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The north and south of the Alps, as well as the eastern shores of the Adriatic Sea, are hot spots of severe convective storms, including hail and lightning associated with deep convection. With advancements in computing power, it has become feasible to simulate deep convection explicitly in climate models by decreasing the horizontal grid spacing to less than 4 km. These kilometer-scale models improve the representation of orography and reduce uncertainties associated with the use of deep convection parameterizations. In this study, we perform km-scale simulations for eight observed cases of severe convective storms (seven with and one without observed hail) over the Alpine-Adriatic region. The simulations are performed with the climate version of the regional model Consortium for Small-scale Modeling (COSMO) that runs on graphics processing units (GPUs) at a horizontal grid spacing of 2.2 km. To analyze hail and lightning we have explored the hail growth model (HAILCAST) and lightning potential index (LPI) diagnostics integrated with the COSMO-crCLIM model. Comparison with available high-resolution observations reveals good performance of the model in simulating total precipitation, hail, and lightning. By performing a detailed analysis of three of the case studies, we identified the importance of significant meteorological factors for heavy thunderstorms that were reproduced by the model. Among these are the moist unstable boundary layer and dry mid-level air, the topographic barrier, as well as an approaching upper-level trough and cold front. Although COSMO HAILCAST tends to underestimate the hail size on the ground, the results indicate that both HAILCAST and LPI are promising candidates for future climate research. [ABSTRACT FROM AUTHOR]

Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's law driving forward rapid specialization of hardware architectures, building simulation codes on a low-level language with hardware-specific optimizations is a significant risk. As a solution, we present Pace, an implementation of the nonhydrostatic FV3 dynamical core and GFDL cloud microphysics scheme which is entirely Python-based. In order to achieve high performance on a diverse set of hardware architectures, Pace is written using the GT4Py domain-specific language. We demonstrate that with this approach we can achieve portability and performance, while significantly improving the readability and maintainability of the code as compared to the Fortran reference implementation. We show that Pace can run at scale on leadership-class supercomputers and achieve performance speeds 3.5–4 times faster than the Fortran code on GPU-accelerated supercomputers. Furthermore, we demonstrate how a Python-based simulation code facilitates existing or enables entirely new use cases and workflows. Pace demonstrates how a high-level language can insulate us from disruptive changes, provide a more productive development environment, and facilitate the integration with new technologies such as machine learning. [ABSTRACT FROM AUTHOR]

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At its core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change. [ABSTRACT FROM AUTHOR]

Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's Law driving forward rapid specialization of hardware architectures, building simulation codes on a low-level language with hardware specific optimizations is a significant risk. As a solution, we present Pace, an implementation of the nonhydrostatic FV3 dynamical core which is entirely Python-based. In order to achieve high performance on a diverse set of hardware architectures, Pace is written using the GT4Py domain-specific language. We demonstrate that with this approach we can achieve portability and performance, while significantly improving the readability and maintainability of the code as compared to the Fortran reference implementation. We show that Pace can run at scale on leadership-class supercomputers and achieve performance speeds 3.5–4 times faster than the Fortran code on GPU-accelerated supercomputers. Furthermore, we demonstrate how a Python-based simulation code facilitates existing or enables entirely new use-cases and workflows. Pace demonstrates how a high-level language can insulate us from disruptive changes, provide a more productive development environment, and facilitate the integration with new technologies such as machine learning. [ABSTRACT FROM AUTHOR]

