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Composition and Dynamics","monthly_downloads":15802,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# SpeedyWeather.jl \u003cimg src=\"docs/src/assets/logo.png\" width=\"100\" /\u003e\n\n[![CI](https://github.com/SpeedyWeather/SpeedyWeather.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/SpeedyWeather/SpeedyWeather.jl/actions/workflows/CI.yml) \n[![status](https://joss.theoj.org/papers/515c81a4d6a69e31cc71ded65ac9c36a/status.svg)](https://joss.theoj.org/papers/515c81a4d6a69e31cc71ded65ac9c36a)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6510139.svg)](https://doi.org/10.5281/zenodo.6510139)  \n[![docs](https://img.shields.io/badge/documentation-latest_release-blue.svg)](https://speedyweather.github.io/SpeedyWeatherDocumentation/stable/)\n[![docs](https://img.shields.io/badge/documentation-main-blue.svg)](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/)\n[![code style: runic](https://img.shields.io/badge/code_style-%E1%9A%B1%E1%9A%A2%E1%9A%BE%E1%9B%81%E1%9A%B2-black)](https://github.com/fredrikekre/Runic.jl)\n\nSpeedyWeather.jl is a global atmospheric model developed as a research playground\nwith an everything-flexible attitude as long as it is speedy. Technically it is a climate model with simple,\nyet interactive representations of ocean, land and sea-ice. It is easy to use and easy to extend, making \natmospheric modelling an interactive experience -- in the terminal, in a notebook or conventionally through scripts.\nWith minimal code redundancies, it supports\n\n**Dynamics and physics**\n- Different physical equations (barotropic vorticity, shallow water, primitive equations, with and without humidity)\n- Particle advection in 2D for all equations\n- Tracer advection in 2D/3D that can be added, deleted, (de)activated anytime\n- Physics parameterizations for convection, precipitation, boundary layer, radiation, etc.\n- Various more or less realistic planets and what-if scenarios by easily modifying initial and boundary conditions\n- A slab ocean and thermodynamic sea ice model\n- A 2-layer land bucket model with soil temperature, moisture, vegetation and snow\n\n**Numerics and computing**\n- Different spatial grids (full and octahedral, Gaussian and Clenshaw-Curtis, HEALPix, OctaHEALPix)\n- Different resolutions (T31 to T1023 and higher, i.e. 400km to 10km using linear, quadratic or cubic truncation)\n- Different arithmetics: Float32 (default), Float64, and (experimental) BFloat16, stochastic rounding\n- A very fast and flexible spherical harmonics transform library SpeedyTransforms\n\n**User interface**\n- Data visualisation: 2D, 3D, interactive (you can zoom and rotate!) powered by Makie\n- Extensibility: New model components (incl. parameterizations) can be externally defined\n- Modularity: Models are constructed from its components, non-defaults are passed on as argument\n- Interactivity: SpeedyWeather.jl runs in a notebook or in the REPL as well as from scripts\n- Callbacks can be used to inject any piece of code after every time step, e.g. custom output, event handling, changing the model while it's running\n\nand Julia will compile to these choices just-in-time.\n\nFor an overview of the functionality and explanation see the\n[documentation](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev).\nYou are always encouraged to [raise an issue](https://github.com/SpeedyWeather/SpeedyWeather.jl/issues)\n(even it is not actually an issue but an idea, a suggestion or really anything)\ndescribing what you'd like to use SpeedyWeather for. We're keen to help!\n\n## Vision and roadmap\n\nWhy another model? You may ask. We believe that most currently available are stiff, difficult to use\nand extend, and therefore slow down research whereas a modern code in a modern language wouldn't have to.\nWe decided to use Julia because it combines the best of Fortran and Python: Within a single language\nwe can interactively run SpeedyWeather but also extend it, inspect its components, evaluate\nindividual terms of the equations, and analyse and visualise output on the fly.\n\nWe do not necessarily aim to make SpeedyWeather an atmospheric model for the purpose of production-ready\nweather forecasting, at least not at the cost of our current level of interactivity and ease of\nuse or extensibility. It is more build with a climate application scope in mind.