Latitude-longitude Grid Research Articles

Global tsunami modelling on a spherical multiple-cell grid

A model of shallow water equations (SWEs) on a spherical multiple-cell (SMC) grid of 4-level (2.5-5-10-20 km) spatial resolutions is used to simulate tsunami propagation on global ocean surfaces. The unstructured SMC grid retains rectangular cells of the longitude-latitude grid so that efficient finite-difference schemes could be used. It also supports multi-resolutions like mesh refinement to resolve small islands and coastline details while keeping models compact enough to fit into available computers. Two earthquake-induced tsunami cases are simulated and compared with available observations. Results indicate that the modelled tsunami arrival times agree well with observations while tsunami wave heights are underestimated, particularly the observed runups on remote coastal lands. Possible reasons for this underestimation include the smoothing scheme used to suppress numerical oscillations and the missing of initial kinetic energy input from the earthquakes. Another reason is the limitation of the SWEs to describe coastal bores and breaking waves in coastal waters. A possible tsunami scenario induced by a landslide in the Canary Islands is also simulated to assess its potential impact on Atlantic coastal regions. Model results indicate that this kind of tsunami may cause severe damage to local areas but its effects on far fields, like the UK and American coastal regions are small. As the initial landslide disturbance is overly simplified, this study does not give a true representation of a real landslide tsunami but rather a qualitative assessment of its impact on the Atlantic Ocean. More realistic initial condition and improved model representation of coastal processes are needed for further studies of this possible landslide hazard.

Water ice particles detected by SELENE's Spectral Profiler at lunar shadowed regions in various local times and latitudes

Processes of water (OH and H2O) migration on the Moon remain unclear, prompting active research. Understanding lunar water migration requires investigation of the trapping and diffusion properties of water at various latitudes and local times. This study analyzed visible to near-infrared spectral data obtained by the Spectral Profiler (SP) onboard SELENE for shadowed regions at various local times and latitudes, not limited to the polar permanently shadowed regions. We assessed SP data for shadowed regions in 60 areas, each spanning a 10° × 10° latitude–longitude grid. Of the 1,061,907 analyzed shadowed-region data, 41,385 at various latitudes exhibited significant absorption in the 1.25 and 1.5 µm bands, indicating water ice particles. Data with the two absorption features suggest the presence of a water ice frost layer covering the lunar surface or suspended water ice particles above the lunar surface, at various latitude shadowed regions. Our spectral simulations have quantified the ice particles as being 0.1–1 µm in diameter, with a column density of 10–4–10–3 kg/m2. The spectral parameters for band absorption at the 1.5 µm band show symmetry between morning and evening sides, which is potentially attributed to the absence of variations in ice grain size and quantity. The 1.5 µm band absorption shows an increasing trend toward terminator regions, indicating variation in the water ice distribution and likely reflecting temperature conditions for water retention. The latitudinal trend of ice grain size and quantity remains uncertain because of the observed noise levels. Observations of water ice particles in shadowed regions at various latitudes and local times can provide new constraints on trapping and diffusion processes of lunar water migration.

GERB Obs4MIPs: a dataset for evaluating diurnal and monthly variations in top-of-atmosphere radiative fluxes in climate models

Abstract. A newly available radiative flux dataset specifically designed to enable the evaluation of the diurnal cycle in top-of-atmosphere (TOA) fluxes as captured by climate and Earth system models is presented. Observations over the period 2007–2012 made by the Geostationary Earth Radiation Budget (GERB) instrument are used to derive monthly hourly mean outgoing longwave radiation (OLR) and reflected shortwave (RSW) fluxes on a regular 1° latitude–longitude grid covering approximately 60° N–60° S and 60° E–60° W. The impact of missing data is evaluated in detail, and a data-filling solution is implemented using estimates of broadband fluxes from the Spinning Enhanced Visible and Infrared Imager flying on the same Meteosat platform, scaled to the GERB observations. This relatively simple approach is shown to deliver an approximate improvement by a factor of 10 in both the bias caused by missing data and the associated variability in the error. To demonstrate the utility of this V1.1 filled GERB Observations for Climate Model Intercomparison Projects (Obs4MIPs) dataset, comparisons are made to radiative fluxes from two climate configurations of the Hadley Centre's Global Environmental Model: HadGEM3-GC3.1 and HadGEM3-GC5.0. Focusing on marine stratocumulus and deep convective cloud regimes, diurnally resolved comparisons between the models and observations highlight discrepancies between the model configurations in terms of their ability to capture the diurnal amplitude and the phase in TOA fluxes, details that cannot be diagnosed by comparisons at lower temporal resolutions. For these cloud regimes the GC5.0 configuration shows improved fidelity to the observations relative to GC3.1, although notable differences remain. The V1.1 filled GERB Obs4MIPs monthly hourly TOA fluxes are available from the Centre for Environmental Data Analysis, with the OLR fluxes accessible at https://doi.org/10.5285/90148d9b1f1c40f1ac40152957e25467 (Bantges et al., 2023a) and the RSW fluxes accessible at https://doi.org/10.5285/57821b58804945deaf4cdde278563ec2 (Bantges et al., 2023b).

