Geofluid Object Workbench (GeoFLOW) for Atmospheric Dynamics in the Approach to Exascale: Spectral Element Formulation and CPU Performance

D. Rosenberg aCooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado
bNational Oceanic and Atmospheric Administration/Global Systems Laboratory, Boulder, Colorado

Search for other papers by D. Rosenberg in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-0208-8689
,
B. Flynt aCooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado
bNational Oceanic and Atmospheric Administration/Global Systems Laboratory, Boulder, Colorado

Search for other papers by B. Flynt in
Current site
Google Scholar
PubMed
Close
,
M. Govett bNational Oceanic and Atmospheric Administration/Global Systems Laboratory, Boulder, Colorado

Search for other papers by M. Govett in
Current site
Google Scholar
PubMed
Close
, and
I. Jankov bNational Oceanic and Atmospheric Administration/Global Systems Laboratory, Boulder, Colorado

Search for other papers by I. Jankov in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A new software framework using a well-established high-order spectral element discretization is presented for solving the compressible Navier–Stokes equations for purposes of research in atmospheric dynamics in bounded and unbounded limited-area domains, with a view toward capturing spatiotemporal intermittency that may be particularly challenging to attain using low-order schemes. A review of the discretization is provided, emphasizing properties such as the matrix product formalism and other design considerations that will facilitate its effective use on emerging exascale platforms, and a new geometry-independent, element boundary exchange method is described to maintain continuity. A variety of test problems are presented that demonstrate accuracy of the implementation primarily in wave-dominated or transitional flow regimes; conservation properties are also demonstrated. A strong scaling CPU study in a three-dimensional domain without using threading shows an average parallel efficiency of ≳99% up to 2 × 104 MPI tasks that is not affected negatively by expansion polynomial order. On-node performance is also examined and reveals that, while the primary numerical operations achieve their theoretical arithmetic intensity, the application performance is largely limited by available memory bandwidth.

Significance Statement

This work considers the need for computationally efficient, high-order, low dissipation numerics to fully leverage emerging exascale computing resources in an effort to examine and improve the accuracy of numerical treatments of atmospheric and weather phenomena. A new spectral element implementation is introduced that attempts to address the issues involved. Well-understood tests are presented that illustrate the known efficacy of the method in wave-dominated, quasi-laminar, and relatively strong shear flow regimes, and good conservation properties for mass and total energy are achieved. Importantly, the implementation is shown to exhibit encouraging performance characteristics.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: D. Rosenberg, duane.rosenberg@noaa.gov

Abstract

A new software framework using a well-established high-order spectral element discretization is presented for solving the compressible Navier–Stokes equations for purposes of research in atmospheric dynamics in bounded and unbounded limited-area domains, with a view toward capturing spatiotemporal intermittency that may be particularly challenging to attain using low-order schemes. A review of the discretization is provided, emphasizing properties such as the matrix product formalism and other design considerations that will facilitate its effective use on emerging exascale platforms, and a new geometry-independent, element boundary exchange method is described to maintain continuity. A variety of test problems are presented that demonstrate accuracy of the implementation primarily in wave-dominated or transitional flow regimes; conservation properties are also demonstrated. A strong scaling CPU study in a three-dimensional domain without using threading shows an average parallel efficiency of ≳99% up to 2 × 104 MPI tasks that is not affected negatively by expansion polynomial order. On-node performance is also examined and reveals that, while the primary numerical operations achieve their theoretical arithmetic intensity, the application performance is largely limited by available memory bandwidth.

Significance Statement

This work considers the need for computationally efficient, high-order, low dissipation numerics to fully leverage emerging exascale computing resources in an effort to examine and improve the accuracy of numerical treatments of atmospheric and weather phenomena. A new spectral element implementation is introduced that attempts to address the issues involved. Well-understood tests are presented that illustrate the known efficacy of the method in wave-dominated, quasi-laminar, and relatively strong shear flow regimes, and good conservation properties for mass and total energy are achieved. Importantly, the implementation is shown to exhibit encouraging performance characteristics.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: D. Rosenberg, duane.rosenberg@noaa.gov
Save
  • Abdi, D. S., L. C. Wilcox, T. C. Warburton, and F. X. Giraldo, 2019: A GPU-accelerated continuous and discontinuous Galerkin non-hydrostatic atmospheric model. Int. J. High Perform. Comput. Appl., 33, 81109, https://doi.org/10.1177/1094342017694427.

