Representation Of Orography Research Articles

Exploring hail and lightning diagnostics over the Alpine-Adriatic region in a km-scale climate model

Abstract. 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.

Assessment of performance of the regional climate model (RegCM4.6) to simulate winter rainfall in the north of Morocco: The case of Tangier-Tétouan-Al-Hociema Region

We examine the performance of the regional climate model RegCM v4.6 to simulate spatial variability of precipitation in the northwestern region of Morocco during the winter of 2009–2010. Simulations cover 24 months from 2009 to 2010 with 30 km as a horizontal grid. We use NCEP reanalysis as forcing data and for better comparison of results, observed precipitations derived from CRU, CHIRPS, and CMORPH data. Results indicate that, on the whole, the RegCM4 model represents appropriate regional aspects of rainfall over the study area but underestimates precipitations over mountainous and Mediterranean regions of the study area (Case of Tangier-Tétouan-Al-Hociema Region) which is probably due to poor representation of orography in the Model and some aspects of local Mediterranean climate. Projected precipitations are also examined in this work in comparison with the reference period of 1970–2005, with simulations performed by RegCM 4.6 regional model for the period 2023–2099 under scenarios RCP4.5 and RCP8.5, forced by HadGEM2-ES General Circulation Model. Results show a decrease in precipitations mean for (2023–2099) for both RCP4.5 and RCP8.5 scenarios over the study area in comparison with the historical period (1970–2005), with a significant decrease under RCP8.5 scenarios. This work proves that the RegCM v4.6 model can be used for regional climate prediction, particularly for the spatial distribution of precipitation, but for sectorial applications and impact studies, the Model outputs should be bias corrected.

Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050

Context. Sites for next-generation telescopes are chosen decades before the first light of a telescope. Site selection is usually based on recent measurements over a period that is too short to account for long-term changes in observing conditions such as those arising from anthropogenic climate change. For astronomical facilities with a typical lifetime of 30 yr, it is therefore essential to be aware of climate evolution to optimise observing time. Aims. In this study, we analyse trends in astronomical observing conditions for eight sites. Most sites either already host telescopes that provide in situ measurements of weather parameters or are candidates for hosting next-generation telescopes. For a fine representation of orography, we use the highest resolution global climate model (GCM) ensemble available provided by the high-resolution model intercomparison project and developed as part of the European Union Horizon 2020 PRIMAVERA project. Methods. We evaluate atmosphere-only and coupled PRIMAVERA GCM historical simulations against in situ measurements and the fifth generation atmospheric reanalysis (ERA5) of the European centre for medium-range weather forecasts for the period 1979–2014. The projections of changes in current site conditions are then analysed for the period 2015–2050 using PRIMAVERA future climate simulations. Results. Over most sites, we find that PRIMAVERA GCMs show a good agreement in temperature, specific humidity, and precipitable water vapour compared to in situ observations and ERA5. The ability of PRIMAVERA to simulate those variables increases confidence in their projections. For those variables, the model ensemble projects an increasing trend for all sites, which will result in progressively poorer astronomical observing conditions compared to current conditions. On the other hand, no significant trends are projected for relative humidity, cloud cover, or astronomical seeing and PRIMAVERA does not simulate these variables well compared to observations and reanalyses. Therefore, there is little confidence in these projections. Conclusions. Our results show that climate change will negatively impact the quality of astronomical observations and is likely to increase time lost due to bad site conditions. We stress that it is essential for astronomers to include long-term climate projections in their process for site selection and monitoring. We show that high-resolution GCMs can be used to analyse the effect of climate change on site characteristics of next-generation telescopes.