Shallow orographic convection embedded in an unstable cap cloud can organize into convective bands. Previous research has highlighted the important role of small-amplitude topographic variations in triggering and organizing banded convection. Here, the underlying dynamical mechanisms are systematically investigated by conducting three-dimensional simulations of moist flows past a two-dimensional mountain ridge using a cloud-resolving numerical model. Most simulations address a sheared environment to account for the observed wind profiles. Results confirm that small-amplitude topographic variations can enhance the development of embedded convection and anchor quasi-stationary convective bands to a fixed location in space. The resulting precipitation patterns exhibit tremendous spatial variability, since regions receiving heavy rainfall can be only kilometers away from regions receiving little or no rain. In addition, the presence of banded convection has important repercussions on the area-mean precipitation amounts. For the experimental setup here, the gravity wave response to small-amplitude topographic variations close to the upstream edge of the cap cloud (which is forced by the larger-scale topography) is found to be the dominant triggering mechanism. Small-scale variations in the underlying topography are found to force the location and spacing of convective bands over a wide range of scales. Further, a self-sufficient mode of unsteady banded convection is investigated that does not dependent on external perturbations and is able to propagate against the mean flow. Finally, the sensitivity of model simulations of banded convection with respect to horizontal computational resolution is investigated. Consistent with predictions from a linear stability analysis, convective bands of increasingly smaller scales arc favored as the horizontal resolution is increased. However, small-amplitude topographic roughness is found to trigger banded convection and to control the spacing and location of the resulting bands. Thereby, the robustness of numerical simulations with respect to an increase in horizontal resolution is increased in the presence of topographic variations.

Marginally unstable air masses impinging upon a mountain ridge may lead to the development of a nominally stratiform orographic cloud with shallow embedded convection. Rainfall amounts and distribution are then strongly influenced by the convective dynamics. In this study, the transition from purely stratiform orographic precipitation to flow regimes with embedded convection is systematically investigated. To this end, idealized cloud-resolving numerical simulations of moist flow past a two-dimensional mountain ridge are performed in a three-dimensional domain. A series of simulations with increasing upstream potential instability shows that the convective dynamics may significantly increase precipitation amounts, intensity, and efficiency, to an extent that cannot be replicated by two-dimensional simulations. Under conditions of uniform upstream flow, the embedded convection is of the cellular type. It is demonstrated that simple stability measures of the upstream profile are poor predictors for the occurrence and depth of embedded convection. A linear stability analysis is performed to understand the linear growth of the developing convective instabilities. Embedded convection results if the growth rates of convective instabilities are compatible with the advective time scale (the time an air parcel spends inside the orographic cloud) and the microphysical time scale (time for rain production and fallout). Individual convective updrafts are anchored to the mean flow. Additional simulations serve to demonstrate that the development of embedded convection and associated precipitation may strongly depend on small-amplitude upstream perturbations. Such perturbations enhance the efficacy of the convective circulations and lead to overall stronger precipitation. The potential implications of this result for the predictability of quantitative precipitation are also discussed.

The correct simulation of fog and low stratus (FLS) is a difficult task for numerical weather prediction (NWP) models. The Swiss Plateau experiences many days with FLS in winter. Most NWP models employ terrain‐following vertical coordinates. As a consequence, the typically flat cloud top is intersected by sloping coordinate surfaces above hilly terrain such as the Swiss Plateau. Horizontal advection across the sloping coordinate surfaces leads to spurious numerical diffusion which promotes erroneous FLS dissipation. To address this problem, we propose a new vertical coordinate formulation which features a local smoothing of the model levels. We demonstrate the positive impact of the new vertical coordinate formulation on a case study in detail and for a full month using the COSMO model. The improved vertical coordinate formulation is not yet sufficient to obtain perfect FLS forecasts, it is however a crucial aspect to consider on the way thereto. Plain Language Summary: In Switzerland, the Swiss Plateau is prone to occurrence of fog and low stratus clouds (FLS) in winter. High‐resolution weather prediction models are an important tool to predict FLS. However, they often struggle to provide accurate FLS forecasts. Among other model aspects which need to be improved, one issue is the determination of the computational mesh: Most weather models employ a mesh which follows the terrain at the Earth's surface, leading to grid cells that are tilted with respect to the horizontal at altitudes where FLS occur. The structure as well as the top of a FLS layer typically is horizontal. Thus, the cells of the computational mesh are at an angle with respect to the dominant physical processes that the model has to represent. We show that a sloping mesh is detrimental for the forecasting of FLS since it promotes premature dissipation of the clouds. For a widely used weather model, we propose a new way to determine the computational mesh leading to a smoother and flatter computational mesh over the Swiss Plateau. The model is still not able to yield perfect FLS forecasts, but in some cases the use of the new mesh leads to considerable forecast improvements. Key Points: Terrain‐following vertical coordinates feature sloping vertical coordinate surfaces in the atmospheric boundary layerSpurious numerical diffusion associated with advection across sloping coordinate surfaces promotes dissipation of fog and low stratusLocal smoothing of the vertical coordinate surfaces improves forecasts of fog and low stratus [ABSTRACT FROM AUTHOR]