    • Search Google Scholar
    • Export Citation
  • Adams, S. V., and Coauthors, 2019: LFRic: Meeting the challenges of scalability and performance portability in weather and climate models. J. Parallel Distrib. Comput., 132, 383396, https://doi.org/10.1016/j.jpdc.2019.02.007.

    • Search Google Scholar
    • Export Citation
  • Afanasyev, A., and Coauthors, 2021: Gridtools: A framework for portable weather and climate applications. SoftwareX, 15, 100707, https://doi.org/10.1016/j.softx.2021.100707.

    • Search Google Scholar
    • Export Citation
  • Baldauf, M., and S. Brdar, 2013: An analytical solution for linear gravity waves in a channel as a test for numerical models using the non-hydrostatic compressible Euler equations. Quart. J. Roy. Meteor. Soc., 139, 19771989, https://doi.org/10.1002/qj.2105.

    • Search Google Scholar
    • Export Citation
  • Beckmann, N., H.-P. Kriegel, R. Schneider, and B. Seeger, 1990: The R*-tree: An efficient and robust access method for points and rectangles. SIGMOD’90: Proc 1990 ACM SIGMOD Int. Conf. on Management of Data, Atlantic City, NJ, Association for Computing Machinery, 322–331, https://dl.acm.org/doi/10.1145/93597.98741.

  • Benacchio, T., and R. Klein, 2019: A semi-implicit compressible model for atmospheric flows with seamless access to soundproof and hydrostatic dynamics. Mon. Wea. Rev., 147, 42214240, https://doi.org/10.1175/MWR-D-19-0073.1.

    • Search Google Scholar
    • Export Citation
  • Bertagna, L., M. Deakin, O. Guba, D. Sunderland, A. M. Bradley, I. K. Tezaur, M. A. Taylor, and A. G. Salinger, 2019: Hommexx 1.0: A performance-portable atmospheric dynamical core for the Energy Exascale Earth System Model. Geosci. Model Dev., 12, 14231441, https://doi.org/10.5194/gmd-12-1423-2019.

    • Search Google Scholar
    • Export Citation
  • Buresti, G., 2015: A note on Stokes’ hypothesis. Acta Mech., 226, 35553559, https://doi.org/10.1007/s00707-015-1380-9.

  • Butcher, J. C., 2016: Numerical Methods for Ordinary Differential Equations. John Wiley and Sons, 544 pp.

  • Canuto, C., M. Y. Hussaini, A. Quarteroni, and T. A. Zang, 1988: Spectral Methods in Fluid Dynamics. Springer, 568 pp.

  • Cockburn, B., and C.-W. Shu, 2001: Runge–Kutta discontinuous Galerkin methods for convection-dominated problems. J. Sci. Comput., 16, 173261, https://doi.org/10.1023/A:1012873910884.

    • Search Google Scholar
    • Export Citation
  • Dennis, J. M., A. Fournier, W. F. Spotz, A. St-Cyr, M. A. Taylor, S. J. Thomas, and H. Tufo, 2005: High resolution mesh convergence properties and parallel efficiency of a spectral element atmospheric dynamical core. Int. J. High Perform. Comput. Appl., 19, 225235, https://doi.org/10.1177/1094342005056108.

    • Search Google Scholar
    • Export Citation
  • Dennis, J. M., and Coauthors, 2012: CAM-SE: A scalable spectral element dynamical core for the Community Atmosphere Model. Int. J. High Perform. Comput. Appl., 26, 7489, https://doi.org/10.1177/1094342011428142.

    • Search Google Scholar
    • Export Citation
  • Deville, M. O., P. F. Fischer, and E. H. Mund, 2002: High-Order Methods for Incompressible Fluid Flow. Cambridge University Press, 499 pp.

  • DOE, 2023: Exascale Computing Project (ECP). Accessed 17 February 2023, https://www.exascaleproject.org.