Combining data assimilation and machine learning to estimate parameters of a convective‐scale model

AbstractErrors in the representation of clouds in convection‐permitting numerical weather prediction models can be introduced by different sources. These can be the forcing and boundary conditions, the representation of orography, the accuracy of the numerical schemes determining the evolution of humidity and temperature, but large contributions are due to the parametrization of microphysics and the parametrization of processes in the surface and boundary layers. These schemes typically contain several tunable parameters that are either not physical or only crudely known, leading to model errors. Traditionally, the numerical values of these model parameters are chosen by manual model tuning. More objectively, they can be estimated from observations by the augmented state approach during the data assimilation. Alternatively, in this work, we look at the problem of parameter estimation through an artificial intelligence lens by training two types of artificial neural network (ANN) to estimate several parameters of the one‐dimensional modified shallow‐water model as a function of the observations or analysis of the atmospheric state. Through perfect model experiments we show that Bayesian neural networks (BNNs) and Bayesian approximations of point estimate neural networks (NNs) are able to estimate model parameters and their relevant statistics. The estimation of parameters combined with data assimilation for the state decreases the initial state errors even when assimilating sparse and noisy observations. The sensitivity to the number of ensemble members, observation coverage and neural network size is shown. Additionally, we use the method of layer‐wise relevance propagation to gain insight into how the ANNs are learning and discover that they naturally select only a few grid points that are subject to strong winds and rain to make their predictions of chosen parameters.

Coupled Climate Model Simulation of Tropical–Extratropical Cloud Bands over Southern Africa

AbstractThere are increasing efforts to use climate model output for adaptation planning, but meanwhile there is often limited understanding of how models represent regional climate. Here we analyze the simulation in global coupled climate models of a key rainfall-generating mechanism over southern Africa: tropical temperate troughs (TTTs). An image-processing algorithm is applied to outgoing longwave radiation data from satellites and models to create TTT event sets. All models investigated produce TTTs with similar circulation features to observed. However, there are large differences among models in the number, intensity, and preferred longitude of events. Five groups of models are identified. The first group generates too few TTTs, and relatively dry conditions over southern Africa compared to other models. A second group generates more TTTs and wet biases. The contrast between these two groups suggests that the number of TTTs could explain intermodel variations in climatological rainfall. However, there is a third group of models that simulate up to 92% more TTTs than observed, but do not have large rainfall biases, as each TTT event is relatively weak. Finally, there are a further two groups that concentrate TTTs over the subcontinent or the ocean, respectively. These distinctions between models are associated with the amount of convective activity in the Congo Basin, the magnitude of moisture fluxes into southern Africa, and the degree of zonal asymmetry in upper-level westerly flow. Model development focused on tropical convection and the representation of orography is needed for improved simulation of TTTs, and therefore southern African rainfall.

The Influence of the Resolution of Orography on the Simulation of Orographic Moist Convection

Abstract Currently, major efforts are under way to refine the horizontal resolution of weather and climate models to kilometer-scale grid spacing (Δx). Besides refining the representation of the atmospheric dynamics and enabling the use of explicit convection, this will also provide higher resolution in the representation of orography. This study investigates the influence of these resolution increments on the simulation of orographic moist convection. Nine days of fair-weather thermally driven flow over the Alps are analyzed. Two sets of simulations with the COSMO model are compared, each consisting of three runs at Δx of 4.4, 2.2, and 1.1 km: one set using a fixed representation of orography at a resolution of 8.8 km, and one with varying representation at the resolution of the computational mesh. The spatial distribution of precipitation during daytime is only marginally affected by the orographic details, but nighttime convection to the south of the Alps—triggered by cold-air outflow from the valleys—is very sensitive to orography and precipitation is enhanced if more detailed orography is provided. During daytime, the onset of precipitation is delayed. The amplitude of the diurnal cycle of precipitation is reduced, even though more moisture converges toward the Alpine region during the afternoon. The hereby accumulated moisture sustains precipitation during the evening and nighttime over the surrounding plains. For these differences, the effects of changes in orographic detail are more important than changes in grid spacing. In addition, the individual convective cells are weaker, but their number increases with higher resolved orography.