Simulation software in geophysics is traditionally written in Fortran or C++ due to the stringent performance requirements these codes have to satisfy. As a result, researchers who use high-productivity languages for exploratory work often find these codes hard to understand, hard to modify, and hard to integrate with their analysis tools. fv3gfs-wrapper is an open-source Python-wrapped version of the NOAA (National Oceanic and Atmospheric Administration) FV3GFS (Finite-Volume Cubed-Sphere Global Forecast System) global atmospheric model, which is coded in Fortran. The wrapper provides simple interfaces to progress the Fortran main loop and get or set variables used by the Fortran model. These interfaces enable a wide range of use cases such as modifying the behavior of the model, introducing online analysis code, or saving model variables and reading forcings directly to and from cloud storage. Model performance is identical to the fully compiled Fortran model, unless routines to copy the state in and out of the model are used. This copy overhead is well within an acceptable range of performance and could be avoided with modifications to the Fortran source code. The wrapping approach is outlined and can be applied similarly in other Fortran models to enable more productive scientific workflows. [ABSTRACT FROM AUTHOR]

Many numerical weather prediction models employ terrain‐following vertical coordinates. As a consequence, over orography, flat tops of stratus clouds are intersected by the vertical coordinate surfaces. We conduct idealised two‐dimensional simulations of a stratus cloud with the COSMO model to study the effect of such sloping vertical coordinate surfaces. The evolution of the stratus cloud above a flat surface within a horizontally homogeneous atmosphere serves as a reference. During night‐time, the cloud thickens, driven by radiative cooling at the cloud top. Adding a sinusoidal perturbation to the vertical coordinate surfaces reduces the growth of the stratus cloud. With strong perturbations, the cloud starts to dissipate. The physical processes in the two simulations are identical, hence this behaviour is purely driven by numerical diffusion. The cloud is similarly thinned when sinusoidal orographic features are introduced. The reduction depends on the amplitude and wavelength of the perturbations and hills. Increasing the horizontal resolution partly mitigates the numerical diffusion. However, this is a very costly measure for an operational weather model. We suggest conducting further research on a new vertical coordinate with additional local smoothing of the orographic signal. [ABSTRACT FROM AUTHOR]

Forecasting fog and low stratus (FLS) accurately poses a challenge to current numerical weather prediction models, despite many advancements in recent years. We present a novel method to quantify FLS extent bias by comparing forecasts with satellite observations. Evaluating a four‐month period, we show that COSMO‐1, the MeteoSwiss high‐resolution operational model, exhibits a considerable negative FLS bias during wintertime. To study the cause, we conduct a series of sensitivity experiments for a representative case study, where COSMO‐1 dissipated extensive FLS erroneously. Replacing the one‐moment bulk microphysics parameterisation scheme by a two‐moment scheme, as well as increasing the number of vertical levels, did not show any improvements. The FLS dissipation was delayed (but not prevented) by decreasing the lower bound imposed on the turbulent diffusion coefficients from 0.4 to 0.01 m2·s−1, or by reducing horizontal grid spacing from 1.1 km to 550 m. Additionally, simulations at 1.1‐km grid spacing with smoothed orography led to more extensive FLS than the same simulations without smoothed orography. An analysis of the cloud water budget revealed that the model's advection scheme is causing a loss of liquid water content near the cloud top. A simulation with an alternative terrain‐following coordinate system, in which the vertical coordinates are quasihorizontal near the cloud top, reduced the loss of cloud water through advection and improved the evolution of FLS in the case study. In combination, our findings suggest that the advection scheme exhibits numerical diffusion, which promotes spurious mixing in the vertical of cloudy and adjacent cloud‐free grid cells in terrain‐following vertical coordinates; this process can become the root cause for too rapid dissipation of FLS during nighttime in complex terrain. [ABSTRACT FROM AUTHOR]