Increasing aerosol optical depth spatial and temporal availability by merging datasets from geostationary and sun-synchronous satellites

Abstract. This comprehensive study analyzed aerosol products from six low-Earth orbit (LEO) and geostationary Earth orbit (GEO) sensors. LEO sensors like the MODerate resolution Imaging Spectroradiometer (MODIS) and VIsible InfraRed Suite (VIIRS) provide one to two daily global measurements, while GEO sensors (Advanced Himawari Imager: AHI, Advanced Baseline Imager: ABI) offer high-frequency data (∼ 10 min) over specific regions. The combination of LEO and GEO capabilities offers expanded coverage of the global aerosol system if aerosol retrievals are applied consistently across all sensors and packaged in an easy-to-use product. The Dark Target aerosol retrieval algorithm was applied to the six sensors, and the resulting Level 2 aerosol optical depth (AOD) products were gridded and merged into a Level 3 quarter-degree latitude–longitude grid with a 30 min temporal resolution, providing the necessary consistency and packaging. Validation of this packaged Level 3 AOD product against Aerosol Robotics NETwork (AERONET) measurements across global locations showcased the merged product's robustness with a correlation coefficient of 0.83, revealing a global mean bias of approximately ±0.05, with 65.5 % of retrievals falling within an expected uncertainty range, underlining the reliability of the dataset. The new gridded Level 3 dataset significantly improved daily global coverage to nearly 45 %, overcoming the limitations of individual sensors, which typically range from 12 % to 25 %. Furthermore, this merged dataset approximates the diurnal cycle of AOD observed by AERONET, thus offering insights into diurnal signatures retrieved elsewhere. The resulting dataset's high spatiotemporal resolution and improved global coverage, especially in regions covered by GEO sensors (Americas and Asia), make it a valuable tool for diverse applications. Tracking aerosol transport from phenomena like wildfires and dust storms is gaining precision, enabling enhanced air quality forecasting and hindcasting. Additionally, the study positions the merged dataset as a significant asset for evaluating and intercomparing regional or global model simulations, which was previously unattainable in such a gridded format. The dataset and fusion framework layout in this study have the potential to include data from recently (future) launched other GEO (FCI, AMI) and LEO (PACE, VIIRS-JPSS) sensors.

A Global C-Staggered Composite Model for Shallow Water Equations with Latitude–Longitude Grid and Reductions in the Polar Regions

To develop a numerical method for global geophysical fluids, we usually need to choose a spherical grid and numerical approximations to represent the partial derivative equations. Some alternatives include the use of finite differences or finite volumes with latitude–longitude or reduced grids. Each of these cases has some advantages and also some limitations. This paper presents a comparison between two methods and describes a composite model using them side by side. The first is a well-known method for latitude–longitude grids and was used from 75[Formula: see text][Formula: see text]S until 75[Formula: see text][Formula: see text]N. The second is a recently developed scheme for reduced grids and was used only in the polar regions. The similarity between the two methods allows the use of small adaptations in their approximations to obtain consistency and mass conservation also in the transition between the two regions. The composite model combines advantages of the other two schemes and has a smaller computational cost. Numerical tests indicated order 2 of convergence, prevention of grid-imprinting errors, and avoidance of nonlinear instability. This model has numerical properties that may lead to efficient implementations with massive parallel computation.