Added Value of a Dynamical Downscaling Approach for Simulating Precipitation and Temperature Over Tianshan Mountains Area, Central Asia

AbstractProgresses in climatological, ecological, and hydrological studies that focused on the Tianshan mountains area (known as “the water tower of central Asia”) have been restricted by the availability of station observations as well as high resolution and quality data set. With the aim to overcome some of these difficulties, the high‐resolution Weather Research and Forecasting (WRF) regional climate model is run over the Tianshan mountains area driven by the ERA‐Interim reanalysis. Double nesting method was used with a horizontal resolution of 40 and 8 km covering the period 1980–2018. A decade of simulation from 1980 to 1989, a period when most abundant station observations are available, is considered for validation. These downscaled results are compared against station observations, ERA‐Interim reanalysis, and three widely used spatially interpolated products in order to investigate the added value of the dynamical downscaling approach. Results of these comparisons show that the WRF‐downscaled data outperform and add significant details to ERA‐Interim reanalysis. A remarkable improvement of the WRF simulation is found at reproducing the observed seasonal cycle of daily extreme temperatures and precipitation. Due to better representation of orography, WRF simulations are able to capture extreme precipitation events that are missing in the high‐quality interpolated products. Refining the resolution from 40 to 8 km further improves the model performance, particularly at depicting orographic enhancement of precipitation. The validated WRF model can be used in future climate projections studies, and this high‐resolution as well as high‐quality climatological data set we present here is useful for impact and further downstream studies.

Uncertainty in the Representation of Orography in Weather and Climate Models and Implications for Parameterized Drag

AbstractThe representation of orographic drag remains a major source of uncertainty for numerical weather prediction (NWP) and climate models. Its accuracy depends on contributions from both the model grid‐scale orography and the subgrid‐scale orography (SSO). Different models use different source orography data sets and different methodologies to derive these orography fields. This study presents the first comparison of orography fields across several operational global NWP models. It also investigates the sensitivity of an orographic drag parameterization to the intermodel spread in SSO fields and the resulting implications for representing the Northern Hemisphere winter circulation in a NWP model. The intermodel spread in both the grid‐scale orography and the SSO fields is found to be considerable. This is due to differences in the underlying source data set employed and in the manner in which this data set is processed (in particular how it is smoothed and interpolated) to generate the model fields. The sensitivity of parameterized orographic drag to the intermodel variability in SSO fields is shown to be considerable and dominated by the influence of two SSO fields: the standard deviation and the mean gradient of the SSO. NWP model sensitivity experiments demonstrate that the intermodel spread in these fields is of first‐order importance to the intermodel spread in parameterized surface stress, and to current known systematic model biases. The revealed importance of the SSO fields supports careful reconsideration of how these fields are generated, guiding future development of orographic drag parameterizations and reevaluation of the resolved impacts of orography on the flow.

Impact of Cartosat-1 orography on weather prediction in a high-resolution NCMRWF unified model

The current study reports for the first time an application of orography from the Cartosat-1 satellite digital elevation model (DEM) generated at a source resolution of 30 m in a convection-permitting numerical weather prediction model. The effects of improvements in the representation of orography have been examined in the high-resolution regional National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model predictions for a heavy rainfall event over the city of Chennai. A time-lagged ensemble method is employed to account for the uncertainties associated with the initial conditions, which can better forecast extreme weather events than single forecasts. The simulations reveal that the predictions based on Cartosat-1 DEM capture the local details of the rainfall distribution better than the National Aeronautics and Space Administration shuttle radar topography mission DEM-based predictions, and better represent the orographic and thermal uplifting. The spatio-temporal patterns of the simulated rainfall over Chennai are superior in Cartosat-1 DEM-based simulations mainly due to the enhanced wind convergence and moisture transport. The present study reveals the role of mountains in the enhancement of heavy rainfall events over coastal cities and highlights the potential use of high-resolution orography in the improvement of the operational weather forecasting skill of the NCMRWF Unified Model.