The correct simulation of the atmospheric boundary layer (ABL) in highly complex terrain is a challenge for mesoscale numerical weather prediction models. An improvement in model performance is possible if horizontal contributions to turbulence kinetic energy (TKE) production, such as horizontal shear production, are implemented in the model's turbulence parameterization. However, 3D turbulence parameterizations often only have a constant horizontal length scale that depends on the horizontal grid spacing. This is unphysical for mesoscale applications, because such parameterizations were initially developed for much smaller model grid spacings (e.g., for large-eddy simulations). In this study, we develop a new physically based horizontal length scale for the high-resolution mesoscale model COSMO. We analyze days dominated by thermally driven circulations (valley wind days) in the Inn Valley, Austria. Results show that the new horizontal length scale improves TKE simulations in the valley, when horizontal shear processes contribute to the overall TKE budget. Vertical profiles of TKE and transects across the valley indicate that the model simulates the ABL in a more realistic way than standard turbulence schemes, because the new scheme is able to account for terrain inhomogeneities. A model validation with 88 stations in Austria for four case study days indicates no change in the mean surface fields of temperature, relative humidity, and wind speed by the new turbulence parameterization. [ABSTRACT FROM AUTHOR]

Inverse modeling of anthropogenic and biospheric CO2 fluxes from ground-based and satellite observations critically depends on the accuracy of atmospheric transport simulations. Previous studies emphasized the impact of errors in simulated winds and vertical mixing in the planetary boundary layer, whereas the potential importance of releasing emissions not only at the surface but distributing them in the vertical was largely neglected. Accounting for elevated emissions may be critical, since more than 50 % of CO2 in Europe is emitted by large point sources such as power plants and industrial facilities. In this study, we conduct high-resolution atmospheric simulations of CO2 with the mesoscale Consortium for Small-scale Modeling model extended with a module for the simulation of greenhouse gases (COSMO-GHG) over a domain covering the city of Berlin and several coal-fired power plants in eastern Germany, Poland and Czech Republic. By including separate tracers for anthropogenic CO2 emitted only at the surface or according to realistic, source-dependent profiles, we find that releasing CO2 only at the surface overestimates near-surface CO2 concentrations in the afternoon on average by 14 % in summer and 43 % in winter over the selected model domain. Differences in column-averaged dry air mole XCO2 fractions are smaller, between 5 % in winter and 8 % in summer, suggesting smaller yet non-negligible sensitivities for inversion modeling studies assimilating satellite rather than surface observations. The results suggest that the traditional approach of emitting CO2 only at the surface is problematic and that a proper allocation of emissions in the vertical deserves as much attention as an accurate simulation of atmospheric transport. [ABSTRACT FROM AUTHOR]

We present a roadmap towards exascale computing based on true application performance goals. It is based on two state-of-the art European numerical weather prediction models (IFS from ECMWF and COSMO from MeteoSwiss) and their current performance when run at very high spatial resolution on present-day supercomputers. We conclude that these models execute about 100–250 times too slow for operational throughput rates at a horizontal resolution of 1 km, even when executed on a full petascale system with nearly 5000 state-of-the-art hybrid GPU-CPU nodes. Our analysis of the performance in terms of a metric that assesses the efficiency of memory use shows a path to improve the performance of hardware and software in order to meet operational requirements early next decade. [ABSTRACT FROM AUTHOR]