ВЛИЯНИЕ ВНЕШНИХ ПАРАМЕТРОВ НА ВЕЛИЧИНУ ЭВАПОТРАНСПИРАЦИИ В МОДЕЛИ ДЕЯТЕЛЬНОГО СЛОЯ СУШИ ИВМ РАН - МГУ

The paper investigates the sensitivity of evapotranspiration in the INM RAS-MSU land surface model to the changes in the weights of land surface classes in the cells of the latitude-longitude grid and the leaf area index LAI. It is demonstrated that the refinement of the values of the mentioned parameters based on modern observational data significantly reduces an error in evapotranspiration. The annual sum of evapotranspiration averaged over 10 years (2002-2012) from the surface of medium-sized (2-50 × 103 km2) watersheds of northern European Russia is used for the analysis. The model error has been calculated against the empirical estimates of evapotranspiration obtained from the watershed water balance equation. As an intermediate task, the accuracy of satellite data on terrestrial water storage used in the calculations is assessed by the comparison with the data of snow route surveys.

Satellite-based time-series of sea-surface temperature since 1980 for climate applications

A 42-year climate data record of global sea surface temperature (SST) covering 1980 to 2021 has been produced from satellite observations, with a high degree of independence from in situ measurements. Observations from twenty infrared and two microwave radiometers are used, and are adjusted for their differing times of day of measurement to avoid aliasing and ensure observational stability. A total of 1.5 × 1013 locations are processed, yielding 1.4 × 1012 SST observations deemed to be suitable for climate applications. The corresponding observation density varies from less than 1 km−2 yr−1 in 1980 to over 100 km−2 yr−1 after 2007. Data are provided at their native resolution, averaged on a global 0.05° latitude-longitude grid (single-sensor with gaps), and as a daily, merged, gap-free, SST analysis at 0.05°. The data include the satellite-based SSTs, the corresponding time-and-depth standardised estimates, their standard uncertainty and quality flags. Accuracy, spatial coverage and length of record are all improved relative to a previous version, and the timeseries is routinely extended in time using consistent methods.

Spline Model: A Hydrostatic/Non-Hydrostatic Dynamic Core with Space-Time Second-Order Precision and Its Exact Tests

We present a new explicit quasi-Lagrangian integration scheme with the three-dimensional cubic spline function transform (transform = fitting + interpolation, referred to as the “spline format”) on a spherical quasi-uniform longitude–latitude grid. It is a consistent longitude–latitude grid, and to verify the feasibility, accuracy, convergence, and stability of the spline format interpolation scheme for the upstream point on the longitude–latitude grid, which may map a quasi-uniform longitude–latitude grid, a set of ideal, exact test schemes is adopted, which are recognized and proven to be effective internationally. The equilibrium flow test, cross-polar flow test, and Rossby–Haurwitz wave test are used to illustrate the spline scheme uniformity to the linear scheme and to overcome the over-dense grid in the polar region and the non-singularity of the poles. The cross-polar flow test demonstrates that the geostrophic wind crosses the polar area correctly, including the South Pole and North Pole. A non-hydrostatic, fully compressible dynamic core is used to complete the density flow test, demonstrating the existence of a time-varying reference atmosphere and that the spline format can simulate highly nonlinear fine-scale transient flows. It can be compared for the two results of the density flow test between the solution with the spline format and the benchmark reference solution with the linear format. Based on the findings, the non-hydrostatic dynamic core with the spline format is recommended for adoption. When simulated for the flow over an ideal mountain, through the “topographic gravity wave test”, the bicubic surface terrain and terrain-following height coordinates, time-split integration, and vector discrete decomposition can be derived successfully. These may serve as the foundations for a global, unified spline-format numerical model in the future.

A Long-Time-Step-Permitting Tracer Transport Model on the Regular Latitude–Longitude Grid

A Long-Time-Step-Permitting Tracer Transport Model on the Regular Latitude–Longitude Grid

Physics-Inspired Adaptions to Low-Parameter Neural Network Weather Forecast Systems

Abstract Recently, there has been a surge of research on data-driven weather forecasting systems, especially applications based on convolutional neural networks (CNNs). These are usually trained on atmospheric data represented on regular latitude–longitude grids, neglecting the curvature of Earth. We assess the benefit of replacing the standard convolution operations with an adapted convolution operation that takes into account the geometry of the underlying data (SphereNet convolution), specifically near the poles. Additionally, we assess the effect of including the information that the two hemispheres of Earth have “flipped” properties—for example, cyclones circulating in opposite directions—into the structure of the network. Both approaches are examples of physics-informed machine learning. The methods are tested on the WeatherBench dataset, at a resolution of ∼1.4°, which is higher than many previous studies on CNNs for weather forecasting. For most lead times up to day +10 for 500-hPa geopotential and 850-hPa temperature, we find that using SphereNet convolution or including hemisphere-specific information individually leads to improvement in forecast skill. Combining the two methods typically gives the highest forecast skill. Our version of SphereNet is implemented flexibly and scales well to high-resolution datasets but is still significantly more expensive than a standard convolution operation. Finally, we analyze cases with high forecast error. These occur mainly in winter and are relatively consistent across different training realizations of the networks, pointing to flow-dependent atmospheric predictability.