\nIf someone wants to implement a parameterization that is very complicated and computationally expensive,\nthen they are more than encouraged to do so. We are happy to provide a general interface\nto enable and support such extensions, which can even be developed as their own independent projects in separate repositories\nthanks to the modular structure of SpeedyWeather. Since SpeedyWeather follows an \"easy and fast by default\" philosophy, these\ncomplex extensions likely will not be used by default. We would nevertheless appreciate such efforts since we aim for SpeedyWeather\nto enable a \"hierarchy of models\" spanning multiple levels process and computational complexity and runnable on hardware ranging from\na single laptop to a large HPC cluster. It is important, however, that SpeedyWeather's defaults be balanced: physically accurate yet\nfast enough to run on a laptop; comprehensive enough to simulate realistic weather yet understandable for newcomers; holistic enough\nto provide an easy-to-use interface yet modular enough to allow for extensive customization.\n\nFinding a good balance is difficult, but we try our best.\n\nDespite our commitment to simplicity and ease-of-use, many more physical processes and other features are continuing to be developed.\nOn our TODO list are\n\n- Radiation depending on greenhouse gas concentrations to represent climate change\n- Exoplanet support with more flexibility on the atmospheric composition\n- 3D particle advection\n\nCurrently in development are\n\n- Single GPU and CPU multi-threading support via KernelAbstractions\n- Differentiability with Enzyme\n\n## Contributing\n\nOpen-source lives from large teams of (even occasional) contributors. If you are interested to\nfix something, implement something, or just use it and provide feedback you are always welcome.\nWe are more than happy to guide you, especially when you don't know where to start.\nWe can point you to the respective code, highlight how everything is connected and tell you\nabout dos and don'ts. Just express your interest to contribute and we'll be happy to have you.\n\n## Example use\n\nFor a more comprehensive tutorial with several examples, see\n[Examples](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/examples_2D/) in the documentation.\nThe basic interface to SpeedyWeather.jl consist of 4 steps: define the grid,\nconstruct the model, initialize, run\n\n```julia\nspectral_grid = SpectralGrid(trunc=31, nlayers=8)   # define resolution\nmodel = PrimitiveWetModel(spectral_grid)            # construct model\nsimulation = initialize!(model)                     # initialize all model components\nrun!(simulation, period=Day(10), output=true)       # aaaand action!\n```\nshowing\n```\nWeather is speedy: run 0001 100%|█████████| Time: 0:00:02 (1000.22 years/day)\n```\n\nHurray🥳 In a few seconds seconds we just simulated 10 days of the Earth's atmosphere at a speed of 1000 years per day.\nThis simulation used a T31 spectral resolution on an\n[octahedral Gaussian grid](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/grids/#Implemented-grids)\n(~400km resolution) solving the primitive equations on 8 vertical levels,\nstoring [NetCDF output](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/output/).\n\nMore examples in the [How to run SpeedyWeather](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/how_to_run_speedy/)\nsection of the [documentation](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev).\n\n## Interactive use\n\nYou can use SpeedyWeather from the [Julia REPL](https://docs.julialang.org/en/v1/stdlib/REPL/), running a script\nlike `julia speedyweather.jl`, or interactively through [Jupyter](https://jupyter.org/try) (install a Julia kernel\nwith [IJulia](https://github.com/JuliaLang/IJulia.jl)) or [Pluto notebooks](https://plutojl.org/).\nHere an example for a Pluto notebook, changing interactively resolution and switching off\nconvection and surface humidity fluxes, automatically updating a plot of near-surface\nhumidity\n\n![speedyweather_pluto](https://github.com/user-attachments/assets/5d485015-8c28-4c44-9c2b-c3acdf335788)\n\n\n\n## Gallery\n\nSpecific humidity in the primitive equation model simulated at T340 spectral resolution (about 40km)\nwith 16 vertical levels (shown here is level 15, just above the surface) on the octahedral Gaussian grid\ncomputed in single precision multi-threaded on 16 CPUs. With convection, large-scale condensation, surface fluxes\nand some simplified radiation (the daily cycle is visible)\n\nhttps://github.com/SpeedyWeather/SpeedyWeather.jl/assets/25530332/614f04cf-5080-4c89-9fd6-35efd54103a7\n\nRelative vorticity in the shallow water model, simulated at T1023 spectral resolution (about 10km) on an\n[octahedral Clenshaw-Curtis grid](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/grids/#Implemented-grids)\nwith more than 4 million grid points\n\nhttps://user-images.githubusercontent.com/25530332/190443050-d5b8d093-86c0-46c9-b515-8420059ac8dc.mp4\n\nSurface temperature in the primitive equation model without surface fluxes or radiation\nat T511 (~20km resolution) and 31 vertical levels. The simulation was multi-threaded in Float32 (single precision).