Inverse modeling of anthropogenic and biospheric CO2 fluxes from ground-based and satellite observations critically depends on the accuracy of atmospheric transport simulations. Previous studies emphasized the impact of errors in simulated winds and vertical mixing in the planetary boundary layer, whereas the potential importance of releasing emissions not only at the surface but distributing them in the vertical was largely neglected. Accounting for elevated emissions may be critical, since more than 50% of CO2 in Europe is emitted by large point sources such as power plants and industrial facilities. In this study we conduct high-resolution atmospheric simulations of CO2 with the mesoscale model COSMO-GHG over a domain covering the city of Berlin and several coal-fired power plants in eastern Germany, Poland and the Czech Republic. By including separate tracers for anthropogenic CO2 emitted only at the surface or according to realistic, source-dependent profiles, we find that releasing CO2 only at the surface overestimates near-surface CO2 concentrations in the afternoon on average by 14% in summer and 43% in winter over the selected model domain. Differences in column mean dry air mole fractions XCO2 are smaller, between 5% in winter and 8% in summer, suggesting smaller yet non-negligible sensitivities for inversion modeling studies assimilating satellite rather than surface observations. The results suggests that the traditional approach of emitting CO2 only at the surface is problematic and that a proper allocation of emissions in the vertical deserves as much attention as an accurate simulation of atmospheric transport. [ABSTRACT FROM AUTHOR]

Climate models project an increase in heavy precipitation events in response to greenhouse gas forcing. Important elements of such events are rain showers and thunderstorms, which are poorly represented in models with parameterized convection. In this study, simulations with 12 km horizontal grid spacing (convection-parameterizing model, CPM) and 2 km grid spacing (convection-resolving model, CRM) are employed to investigate the change in the diurnal cycle of convection with warmer climate. For this purpose, simulations of 11 days in June 2007 with a pronounced diurnal cycle of convection are compared with surrogate simulations from the same period. The surrogate climate simulations mimic a future climate with increased temperatures but unchanged relative humidity and similar synoptic-scale circulation. Two temperature scenarios are compared: one with homogeneous warming (HW) using a vertically uniform warming and the other with vertically dependent warming (VW) that enables changes in lapse rate. The two sets of simulations with parameterized and explicit convection exhibit substantial differences, some of which are well known from the literature. These include differences in the timing and amplitude of the diurnal cycle of convection, and the frequency of precipitation with low intensities. The response to climate change is much less studied. We can show that stratification changes have a strong influence on the changes in convection. Precipitation is strongly increasing for HW but decreasing for the VW simulations. For cloud type frequencies, virtually no changes are found for HW, but a substantial reduction in high clouds is found for VW. Further, we can show that the climate change signal strongly depends upon the horizontal resolution. In particular, significant differences between CPM and CRM are found in terms of the radiative feedbacks, with CRM exhibiting a stronger negative feedback in the top-ofthe- atmosphere energy budget. [ABSTRACT FROM AUTHOR]

The best hope for reducing long-standing global climate model biases is by increasing resolution to the kilometer scale. Here we present results from an ultrahighresolution non-hydrostatic climate model for a near-global setup running on the full Piz Daint supercomputer on 4888 GPUs (graphics processing units). The dynamical core of the model has been completely rewritten using a domainspecific language (DSL) for performance portability across different hardware architectures. Physical parameterizations and diagnostics have been ported using compiler directives. To our knowledge this represents the first complete atmospheric model being run entirely on accelerators on this scale. At a grid spacing of 930m (1.9 km), we achieve a simulation throughput of 0.043 (0.23) simulated years per day and an energy consumption of 596MWh per simulated year. Furthermore, we propose a new memory usage efficiency (MUE) metric that considers how efficiently the memory bandwidth - the dominant bottleneck of climate codes - is being used. [ABSTRACT FROM AUTHOR]