  • Eyink, G. L., and T. D. Drivas, 2018: Cascades and dissipative anomalies in compressible fluid turbulence. Phys. Rev., 8X, 011022, https://doi.org/10.1103/PhysRevX.8.011022.

    • Search Google Scholar
    • Export Citation
  • Frisch, U., 1995: Turbulence: The Legacy of A. N. Kolmogorov. Cambridge University Press, 296 pp.

  • Fuhrer, O., and Coauthors, 2018: Near-global climate simulation at 1 km resolution: Establishing a performance baseline on 4888 GPUs with COSMO 5.0. Geosci. Model Dev., 11, 16651681, https://doi.org/10.5194/gmd-11-1665-2018.

    • Search Google Scholar
    • Export Citation
  • Gal-Chen, T., and R. C. J. Somerville, 1975: On the use of a coordinate transformation for the solution of the Navier-Stokes equations. J. Comput. Phys., 17, 209228, https://doi.org/10.1016/0021-9991(75)90037-6.

    • Search Google Scholar
    • Export Citation
  • Giraldo, F. X., 2020 : An Introduction to Element-Based Galerkin Methods on Tensor-Product Bases: Analysis, Algorithms, and Applications. Springer Nature, 559 pp., https://doi.org/10.1007/978-3-030-55069-1.

  • Giraldo, F. X., and T. E. Rosmond, 2004: A scalable spectral element Eulerian atmospheric model (SEE-AM) for NWP: Dynamical core tests. Mon. Wea. Rev., 132, 133153, https://doi.org/10.1175/1520-0493(2004)132<0133:ASSEEA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Giraldo, F. X., and M. Restelli, 2008: A study of spectral element and discontinuous Galerkin methods for the Navier–Stokes equations in nonhydrostatic mesoscale atmospheric modeling: Equation sets and test cases. J. Comput. Phys., 227, 38493877, https://doi.org/10.1016/j.jcp.2007.12.009.

    • Search Google Scholar
    • Export Citation
  • Gordon, W. J., and C. A. Hall, 1973: Construction of curvilinear co-ordinate systems and applications to mesh generation. Int. J. Numer. Methods Eng., 7, 461477, https://doi.org/10.1002/nme.1620070405.

    • Search Google Scholar
    • Export Citation
  • Govett, M., and Coauthors, 2017: Parallelization and performance of the NIM Weather Model on CPU, GPU, and MIC processors. Bull. Amer. Meteor. Soc., 98, 22012213, https://doi.org/10.1175/BAMS-D-15-00278.1.

    • Search Google Scholar
    • Export Citation
  • Guttman, A., 1984: R-trees: A dynamic index structure for spatial searching. SIGMOD’84: Proc. 1984 ACM SIGMOD Int. Conf. on Management of Data, Boston, MA, Association for Computing Machinery, 47–57, https://dl.acm.org/doi/10.1145/971697.602266.

  • Kelly, J. F., and F. X. Giraldo, 2012: Continuous and discontinuous Galerkin methods for a scalable three-dimensional nonhydrostatic atmospheric model: Limited-area mode. J. Comput. Phys., 231, 79888008, https://doi.org/10.1016/j.jcp.2012.04.042.

    • Search Google Scholar
    • Export Citation
  • Kirshbaum, D. J., and C.-C. Wang, 2014: Boundary layer updrafts driven by airflow over heated terrain. J. Atmos. Sci., 71, 14251442, https://doi.org/10.1175/JAS-D-13-0287.1.

    • Search Google Scholar
    • Export Citation
  • Kopriva, D. A., 2006: Metric identities and the discontinuous spectral element method on curvilinear meshes. J. Sci. Comput., 26, 301327, https://doi.org/10.1007/s10915-005-9070-8.

    • Search Google Scholar
    • Export Citation
  • Kühnlein, C., W. Deconinck, R. Klein, S. Malardel, Z. P. Piotrowski, P. K. Smolarkiewicz, J. Szmelter, and N. P. Wedi, 2019: FVM: A nonhydrostatic finite volume dynamical core for the IFS. Geosci. Model Dev., 12, 651676, https://doi.org/10.5194/gmd-12-651-2019.