\n\nhttps://github.com/SpeedyWeather/SpeedyWeather.jl/assets/25530332/95897b82-9b81-4980-934b-cfdcf4d5a4b0\n\nSpeedyWeather.jl can also solve the 2D barotropic vorticity equations on the sphere.\nHere, we use Float32 (single precision) at a resolution of T340 (40km) on\nan [octahedral Gaussian grid](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/grids/#Implemented-grids).\nForcing is a stochastic stirring on northern hemisphere mid-latitudes following Barnes and Hartmann, 2011.\nMap projection is orthographic centred on the north pole.\n\nhttps://github.com/SpeedyWeather/SpeedyWeather.jl/assets/25530332/3d7fccd5-b66d-42e3-9f73-64dcf21d00ee\n\nHere, SpeedyWeather.jl simulates atmospheric gravity waves, initialised randomly interacting with orography\nover a period of 2 days. Each frame is one time step, solved with a centred semi-implicit scheme that\nresolves gravity waves with a timestep of CFL=1.2-1.4 despite a single-stage RAW-filtered leapfrog integration.\n\nhttps://github.com/SpeedyWeather/SpeedyWeather.jl/assets/25530332/510c38c7-12cb-42d5-b905-c66b4eaa514d\n\nAdvection of 5000 particles with wind in the lower-most layer of the primitive equation model at\nT85 (150km) resolution and 8 vertical layers.\n\nhttps://github.com/SpeedyWeather/SpeedyWeather.jl/assets/25530332/a6192374-24d9-4065-9fcc-8b719190472f\n\n## Data visualisation\n\nDifficult to plot spherical data? SpeedyWeather also includes extensions for Makie and GeoMakie\nmaking it supereasy to create plots and interactively investigate a variables from a simulation.\nTwo examples, a variable from a simulation\n\nhttps://github.com/user-attachments/assets/543cddf8-bcd9-41a7-83e3-184dba994069\n\nor visualising cell centres and faces of the HEALPixGrid via our `globe` function\n\nhttps://github.com/user-attachments/assets/fa0636e3-30d7-4fbe-af29-dba047e8bded\n\n## History\n\nSpeedyWeather.jl started off as a reinvention of the atmospheric general circulation model\n[SPEEDY](http://users.ictp.it/~kucharsk/speedy-net.html) in Julia.\nSpeedyWeather has a different philosophy except for the simplicty of some parameterizations\nand many features have been added or changed so that only some of the numerical\nschemes share similarities, but so do many global spectral models.\nFortran SPEEDY's dynamical core has an obscure history:\nLarge parts were written by Isaac Held at GFDL in/before the 90ies with an unknown amount of\ncontributions/modifications from Steve Jewson (Oxford) in the 90ies.\nThe physical parametrizations were then added by Franco Molteni, Fred Kucharski, and Martin P. King\nafterwards while the model was still written in Fortran77.\nAround 2018-19, SPEEDY was then translated to Fortran90 by Sam Hatfield in [speedy.f90](https://github.com/samhatfield/speedy.f90).\nSpeedyWeather.jl is then adopted from [first translations to Julia](https://github.com/samhatfield/speedy.jl) by Sam Hatfield.\nAll these past creators and contributors are strongly acknowledged. Thank you for letting us use your efforts to create\nintermediate-complexity climate models as a conceptual launchpad for SpeedyWeather!\n\n## Submodules\n\nSpeedyWeather.jl defines several modules that are part of this repository, but also available to be used and installed as stand-alone packages: \n\n- [__RingGrids__](https://speedyweather.github.io/SpeedyWeather.jl/dev/ringgrids/),\na module that defines several iso-latitude ring-based spherical grids (like the FullGaussianGrid or the HEALPixGrid)\nand interpolations between them\n- [__LowerTriangularArrays__](https://speedyweather.github.io/SpeedyWeather.jl/dev/lowertriangularmatrices/),\na module that defines `LowerTriangularMatrix` used for the spherical harmonic coefficients\n- [__SpeedyTransforms__](https://speedyweather.github.io/SpeedyWeather.jl/dev/speedytransforms/), a module that defines\nthe spherical harmonic transform between spectral space (for which LowerTriangularArrays is used) and grid-point space\n(as defined by RingGrids).