The best hope for reducing long-standing global climate model biases, is through increasing the resolution to the kilometer scale. Here we present results from an ultra-high resolution non-hydrostatic climate model for a near-global setup running on the full Piz Daint supercomputer on 4888 GPUs. The dynamical core of the model has been completely rewritten using a domain-specific language (DSL) for performance portability across different hardware architectures. Physical parameterizations and diagnostics have been ported using compiler directives. To our knowledge this represents the first complete atmospheric model being run entirely on accelerators at this scale. At a grid spacing of 930 m (1.9 km), we achieve a simulation throughput of 0.043 (0.23) simulated years per day and an energy consumption of 596 MWh per simulated year. Furthermore, we propose the new memory usage efficiency metric that considers how efficiently the memory bandwidth - the dominant bottleneck of climate codes - is being used. [ABSTRACT FROM AUTHOR]

Climate models project an increase in heavy precipitation events in response to greenhouse gas forcing. Important elements of such events are rain showers and thunderstorms, which are poorly represented in models with parameterized convection. In this study, simulations with 12 km horizontal grid spacing (convection-parameterizing model, CPM) and 2 km grid spacing (convection-resolving model, CRM), and with either a one-moment microphysics scheme (1M) or a two-moment microphysics scheme (2M) are employed to investigate the change in the diurnal cycle of convection with warmer climate. For this purpose, simulations of 11 days in June 2007 with a pronounced diurnal cycle of convection are compared with surrogate simulations from the same period. The surrogate climate simulations mimic a future climate with increased temperatures, but unchanged relative humidity and synoptic-scale circulation. Two temperature scenarios are compared, one with homogeneous warming (HW) using a vertically uniform warming, the other with vertically-dependent warming (VW) that enables changes in lapse rate. The two sets of simulations with parameterized and explicit convection exhibit substantial differences, which are well known from the literature. These include differences in the timing and amplitude of the diurnal cycle of convection, and the frequency of precipitation with low intensities. There are also significant differences in terms of the response to the surrogate warming. For CRM, an increase of hourly heavy precipitation events is found for both surrogate scenarios and microphysics schemes. The intensification is consistent with the Clausius-Clapeyron relation. For cloud type frequencies, virtually no changes are found for HW, but a substantial reduction in high clouds is found for VW. Some of the CPM sensitivities differ significantly. Importantly, the increase of heavy precipitation events simulated by CPM is larger than suggested by the Clausius-Clapeyron relation. Moreover, significant differences between CPM and CRM are found in terms of the radiative feedbacks, with CRM exhibiting a stronger negative feedback in the top of the atmosphere energy budget. [ABSTRACT FROM AUTHOR]

The representation of moist convection in climate models represents a major challenge, due to the small scales involved. Using horizontal grid spacings of O(1km), convection-resolving weather and climate models allows one to explicitly resolve deep convection. However, due to their extremely demanding computational requirements, they have so far been limited to short simulations and/or small computational domains. Innovations in supercomputing have led to new hybrid node designs, mixing conventional multi-core hardware and accelerators such as graphics processing units (GPUs). One of the first atmospheric models that has been fully ported to these architectures is the COSMO (Consortium for Small-scale Modeling) model. Here we present the convection-resolving COSMO model on continental scales using a version of the model capable of using GPU accelerators. The verification of a week-long simulation containing winter storm Kyrill shows that, for this case, convection-parameterizing simulations and convectionresolving simulations agree well. Furthermore, we demonstrate the applicability of the approach to longer simulations by conducting a 3-month-long simulation of the summer season 2006. Its results corroborate the findings found on smaller domains such as more credible representation of the diurnal cycle of precipitation in convection-resolving models and a tendency to produce more intensive hourly precipitation events. Both simulations also show how the approach allows for the representation of interactions between synopticscale and meso-scale atmospheric circulations at scales ranging from 1000 to 10 km. This includes the formation of sharp cold frontal structures, convection embedded in fronts and small eddies, or the formation and organization of propagating cold pools. Finally, we assess the performance gain from using heterogeneous hardware equipped with GPUs relative to multi-core hardware. With the COSMO model, we now use a weather and climate model that has all the necessary modules required for real-case convection-resolving regional climate simulations on GPUs. [ABSTRACT FROM AUTHOR]

