian hierarchical model for postprocessing surface maximum wind gusts in COSMO-REA6. Our approach uses a non-stationary extreme value distribution (GEV) at the top level, with parameters that vary based on linear regressions of predictor variables from the COSMO-REA6 reanalysis. To capture spatial patterns in surface extreme wind gust behavior, the regression...
We introduce numerical techniques and physical implementation for the development of a global large-eddy simulation model by the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). NICAM has been studied for global kilometer-scale simulations since the first global 3.5-km mesh aqua-planet simulation by Tomita et al. (2005). Miyamoto et al. (2013) conducted a global 870-m mesh simulation...
Despite the steady progress in resolution and skill of weather and Earth-system models during the last decades, their physical fidelity and computational efficiency needs to be and can be significantly improved. Existing model infrastructures and software appear suboptimal to take advantage of computing technology advances and the potential from machine learning emulation alongside numerical...
Final discussion of results and on future