A first global height-resolved cloud condensation nuclei data set derived from spaceborne lidar measurements

Abstract. We present a global multiyear height-resolved data set of aerosol-type-specific cloud condensation nuclei concentrations (nCCN) estimated from the spaceborne lidar aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. For estimating nCCN, we apply the recently introduced Optical Modelling of the CALIPSO Aerosol Microphysics (OMCAM) algorithm to the CALIPSO Level 2 Aerosol Profile product. The estimated nCCN are then gridded into a uniform latitude–longitude grid of 2∘×5∘, a vertical grid of resolution 60 m from the surface to an altitude of 8 km, and a temporal resolution of 1 month. The data span a total of 186 months, from June 2006 to December 2021. In addition, we provide a 3D aerosol-type-specific climatology of nCCN produced using the complete time series. We further highlight some potential applications of the data set in the context of aerosol–cloud interactions. The complete data set can be accessed at https://doi.org/10.1594/PANGAEA.956215 (Choudhury and Tesche, 2023).

Robust global mantle flow trajectories and their validation via dynamic topography histories

SUMMARYThe ability to construct time-trajectories of mantle flow is crucial to move from studies of instantaneous to time-dependent earth models and to exploit geological constraints for mantle convection modelling. However mantle convection is chaotic and subject to the butterfly effect: the trajectories of two identical mantle convection models initialized with slightly different temperature fields diverge exponentially in time until they become uncorrelated. Because one may use seismic inferences about the mantle state as a starting or terminal condition to project mantle flow forward or backward in time, and because the seismic inference is invariably subject to uncertainties, this seemingly would rule out any construction of robust mantle flow trajectories. Here we build upon earlier work which showed that assimilation of the horizontal component of the surface velocity field from a known reference model allows one to overcome the butterfly effect and to construct robust mantle flow trajectories, regardless of the choice of the initial state perturbation. To this end, we use high resolution 3-D spherical mantle convection models in four end-member configurations: an isoviscous purely internally heated model, an isoviscous purely bottom heated model, a model with a radial increase in viscosity along with pure internal heating as well as a model that combines the effects of radial viscosity increase, internal and bottom heating. In order to capture the impact of seismic filtering, we perturb the initial temperature fields of these end-member models through either radial or horizontal smoothing of the temperature field or the application of the tomographic filter of seismic model S20RTS. We assess the quality of the constructed model trajectories via a number of statistical measures as well as comparisons of their dynamic topography histories. The latter is an essential step since mantle flow cannot be directly observed but has to be inferred via its surface manifestations. Importantly, linking mantle flow to surface observations yields patterns representable on a latitude–longitude grid similar to meteorological observables such as precipitation. This invites the application of meteorological quality metrics, such as the power ratio and Taylor diagram, to assess the quality of mantle flow trajectories. We introduce these metrics for the first time in the context of mantle convection and demonstrate their viability based on the compact manner in which they summarize model performance.

Detection of Solar Filaments Using Suncharts from Kodaikanal Solar Observatory Archive Employing a Clustering Approach

With over 100 yr of solar observations, the Kodaikanal Solar Observatory (KoSO) is a one-of-a-kind solar data repository in the world. Among its many data catalogs, the “suncharts” at KoSO are of particular interest. These suncharts (1904–2020) are colored drawings of different solar features, such as sunspots, plages, filaments, and prominences, made on papers with a Stonyhurst latitude–longitude grid etched on them. In this paper, we analyze this unique data by first digitizing each sunchart using an industry-standard scanner and saving those digital images in a high-resolution “.tif” format. We then examine cycle 19 and cycle 20 data (two of the strongest cycles of the last century) with the aim of detecting filaments. To this end, we employed the “K-means clustering” method, and obtained different filament parameters such as position, tilt angle, length, and area. Our results show that filament length (and area) increases with latitude and the poleward migration is clearly dominated by a particular tilt sign. Lastly, we cross verified our findings with results from KoSO digitized photographic plate database for the overlapping time period and obtained a good agreement between them. This work, acting as a proof-of-the-concept, will kickstart new efforts to effectively use the entire hand-drawn series of multifeature, full-disk solar data and enable researchers to extract new sciences, such as the generation of pseudomagnetograms for the last 100 yr.