Many regional forecasting models struggle to simulate low-lying strong temperature inversions. To understand this apparent deficit for forecast improvements, a case study of a strong inversion occurring in the Bay of Biscay on 27 January 2003 is conducted. The event was characterized by extensive stratocumulus cloud cover beneath an extensive high pressure system in combination with a particularly strong inversion of 10-12 K at an altitude of 500-800 m. Simulations were performed at 2- and 12-km horizontal resolutions, with 60 vertical levels (13 levels within the first 1000 m), and with lead times of 12-72 h. The simulations were validated using in situ radiosonde and satellite data. Besides large-scale subsidence, turbulent vertical mixing is a key dynamical process for the formation of nocturnal inversions. Sensitivities to parameters for vertical mixing (the minimum threshold for eddy diffusivity and the turbulence length scale) are investigated. Results presented herein show the planetary boundary layer (PBL) profiles to be very sensitive to the minimum threshold applied for eddy diffusivity, whereas little sensitivity with respect to the turbulence length-scale parameter was found. PBL moisture and potential temperature θ profiles for hindcasts between 24- and 72-h lead times at both resolutions were adequately simulated. In simulations with an adequate representation of the vertical turbulent exchange, realistic cloud cover was simulated, while too high values of the aforementioned threshold produced a strong underestimation of the cloud cover. These results indicate that a realistic simulation of strong inversions and their associated cloud cover is feasible, provided the vertical turbulent exchange is adequately represented. [ABSTRACT FROM AUTHOR]

For many scientific applications, Graphics Processing Units (GPUs) can be an interesting alternative to conventional CPUs as they can deliver higher memory bandwidth and computing power. While it is conceivable to re-write the most execution time intensive parts using a low-level API for accelerator programming, it may not be feasible to do it for the entire application. But, having only selected parts of the application running on the GPU requires repetitively transferring data between the GPU and the host CPU, which may lead to a serious performance penalty. In this paper we assess the potential of compiler directives, based on the OpenACC standard, for porting large parts of code and thus achieving a full GPU implementation. As an illustrative and relevant example, we consider the climate and numerical weather prediction code COSMO (Consortium for Small Scale Modeling) and focus on the physical parametrizations, a part of the code which describes all physical processes not accounted for by the fundamental equations of atmospheric motion. We show, by porting three of the dominant parametrization schemes, the radiation, microphysics and turbulence parametrizations, that compiler directives are an efficient tool both in terms of final execution time as well as implementation effort. Compiler directives enable to port large sections of the existing code with minor modifications while still allowing for further optimizations for the most performance critical parts. With the example of the radiation parametrization, which contains the solution of a block tri-diagonal linear system, the required code modifications and key optimizations are discussed in detail. Performance tests for the three physical parametrizations show a speedup of between 3× and 7× for execution time obtained on a GPU and on a multi-core CPU of an equivalent generation. [ABSTRACT FROM AUTHOR]