    • Search Google Scholar
    • Export Citation
  • Lock, S.-J., H.-W. Bitzer, A. Coals, A. Gadian, and S. Mobbs, 2012: Demonstration of a cut-cell representation of 3D orography for studies of atmospheric flows over very steep hills. Mon. Wea. Rev., 140, 411424, https://doi.org/10.1175/MWR-D-11-00069.1.

    • Search Google Scholar
    • Export Citation
  • Lopez, D. H., M. R. Rabbani, E. Crosbie, A. Raman, A. F. Arellano, and A. Sorooshian, 2016: Frequency and character of extreme aerosol events in the southwestern United States: A case study analysis in Arizona. Atmosphere, 7, 1, https://doi.org/10.3390/atmos7010001.

    • Search Google Scholar
    • Export Citation
  • Melvin, T., M. Dubal, N. Wood, A. Staniforth, and M. Zerroukat, 2010: An inherently mass-conserving iterative semi-implicit semi-Lagrangian discretization of the non-hydrostatic vertical slice equations. Quart. J. Roy. Meteor. Soc., 136, 799814, https://doi.org/10.1002/qj.603.

    • Search Google Scholar
    • Export Citation
  • Melvin, T., T. Benacchio, B. Shipway, N. Wood, J. Thuburn, and C. Cotter, 2019: A mixed finite-element, finite-volume, semi-implicit discretization for atmospheric dynamics: Cartesian geometry. Quart. J. Roy. Meteor. Soc., 145, 28352853, https://doi.org/10.1002/qj.3501.

    • Search Google Scholar
    • Export Citation
  • Middlecoff, J., Y. G. Yu, and M. W. Govett, 2023: Performance comparison of the A-grid and C-grid shallow-water models on icosahedral grids. Int. J. High Perform. Comput. Appl., 37, 197208, https://doi.org/10.1177/10943420221139509.

    • Search Google Scholar
    • Export Citation
  • Moxey, D., C. D. Cantwell, R. M. Kirby, and S. J. Sherwin, 2016: Optimising the performance of the spectral/hp element method with collective linear algebra operations. Comput. Methods Appl. Mech. Eng., 310, 628645, https://doi.org/10.1016/j.cma.2016.07.001.

    • Search Google Scholar
    • Export Citation
  • Müller, A., M. A. Kopera, S. Marras, L. C. Wilcox, T. Isaac, and F. X. Giraldo, 2019: Strong scaling for numerical weather prediction at petascale with the atmospheric model NUMA. Int. J. High Perform. Comput. Appl., 33, 411426, https://doi.org/10.1177/1094342018763966.

    • Search Google Scholar
    • Export Citation
  • Nair, R., L. Bao, M. D. Toy, and R. Kloefkorn, 2016: A high-order multiscale global atmospheric model. Eighth AIAA Atmospheric and Space Environments Conf., Washington, DC, The American Institute of Aeronautics and Astronautics, 13 pp., https://www.image.ucar.edu/staff/rnair/NairBaoToyKloefkorn_AIAA2016.pdf.

  • Ng, C. S., D. Rosenberg, K. Germaschewski, A. Pouquet, and A. Bhattacharjee, 2008: A comparison of spectral element and finite difference methods using statically refined nonconforming grids for the MHD island coalescence instability problem. Astrophys. J., 177 (Suppl.), 613, https://doi.org/10.1086/588139.

    • Search Google Scholar
    • Export Citation
  • Ooyama, K. V., 2001: A dynamic and thermodynamic foundation for modeling the moist atmosphere with parameterized microphysics. J. Atmos. Sci., 58, 20732102, https://doi.org/10.1175/1520-0469(2001)058<2073:ADATFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Patera, A. T., 1984: A spectral element method for fluid dynamics: Laminar flow in a channel expansion. J. Comput. Phys., 54, 468488, https://doi.org/10.1016/0021-9991(84)90128-1.

    • Search Google Scholar
    • Export Citation
  • Pathak, J., B. Hunt, M. Girvan, Z. Lu, and E. Ott, 2018: Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach. Phys. Rev. Lett., 120, 024102, https://doi.org/10.1103/PhysRevLett.120.024102.

    • Search Google Scholar
    • Export Citation
  • Pope, S., 2000: Turbulent Flows. Cambridge University Press, 771 pp.