Towards learned emulation of interannual water isotopologue variations in General Circulation Models

Abstract Simulating abundances of stable water isotopologues, that is, molecules differing in their isotopic composition, within climate models allows for comparisons with proxy data and, thus, for testing hypotheses about past climate and validating climate models under varying climatic conditions. However, many models are run without explicitly simulating water isotopologues. We investigate the possibility of replacing the explicit physics-based simulation of oxygen isotopic composition in precipitation using machine learning methods. These methods estimate isotopic composition at each time step for given fields of surface temperature and precipitation amount. We implement convolutional neural networks (CNNs) based on the successful UNet architecture and test whether a spherical network architecture outperforms the naive approach of treating Earth’s latitude-longitude grid as a flat image. Conducting a case study on a last millennium run with the iHadCM3 climate model, we find that roughly 40% of the temporal variance in the isotopic composition is explained by the emulations on interannual and monthly timescale, with spatially varying emulation quality. The tested CNNs outperform simple baseline models such as random forest and pixel-wise linear regression substantially. A modified version of the standard UNet architecture for flat images yields results that are as good as the predictions by the spherical CNN. Variations in the implementation of isotopes between climate models likely contribute to an observed deterioration of emulation results when testing on data obtained from different climate models than the one used for training. Future work toward stable water-isotope emulation might focus on achieving robust climate–oxygen isotope relationships or exploring the set of possible predictor variables.

The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation

Abstract. The E3SM Diagnostics Package (E3SM Diags) is a modern, Python-based Earth system model (ESM) evaluation tool (with Python module name e3sm_diags), developed to support the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). E3SM Diags provides a wide suite of tools for evaluating native E3SM output, as well as ESM data on regular latitude–longitude grids, including output from Coupled Model Intercomparison Project (CMIP) class models. E3SM Diags is modeled after the National Center for Atmospheric Research (NCAR) Atmosphere Model Working Group (AMWG, 2022) diagnostics package. In its version 1 release, E3SM Diags included a set of core essential diagnostics to evaluate the mean physical climate from model simulations. As of version 2.7, more process-oriented and phenomenon-based evaluation diagnostics have been implemented, such as analysis of the quasi-biennial oscillation (QBO), the El Niño–Southern Oscillation (ENSO), streamflow, the diurnal cycle of precipitation, tropical cyclones, ozone and aerosol properties. An in situ dataset from DOE's Atmospheric Radiation Measurement (ARM) program has been integrated into the package for evaluating the representation of simulated cloud and precipitation processes. This tool is designed with enough flexibility to allow for the addition of new observational datasets and new diagnostic algorithms. Additional features include customizable figures; streamlined installation, configuration and execution; and multiprocessing for fast computation. The package uses an up-to-date observational data repository maintained by its developers, where recent datasets are added to the repository as they become available. Finally, several applications for the E3SM Diags module were introduced to fit a diverse set of use cases from the scientific community.

Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales

Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the tropics than other high-latitude regions, less attention has been paid to the areal weights of these grid data. Here, we revisited two methods available for processing these uniform grid data, including weighted sample statistics and grid interpolation. The former directly considers the grid area differences using geodetic weights; the latter converts the uniform grids to equal-area grids for conventional data analysis. When applied to global temperature and precipitation data, we found larger differences between weighted and unweighted samples and smaller differences between weighted and interpolated samples, highlighting the importance of areal weights in grid data analysis. Given the different results from various methods, we call for explicit clarification of the grid data processing methods to improve reproducibility in climate research.

A consistent mass-conserving C-staggered method for shallow water equations on global reduced grids

The increasing number of cores in modern supercomputers motivated the search for methods with good scalability to model global problems in geophysical fluid dynamics, including the representation of the atmospheric dynamics in global weather forecasting and climate simulations. Grid-point schemes with quasi-uniform spherical grids, along with explicit time integration, are one of the known options for recent dynamical core developments. These methods avoid the concentration of points in the poles and do not require global communication, intending to contribute to the efficiency of parallel implementations. On the other hand, they may yield low accuracy or present errors stimulated by the grid. Here, we target reduced latitude-longitude global grids and build from derivations of previous schemes to propose a method for the shallow water equations on a C-staggered global reduced grid using a combination of finite differences, finite volumes, and explicit time-stepping. This scheme employs only local approximations, has some conservative properties, and uses numerically consistent operators everywhere. Classical and recently proposed benchmarks were used to evaluate convergence, stability, and accumulation of errors. Results indicated that the proposed scheme provides adequate accuracy at competitive computational cost.