Response of Indian monsoon to increase of resolution in NCAR-CAM5

Effects of increase in horizontal resolution (from 2°, to 1° and 0.5°) in simulating Indian summer monsoon (ISM) in Community Atmosphere Model version 5 (CAM5) are reported herein. Various parameters — temperature, rainfall, winds, surface evaporation, precipitable water, specific humidity, and clouds — of the model simulations are examined with respect to observations {India Meteorological Department (IMD) and Tropical Rainfall Measuring Mission (TRMM) data for precipitation and Modern-Era Retrospective analysis for Research and Applications (MERRA) products for rest of the variables}. With increase in horizontal resolution overall improvement in majority of surface driven parameters like surface air temperature, surface specific humidity, and low-level winds is observed that highlights the importance of better representation of orography. Over the Indian subcontinent and surrounding oceans, rainfall is overestimated due to the overestimation of the large-scale precipitation in the model, which significantly gets alleviated with increase in resolution. Overall, seasonal mean features of ISM show significant improvements, which are likely to further improve by representing finer spatial features with increase in resolution. However, some aspects of its temporal variations — seasonal, intraseasonal, and diurnal variations — of monsoon rainfall do not show much improvement with increase in resolution, suggesting that traditional physics parameterizations do not work well at high resolution and hence there is a need for improvement in model physics.

Comparison of statistical and dynamical downscaling methods for seasonal‐scale winter precipitation predictions over north India

The main aim of the present study is to analyse the capabilities of two downscaling approaches (statistical and dynamical) in predicting wintertime seasonal precipitation over north India. For this purpose, a canonical correlation analysis (CCA) based statistical downscaling approach and dynamical downscaling approach (at 30 km) with an optimized configuration of the regional climate model (RegCM) nested in coarse resolution global spectral model have been used for a period of 28 years (1982–2009). For CCA, nine predictors (precipitation, zonal and meridional winds at 850 and 200 hPa, temperature at 200 hPa and sea surface temperatures) over three different domains were selected. The predictors were chosen based on the statistically significant teleconnection maps and physically based relationships between precipitation over the study region and meteorological variables. The validation revealed that both the downscaling approaches provided improved precipitation forecasts compared to the global model. Reasons for improved prediction by downscaling techniques have been examined. The improvement mainly comes due to better representation of orography, westerly moisture transport and vertical pressure velocity in the regional climate model. Furthermore, two bias correction methods namely quantile mapping (QM) and mean bias‐remove (MBR) have been applied on downscaled RegCM, statistically downscaled CCA as well as the global model products. It was found that when the QM‐based bias correction is applied on dynamically downscaled RegCM products, it has better skill in predicting wintertime precipitation over the study region compared to the CCA‐based statistical downscaling. Overall, the results indicate that the QM‐based bias‐corrected downscaled RegCM model is a useful tool for wintertime seasonal‐scale precipitation prediction over north India.

ICON‐A, The Atmosphere Component of the ICON Earth System Model: II. Model Evaluation

AbstractWe evaluate the new icosahedral nonhydrostatic atmospheric (ICON‐A) general circulation model of the Max Planck Institute for Meteorology that is flexible to be run at grid spacings from a few tens of meters to hundreds of kilometers. A simulation with ICON‐A at a low resolution (160 km) is compared to a not‐tuned fourfold higher‐resolution simulation (40 km). Simulations using the last release of the ECHAM climate model (ECHAM6.3) are also presented at two different resolutions. The ICON‐A simulations provide a compelling representation of the climate and its variability. The climate of the low‐resolution ICON‐A is even slightly better than that of ECHAM6.3. Improvements are obtained in aspects that are sensitive to the representation of orography, including the representation of cloud fields over eastern‐boundary currents, the latitudinal distribution of cloud top heights, and the spatial distribution of convection over the Indian Ocean and the Maritime Continent. Precipitation over land is enhanced, in particular at high‐resolution ICON‐A. The response of precipitation to El Niño sea surface temperature variability is close to observations, particularly over the eastern Indian Ocean. Some parameterization changes lead to improvements, for example, with respect to rain intensities and the representation of equatorial waves, but also imply a warmer troposphere, which we suggest leads to an unrealistic poleward mass shift. Many biases familiar to ECHAM6.3 are also evident in ICON‐A, namely, a too zonal SPCZ, an inadequate representation of north hemispheric blocking, and a relatively poor representation of tropical intraseasonal variability.