\n\nThese modules can also be used independently of SpeedyWeather. They are registered, so just install and use them as every other package, e.g:\n```julia\n(@v1.11) pkg\u003e add RingGrids\njulia\u003e using RingGrids\n```\ncheck out their documentation: [RingGrids](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/ringgrids/),\n[LowerTriangularArrays](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/lowertriangularmatrices/),\n[SpeedyTransforms](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/speedytransforms/).\n\n## Installation\n\nSpeedyWeather.jl is registered in Julia's registry, so open the package manager with `]` and\n```julia\n(@v1.11) pkg\u003e add SpeedyWeather\n```\nwhich will install the [latest release]([url](https://github.com/SpeedyWeather/SpeedyWeather.jl/releases))\nand all dependencies automatically. For more information see the\n[Installation](https://speedyweather.github.io/SpeedyWeatherDocumentation/dev/installation/) in the documentation.\nPlease use the current minor version of Julia,\ncompatibilities with older versions are not guaranteed.\n\n## Benchmarks\n\nThe primitive equations at 400km resolution with 8 vertical layers can be simulated by\nSpeedyWeather.jl at 2300 simulated years per day (SYPD) on a single core of newer CPUs with arm architecture\n(M4 MacBooks in this case). At that speed, simulating one year takes about 40 seconds\nwithout output. The complex fused-multiply adds of the spectral transform compile efficiently to\nthe large vector extensions of the newer arm chips in single precision.\nAnother considerable speed-up comes from the reduced grids minimizing the number of columns for\nwhich expensive parameterizations like convection have to be computed. The parameterizations\ntake up 40-60% of the total simulation time, depending on the grid. Particularly the\n`OctaminimalGaussianGrid`, `OctaHEALPixGrid` and the `HEALPixGrid` are increasingly faster,\nat a small accuracy sacrifice of the then inexact spectral transforms. \n\nOn older CPUs, like the Intel CPU MacBooks, the speed drops to about 500-600 SYPD,\nwhich is still 2x faster than Fortran SPEEDY which is reported to reach 240 SYPD.\nWith GPU support being a work in progress we do not expect a performance increase for \nthe lower resolutions but aim to provide the ability to also run the model very efficiently\nat high resolution!\n\nFor an overview of typical simulation speeds a user can expect under different model setups see\n[Benchmarks](https://github.com/SpeedyWeather/SpeedyWeather.jl/blob/main/benchmark).\n\n## Citing\n\nIf you use SpeedyWeather.jl in research, teaching, or other activities, we would be grateful \nif you could mention SpeedyWeather.jl and cite our paper in JOSS:\n\n\u003e Klöwer et al., (2024). SpeedyWeather.jl: Reinventing atmospheric general circulation models towards interactivity and extensibility. Journal of Open Source Software, 9(98), 6323, doi:[10.21105/joss.06323](https://doi.org/10.21105/joss.06323).\n\nThe bibtex entry for the paper is:\n\n```bibtex\n@article{SpeedyWeatherJOSS,\n    doi = {10.21105/joss.06323},\n    url = {https://doi.org/10.21105/joss.06323},\n    year = {2024},\n    publisher = {The Open Journal},\n    volume = {9},\n    number = {98},\n    pages = {6323},\n    author = {Milan Klöwer and Maximilian Gelbrecht and Daisuke Hotta and Justin Willmert and Simone Silvestri and Gregory L. Wagner and Alistair White and Sam Hatfield and Tom Kimpson and Navid C. Constantinou and Chris Hill},\n    title = {{SpeedyWeather.jl: Reinventing atmospheric general circulation models towards interactivity and extensibility}},\n    journal = {Journal of Open Source Software}\n}\n```\n\n## Copyright and license\n\nCopyright (c) 2020 Milan Klöwer for SpeedyWeather.jl  \nCopyright (c) 2021 The SpeedyWeather.jl Contributors for SpeedyWeather.jl  \nCopyright (c) 2022 Fred Kucharski and Franco Molteni for SPEEDY parametrization schemes  \n\nSoftware licensed under the [MIT License](LICENSE.txt).\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.6510139","https://doi.org/10.21105/joss.06323"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["climate","differential-equations","climate-change","data-assimilation","ocean","differentialequations","ode","sciml","oceanography","meteorology"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/125733","html_url":"https://ost.ecosyste.ms/projects/125733"}