  • Ringler, T. D., J. Thuburn, J. B. Klemp, and W. C. Skamarock, 2010: A unified approach to energy conservation and potential vorticity dynamics for arbitrarily-structured C-grids. J. Comput. Phys., 229, 30653090, https://doi.org/10.1016/j.jcp.2009.12.007.

    • Search Google Scholar
    • Export Citation
  • Rosenberg, D., A. Fournier, P. Fischer, and A. Pouquet, 2006: Geophysical-Astrophysical Spectral-element AdaptiveRefinement (GASpAR): Object-oriented h-adaptive fluid dynamics simulation. J. Comput. Phys., 215, 5980, https://doi.org/10.1016/j.jcp.2005.10.031.

    • Search Google Scholar
    • Export Citation
  • Rosinski, J., 2021: GPTL—General purpose timing library. Accessed 24 November 2021, https://jmrosinski.github.io/GPTL/.

  • Saito, I., and T. Gotoh, 2018: Turbulence and cloud droplets in cumulus clouds. New J. Phys., 20, 023001, https://doi.org/10.1088/1367-2630/aaa229.

    • Search Google Scholar
    • Export Citation
  • Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga, 2008: Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for global cloud resolving simulations. J. Comput. Phys., 227, 34863514, https://doi.org/10.1016/j.jcp.2007.02.006.

    • Search Google Scholar
    • Export Citation
  • Serafin, S., and Coauthors, 2018: Exchange processes in the atmospheric boundary layer over mountainous terrain. Atmosphere, 9, 102, https://doi.org/10.3390/atmos9030102.

    • Search Google Scholar
    • Export Citation
  • Shapiro, A., and K. M. Kanak, 2002: Vortex formation in ellipsoidal thermal bubbles. J. Atmos. Sci., 59, 22532269, https://doi.org/10.1175/1520-0469(2002)059<2253:VFIETB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, https://doi.org/10.1016/j.jcp.2007.01.037.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., J. B. Klemp, M. G. Duda, L. D. Fowler, S.-H. Park, and T. D. Ringler, 2012: A multiscale nonhydrostatic atmospheric model using centroidal Voronoi tesselations and C-grid staggering. Mon. Wea. Rev., 140, 30903105, https://doi.org/10.1175/MWR-D-11-00215.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2021: A description of the Advanced Research WRF Model version 4.3. NCAR Tech. Note NCAR/TN-556+STR, 165 pp., https://doi.org/10.5065/1dfh-6p97.

  • Spyksma, K., M. Magcalas, and N. Campbell, 2012: Quantifying effects of hyperviscosity on isotropic turbulence. Phys. Fluids, 24, 125102, https://doi.org/10.1063/1.4768809.

    • Search Google Scholar
    • Export Citation
  • Sridhar, A., and Coauthors, 2022: Large-eddy simulations with ClimateMachine v0.2.0: A new open-source code for atmospheric simulations on GPUs and CPUs. Geosci. Model Dev., 15, 62596284, https://doi.org/10.5194/gmd-15-6259-2022.

    • Search Google Scholar
    • Export Citation
  • Straka, J. M., R. B. Wilhelmson, L. J. Wicker, J. R. Anderson, and K. K. Droegemeier, 1993: Numerical solutions of a non-linear density current: A benchmark solution and comparisons. Int. J. Numer. Methods Fluids, 17, 122, https://doi.org/10.1002/fld.1650170103.

    • Search Google Scholar
    • Export Citation
  • Sun, J., and Coauthors, 2015: Review of wave turbulence interactions in the stable atmospheric boundary layer. Rev. Geophys., 53, 956993, https://doi.org/10.1002/2015RG000487.

    • Search Google Scholar
    • Export Citation
  • Taylor, M. A., O. Guba, A. Steyer, P. A. Ullrich, D. M. Hall, and C. Eldrid, 2020: An energy consistent discretization of the nonhydrostatic equations in primitive variables. J. Adv. Model. Earth Syst., 12, e2019MS001783, https://doi.org/10.1029/2019MS001783.

    • Search Google Scholar
    • Export Citation
  • Terpstra, D., H. Jagode, H. You, and J. Dongarra, 2010: Collecting performance data with PAPI-C. Tools for High Performance Computing 2009, M. Müller et al., Eds., Springer, 157–173.