Most atmospheric models use terrain-following coordinates, and it is well known that the associated deformation of the computational mesh leads to numerical inaccuracies. In a previous study, the authors proposed a new terrain-following coordinate formulation [the smooth level vertical (SLEVE) coordinate], which yields smooth vertical coordinate levels at mid and upper levels and thereby considerably reduces numerical errors in the simulation of flow past complex topography. In the current paper, a generalization of the SLEVE coordinate is presented by using a modified vertical decay of the topographic signature with height. The new formulation enables an almost uniform thickness of the lowermost computational layers, while preserving the fast transition to smooth levels in the mid and upper atmosphere. This allows for a more consistent and more stable coupling with planetary boundary layer schemes, while retaining the advantages over classic sigma coordinates at upper levels. The generalized SLEVE coordinate is implemented and successfully tested in real-case simulations using an operational nonhydrostatic atmospheric model. [ABSTRACT FROM AUTHOR]

In numerically integrating the equations of motion in terrain-following coordinates, care must be taken in treating the metric terms that arise due to the sloping coordinate surfaces. In particular, metric terms that appear in the advection and pressure-gradient operators should be represented in a manner such that they exactly cancel when transformed back to Cartesian coordinates. Noncancellation of these terms can lead to spurious forcing at small scales on the numerical grid. This effect is demonstrated for a mountain wave flow problem through analytic solutions to the linear finite-difference equations. Further confirmation is provided through numerical simulations with a two-dimensional prototype version of the Weather Research and Forecasting (WRF) model, and with the Canadian Mesoscale Compressible Community (MC2) model. [ABSTRACT FROM AUTHOR]

Most numerical weather prediction models rely on a terrain-following coordinate framework. The computational mesh is thus characterized by inhomogeneities with scales determined by the underlying topography. Such inhomogeneities may affect the truncation error of numerical schemes. In this study, a new class of terrainfollowing coordinate systems for use in atmospheric prediction models is proposed. Unlike conventional systems, the new smooth level vertical (SLEVE) coordinate yields smooth coordinates at mid- and upper levels. The basic concept of the new coordinate is to employ a scale-dependent vertical decay of underlying terrain features. The decay rate is selected such that small-scale topographic variations decay much faster with height than their large-scale counterparts. This generalization implies a nonlocal coordinate transformation. The new coordinate is tested and compared against standard sigma and hybrid coordinate systems using an idealized advection test. It is demonstrated that the presence of coordinate transformations induces substantial truncation errors. These are critical for grid inhomogeneities with wavelengths smaller than approximately eight grid increments, and may overpower the regular-grid truncation error of the underlying finite-difference approximation. These results are confirmed by a theoretical analysis of the truncation error. In addition, the new coordinate is tested in idealized and real-case numerical experiments using a nonhydrostatic model. The simulations using the new coordinate yield a substantial reduction of small-scale noise in dynamical and thermodynamical model fields. [ABSTRACT FROM AUTHOR]

We evaluate the near-surface representation of thermally driven winds in the Swiss Alps in a numerical weather prediction model at km-scale resolution. In addition, the influence of grid resolution (2.2 km and 1.1 km), topography filtering, and land surface datasets on the accuracy of the simulated valley winds is investigated. The simulations are evaluated against a comprehensive set of surface observations for an 18-day fair-weather summer period in July 2006. The episode is characterized by strong diurnal wind systems and the formation of shallow convection over the mountains, which transitions to precipitating convection in some areas. The near-surface winds (10 m above ground level) follow a typical diurnal pattern with strong daytime up-valley flow and weaker nighttime down-valley flow. At a 2.2 km resolution the valley winds are poorly simulated for most stations, while at a 1.1 km resolution the diurnal cycle of the valley winds is well represented in most large (e.g., Rhein valley at Chur and Rhone valley at Visp) and medium-sized valleys (e.g., Linth valley at Glarus). In the smaller valleys (e.g., Maggia valley at Cevio), the amplitude of the valley wind is still significantly underestimated, even at a 1.1 km resolution. Detailed sensitivity experiments show that the use of high-resolution land surface datasets, for both the soil characteristics as well as for the land cover, and reduced filtering of the topography are essential to achieve good performance at a 1.1 km resolution. [ABSTRACT FROM AUTHOR]