Climate model biases in jet streams, blocking and storm tracks resulting from missing orographic drag

AbstractState‐of‐the art climate models generally struggle to represent important features of the large‐scale circulation. Common model deficiencies include an equatorward bias in the location of the midlatitude westerlies and an overly zonal orientation of the North Atlantic storm track. Orography is known to strongly affect the atmospheric circulation and is notoriously difficult to represent in coarse‐resolution climate models. Yet how the representation of orography affects circulation biases in current climate models is not understood. Here we show that the effects of switching off the parameterization of drag from low‐level orographic blocking in one climate model resemble the biases of the Coupled Model Intercomparison Project Phase 5 ensemble: An overly zonal wintertime North Atlantic storm track and less European blocking events, and an equatorward shift in the Southern Hemispheric jet and increase in the Southern Annular Mode time scale. This suggests that typical circulation biases in coarse‐resolution climate models may be alleviated by improved parameterizations of low‐level drag.

Precipitation in the EURO-CORDEX $$0.11^{\circ }$$ 0 . 11 ∘ and $$0.44^{\circ }$$ 0 . 44 ∘ simulations: high resolution, high benefits?

In the framework of the EURO-CORDEX initiative an ensemble of European-wide high-resolution regional climate simulations on a $$0.11^{\circ }\,({\sim}12.5\,\hbox {km})$$ grid has been generated. This study investigates whether the fine-gridded regional climate models are found to add value to the simulated mean and extreme daily and sub-daily precipitation compared to their coarser-gridded $$0.44^{\circ }\,({\sim}50\,\hbox {km})$$ counterparts. Therefore, pairs of fine- and coarse-gridded simulations of eight reanalysis-driven models are compared to fine-gridded observations in the Alps, Germany, Sweden, Norway, France, the Carpathians, and Spain. A clear result is that the $$0.11^{\circ }$$ simulations are found to better reproduce mean and extreme precipitation for almost all regions and seasons, even on the scale of the coarser-gridded simulations (50 km). This is primarily caused by the improved representation of orography in the $$0.11^{\circ }$$ simulations and therefore largest improvements can be found in regions with substantial orographic features. Improvements in reproducing precipitation in the summer season appear also due to the fact that in the fine-gridded simulations the larger scales of convection are captured by the resolved-scale dynamics . The $$0.11^{\circ }$$ simulations reduce biases in large areas of the investigated regions, have an improved representation of spatial precipitation patterns, and precipitation distributions are improved for daily and in particular for 3 hourly precipitation sums in Switzerland. When the evaluation is conducted on the fine (12.5 km) grid, the added value of the $$0.11^{\circ }$$ models becomes even more obvious.

Dynamical downscaling approach for wintertime seasonal-scale simulation over the Western Himalayas

The performance of RegCM4 for seasonal-scale simulation of winter circulation and associated precipitation over the Western Himalayas (WH) is examined. The model simulates the circulation features and precipitation in three distinct precipitation years reasonably well. It is found that the RMSE decreases and correlation coefficient increases in the precipitation simulations with the increase of model horizontal resolutions. The ETS and POD for the simulated precipitation also indicate that the performance of model is better at 30 km resolution than at 60 and 90 km resolutions. This improvement comes due to better representation of orography in the high-resolution model in which sharp orography gradient in the domain plays an important role in wintertime precipitation processes. A comparison of model-simulated precipitation with observed precipitation at 17 station locations has been carried out. Overall, the results suggest that 30 km model produced better skill in simulating the precipitation over the WH and this model is a useful tool for further regional downscaling studies.

The sensitivity of the tropical circulation and Maritime Continent precipitation to climate model resolution

The dependence of the annual mean tropical precipitation on horizontal resolution is investigated in the atmospheric version of the Hadley Centre General Environment Model. Reducing the grid spacing from about 350 km to about 110 km improves the precipitation distribution in most of the tropics. In particular, characteristic dry biases over South and Southeast Asia including the Maritime Continent as well as wet biases over the western tropical oceans are reduced. The annual-mean precipitation bias is reduced by about one third over the Maritime Continent and the neighbouring ocean basins associated with it via the Walker circulation. Sensitivity experiments show that much of the improvement with resolution in the Maritime Continent region is due to the specification of better resolved surface boundary conditions (land fraction, soil and vegetation parameters) at the higher resolution. It is shown that in particular the formulation of the coastal tiling scheme may cause resolution sensitivity of the mean simulated climate. The improvement in the tropical mean precipitation in this region is not primarily associated with the better representation of orography at the higher resolution, nor with changes in the eddy transport of moisture. Sizeable sensitivity to changes in the surface fields may be one of the reasons for the large variation of the mean tropical precipitation distribution seen across climate models.