  • Tumolo, G., and L. Bonaventura, 2015: A semi-implicit, semi-Lagrangian discontinuous Galerkin framework for adaptive numerical weather prediction. Quart. J. Roy. Meteor. Soc., 141, 25822601, https://doi.org/10.1002/qj.2544.

    • Search Google Scholar
    • Export Citation
  • Turner, D. D., V. Wulfmeyer, L. K. Berg, and J. H. Schween, 2014: Water vapor turbulence profiles in stationary continental convective mixed layers. J. Geophys. Res. Atmos., 119, 11 15111 165, https://doi.org/10.1002/2014JD022202.

    • Search Google Scholar
    • Export Citation
  • UCAR/NCAR–Computational and Information Systems Laboratory, 2022: Visualization and Analysis Systems Technologies, software version 3.6.1. Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers (VAPOR), accessed 24 August 2023, https://doi.org/doi:10.5065/d6j38qhc.

  • van der Vorst, H. A., 2003: Iterative Krylov Methods for Large Linear Systems. Cambridge University Press, 221 pp.

  • Vos, P. E. J., S. J. Sherwin, and R. M. Kirby, 2010: From h to p efficiently: Implementing finite and spectral/hp element methods to achieve optimal performance for low- and high-order discretisations. J. Comput. Phys., 229, 51615181, https://doi.org/10.1016/j.jcp.2010.03.031.

    • Search Google Scholar
    • Export Citation
  • Wang, J.-W. A., and P. D. Sardeshmukh, 2021: Inconsistent global kinetic energy spectra in reanalyses and models. J. Atmos. Sci., 78, 25892603, https://doi.org/10.1175/JAS-D-20-0294.1.

    • Search Google Scholar
    • Export Citation
  • Waruszewski, M., J. E. Kozdon, L. C. Wilcox, T. H. Gibson, and F. X. Giraldo, 2022: Entropy stable discontinuous Galerkin methods for balance laws in non-conservative form: Applications to the Euler equations with gravity. J. Comput. Phys., 468, 111507, https://doi.org/10.1016/j.jcp.2022.111507.

    • Search Google Scholar
    • Export Citation
  • Wong, M., W. C. Skamarock, P. H. Lauritzen, J. B. Klemp, and R. B. Stull, 2015: Testing of a cell-integrated semi-Lagrangian semi-implicit nonhydrostatic atmospheric solver (CSLAM-NH) with idealized orography. Mon. Wea. Rev., 143, 13821398, https://doi.org/10.1175/MWR-D-14-00059.1.

    • Search Google Scholar
    • Export Citation
  • Wulfmeyer, W., S. K. Muppa, A. Behrendt, E. Hammann, F. Späth, Z. Sorbjan, D. D. Turner, and R. M. Hardesty, 2016: Determination of convective boundary layer entrainment fluxes, dissipation rates, and the molecular destruction of variances: Theoretical description and a strategy for its confirmation with a novel lidar system synergy. J. Atmos. Sci., 73, 667692, https://doi.org/10.1175/JAS-D-14-0392.1.

    • Search Google Scholar
    • Export Citation
  • Yamazaki, H., T. Satomura, and N. Nikiforakis, 2016: Three-dimensional cut-cell modelling for high-resolution atmospheric simulations. Quart. J. Roy. Meteor. Soc., 142, 13351350, https://doi.org/10.1002/qj.2736.

    • Search Google Scholar
    • Export Citation
  • Yu, Y. G., N. Wang, J. Middlecoff, P. Peixoto, and M. W. Govett, 2020: Comparing numerical accuracy of icosahedral A-grid and C-grid schemes in solving the shallow-water model. Mon. Wea. Rev., 148, 40094033, https://doi.org/10.1175/MWR-D-20-0024.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., J. Hemperly, N. Meskhidze, and W. C. Skamarock, 2012: The Global Weather Research and Forecasting Model: Model evaluation, sensitivity study, and future year simulation. Atmos. Climate Sci., 2, 231253, https://doi.org/10.4236/acs.2012.23024.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 646 646 11
Full Text Views 97 97 11
PDF Downloads 80 80 11