TriCCo v1.1.0 – a cubulation-based method for computing connected components on triangular grids

Abstract. We present a new method to identify connected components on triangular grids used in atmosphere and climate models to discretize the horizontal dimension. In contrast to structured latitude–longitude grids, triangular grids are unstructured and the neighbors of a grid cell do not simply follow from the grid cell index. This complicates the identification of connected components compared to structured grids. Here, we show that this complication can be addressed by involving the mathematical tool of cubulation, which allows one to map the 2-D cells of the triangular grid onto the vertices of the 3-D cells of a cubical grid. Because the latter is structured, connected components can be readily identified by previously developed software packages for cubical grids. Computing the cubulation can be expensive, but, importantly, needs to be done only once for a given grid. We implement our method in a Python package that we name TriCCo and make available via pypi, gitlab, and zenodo. We document the package and demonstrate its application using simulation output from the ICON atmosphere model. Finally, we characterize its computational performance and compare it to graph-based identifications of connected components using breadth-first search. The latter shows that TriCCo is ready for triangular grids with up to 500 000 cells, but that its speed and memory requirement should be improved for its application to larger grids.

The agony of choice: Species richness and range size in the determination of hotspots for the conservation of phyllostomid bats

Area-based strategies for conservation include defining species richness and rarity hotspots. However, excluding vulnerable species (e.g., with restricted distribution and categorized as threatened), in establishing such hotspots may limit their representativeness, so the convenience of asserting them has been widely debated. To inform conservation assessments for the New World leaf-nosed bats (Chiroptera: Phyllostomidae), we identified hotspots based simultaneously on species richness and rarity of 214 species. We projected species range maps on a 0.5° × 0.5° longitude-latitude grid, from which we built a presence-absence matrix with 6951 sites. Using range-diversity plots, we described richness-rarity hotspots (sites with high species richness and presence of rare species) and poorness-rarity hotspots (with low species richness and presence of rare species). We assess the representativeness of the hotspots within established protected areas using the World Database on Protected Areas. The richness-rarity hotspot was located in the Andean zone from Peru to Panama within which 46 species of phyllostomid bats are distributed and 37% of its surface is protected. While the poorness-rarity hotspot was located in the northern region of Mexico and the Caribbean with 50 phyllostomid bats species and 19% of its surface protected. We hope that our analysis represents a relevant tool for the conservation of phyllostomid bats, which provide several ecosystem services and are currently facing different threats derived from anthropogenic activities.

Hybrid multi-grid parallelisation of WAVEWATCH III model on spherical multiple-cell grids

Spherical Multiple-Cell (SMC) grid is an unstructured grid, supporting flexible domain shapes and multi-resolutions. It retains the quadrilateral cells as in the latitude-longitude (lat-lon) grid so that simple finite-difference schemes can be used. Sub-timesteps are applied on refined cells and grid cells are merged at high latitudes to relax the CFL restriction. A fixed reference direction is used in polar regions to solve vector polar problems. The SMC grid was implemented in the WAVEWATCH III (WW3) wave model in 2012 as an alternative for the lat-lon grid and updated in the latest WW3 V6.07. The WW3 model is parallelised by wave spectral component decomposition (CD) in MPI mode, which has a limit on number of MPI ranks. Hybrid or combined MPI-OpenMP parallelisation may extend the node usage but the OpenMP scalability flattens out beyond a few threads. Another parallelisation method that combines CD with domain decomposition (DD) is enabled in WW3 model by a multi-grid framework for further extension of node usage. Those regular lat-lon grid parallelisation options are gradually added to the SMC grid and this article reports the recent extension of the SMC grid into the multi-grid framework with hybrid parallelisation. The flexible domain shape of the SMC grid allows optimised domain splitting and minimised boundary exchanges. The combined CD-DD method is tested on SMC sub-grids with various hybrid node-thread combinations. Results indicate that switching from MPI to hybrid MPI-OpenMP mode can halve the global model elapsed time and using hybrid CD-DD on 3 SMC sub-grids may reduce it further by 30%. Elapsed time for one model day run is reduced from about 3 min on 12 nodes to less than 1 min on 90 or 180 nodes. Besides, the hybrid multi-grid method reduces memory demand on one computing node and allows future model updates for higher resolutions.