Performance of the cut‐cell method of representing orography in idealized simulations

AbstractSeveral tests of a model with a cut‐cell representation of orography are presented: a resting atmosphere test, advection across a hill and a warm rising bubble over hills with different gradients. The tests are compared with results from terrain‐following models. Results indicate that errors associated with terrain‐following coordinates are reduced, in some cases greatly reduced, with the cut‐cell approach. In a resting atmosphere, the cut‐cell approach does not generate flow around an isolated hill however steep the terrain. Relative errors in a rising bubble test are an order of magnitude smaller than terrain‐following simulations.

Linearly implicit peer methods for the compressible Euler equations

When cut cells are used for the representation of orography this leads to very small cells. For explicit methods this results in very harsh time step restrictions due to the CFL criterion. Therefore we consider linearly implicit peer methods for the integration of the compressible Euler equations. To be more efficient we use a simplified Jacobian. We present a linear stability theory which takes the effects of this simplified Jacobian in account. The developed second-order two-stage peer method is A-stable not only in the common sense but also when using the simplified Jacobian and retains the order independently of the Jacobian. In numerical tests the presented method produces good results even with time steps 100 times larger than explicit schemes could stably use.

Complexity of Snow Schemes in a Climate Model and Its Impact on Surface Energy and Hydrology

Abstract Three different complexity snow schemes implemented in the ECMWF land surface scheme Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) are evaluated within the EC-EARTH climate model. The snow schemes are (i) the original HTESSEL single-bulk-layer snow scheme, (ii) a new snow scheme in operations at ECMWF since September 2009, and (iii) a multilayer version of the previous. In offline site simulations, the multilayer scheme outperforms the single-layer schemes in deep snowpack conditions through its ability to simulate sporadic melting events thanks to the lower thermal inertial of the uppermost layer. Coupled atmosphere–land/snow simulations performed by the EC-EARTH climate model are validated against remote sensed snow cover and surface albedo. The original snow scheme has a systematic early melting linked to an underestimation of surface albedo during spring that was partially reduced with the new snow schemes. A key process to improve the realism of the near-surface atmospheric temperature and at the same time the soil freezing is the thermal insulation of the snowpack (tightly coupled with the accuracy of snow mass and density simulations). The multilayer snow scheme outperforms the single-layer schemes in open deep snowpack (such as prairies or tundra in northern latitudes) and is instead comparable in shallow snowpack conditions. However, the representation of orography in current climate models implies limitations for accurately simulating the snowpack, particularly over complex terrain regions such as the Rockies and the Himalayas.

Demonstration of a Cut-Cell Representation of 3D Orography for Studies of Atmospheric Flows over Very Steep Hills

Advances in computing are enabling atmospheric models to operate at increasingly fine resolution, giving rise to more variations in the underlying orography being captured by the model grid. Consequently, high-resolution models must overcome the problems associated with traditional terrain-following approaches of spurious winds and instabilities generated in the vicinity of steep and complex terrain. Cut-cell representations of orography present atmospheric models with an alternative to terrain-following vertical coordinates. This work explores the capabilities of a cut-cell representation of orography for idealized orographically forced flows. The orographic surface is represented within the model by continuous piecewise bilinear surfaces that intersect the regular Cartesian grid creating cut cells. An approximate finite-volume method for use with advection-form governing equations is implemented to solve flows through the resulting irregularly shaped grid boxes. Comparison with a benchmark orographic test case for nonhydrostatic flow shows very good results. Further tests demonstrate the cut-cell method for flow around 3D isolated hills and stably resolving flows over very steep orography.