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Change","sub_category":"Earth and Climate Modeling","monthly_downloads":149,"total_dependent_repos":2,"total_dependent_packages":0,"readme":"METSIM: Meteorology Simulator\n=============================\n| MetSim Links \u0026 Badges              |                                                                             |\n|------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| JOSS Paper             | [![DOI](https://joss.theoj.org/papers/10.21105/joss.02042/status.svg)](https://doi.org/10.21105/joss.02042) |\n| MetSim Documentation      | [![Documentation Status](http://readthedocs.org/projects/metsim/badge/?version=develop)](http://metsim.readthedocs.io/en/develop/?badge=develop) |\n| Travis-CI Build           | [![Build Status](https://travis-ci.org/UW-Hydro/MetSim.png)](https://travis-ci.org/UW-Hydro/MetSim) |\n| License                | [![GitHub license](https://img.shields.io/badge/license-GPLv3-blue.svg)](https://raw.githubusercontent.com/UW-Hydro/MetSim/master/LICENSE) |\n| Current Release DOI    | [![DOI](https://zenodo.org/badge/69834400.svg)](https://zenodo.org/badge/latestdoi/69834400) |\n| MetSim Tutorial        | https://github.com/UW-Hydro/MetSim-tutorial |\n\nMetSim is a meteorological simulator and forcing disaggregator for\nhydrologic modeling and climate applications. Metsim is based on\n[MtClim](http://www.ntsg.umt.edu/project/mtclim)\nand the preprocessor from version 4 of the [VIC hydrologic\nmodel](https://github.com/UW-Hydro/VIC).\n\nMetSim consists of 3 main modules that govern the operation of 3 major\naspects of its operation:\n\n**1. Management of dataset preprocessing and IO**\n\nThe MetSim object provides high level support for setting up jobs and\ninfrastructure for running simulation/disaggregation steps. It is the\nmain interface through which the other modules are accessed.\n\n**2. Simulation of daily meteorological forcings**\n\nThe base implementation of the meteorological simulator is based off of\nthe algorithms described in[1]. This component has been designed to be\nflexible in allowing for alternative implementations which may be\nspecified during the setup of the MetSim object. The default\nimplementation allows for the daily simulation of:\n\n-   Mean daily temperature\n-   Incoming shortwave radiation\n-   Cloud cover fraction\n-   Potential evapotranspiration\n-   Vapor pressure\n\nFor the \"triangle\" and \"mix\" methods of precipitation disaggregation,\ndoumentation can be found [here](https://github.com/UW-Hydro/MetSim/blob/develop/docs/PtriangleMethod.pdf).\nThis will eventually\nbe superceded by a journal article that is currently in review [7].\n\n**3. Disaggregation of daily simulation values to sub-daily timesteps**\n\nDaily data from given input or simulated via the forcings generation\ncomponent of MetSim can be disaggregated down to sub-daily values at\nintervals specified in minutes (provided they divide evenly into 24\nhours). The operation of these algorithms is also described in [1].\nThe variables estimated are:\n\n-  Temperature\n-  Vapor pressure\n-  Relative and specific humidity\n-  Air pressure\n-  Cloud cover fraction\n-  Longwave radiation\n-  Shortwave radiation\n-  Precipitation\n-  Wind speed\n\nGetting Started\n===============\nA tutorial for running MetSim and working with input/output data can be run\nvia binder here: https://github.com/UW-Hydro/MetSim-tutorial\n\nInstallation\n------------\n\nMetSim itself is a pure Python package, but its dependencies are not.\nYou should ensure that you have all of the required dependencies:\n\n-   Python 3.5 or greater\n-   [xarray](http://xarray.pydata.org/) (0.10.9 or later)\n-   [pandas](http://pandas.pydata.org/) (0.19.0 or later)\n-   [numba](http://numba.pydata.org/) (0.31.0 or later)\n-   [netCDF4](https://github.com/Unidata/netcdf4-python)\n-   [scipy](http://scipy.org/)\n\nThen, install MetSim with pip or conda:\n\n    $ pip install metsim\n\nor\n\n    $ conda install -c conda-forge metsim\n\nAlternatively, you can install MetSim directly from the source if you desire to:\n\n    $ git clone https://github.com/UW-Hydro/MetSim.git\n    $ cd MetSim\n    $ python setup.py install\n    \nIf you are installing from source you may wish to also run the tests. \nYou can do this from the MetSim directory with the command:\n\n    $ pytest --verbose\n\nBasic Usage\n-----------\n\nMetSim provides a simple command line interface which is primarily\noperated via configuration files. For more information about the options\navailable to be set in the configuration files see the configuration\npage in the full [documentation](http://metsim.readthedocs.io/en/develop/).\n\n\nOnce installed, MetSim can be used from the command line via:\n\n`ms /path/to/configuration [-v] [-n #]`\n\nBracketed flags are optional; `-v` activates verbose mode to print\nmessages about the status of a run, and `-n` activates parallelism. The\nnumber given after the `-n` flag is the number of processes to run. A\ngood rule of thumb is to use one less process than the number of\nprocesssors (or threads) that the machine you are running on has.\n\n:exclamation: Users in environments where OpenMP is available may experience\nover-utilization of CPU resources, leading to lower performance. If you experience\nthis issue try setting the `OMP_NUM_THREADS` environment variable to 1 before running\nMetSim.. This can be done in bash and similar shells by running\n`export OMP_NUM_THREADS=1`.\n\nCiting MetSim\n=============\nIf you use MetSim in your work and would like to cite it you can use our JOSS paper as:\n\n \u003e Bennett et al., (2020). MetSim: A Python package for estimation and disaggregation of meteorological data. Journal of Open Source Software, 5(47), 2042, https://doi.org/10.21105/joss.02042\n\nOr in BibTeX:\n``` \n @article{Bennett2020,\n  doi = {10.21105/joss.02042},\n  url = {https://doi.org/10.21105/joss.02042},\n  year = {2020},\n  publisher = {The Open Journal},\n  volume = {5},\n  number = {47},\n  pages = {2042},\n  author = {Andrew Bennett and Joseph Hamman and Bart Nijssen},\n  title = {MetSim: A Python package for estimation and disaggregation of meteorological data},\n  journal = {Journal of Open Source Software}\n}\n```\n\nAcknowledgements\n================\nMetSim has greatly benefited from the user community, who have contributed code, tested features, provided feedback, and helped with documentation.\nWe would like to thank and acknowledge the work of Ted Bohn, Andy Wood, Kristen Whitney, Yifan Cheng, Liz Clark, Oriana Chegwidden, Ethan Gutmann, Kostas Andreadis, Thomas Remke, Ed Maurer, and Philipp Sommer for their help.\n\n\nReferences\n==========\n\n[1]: Bohn, T. J., B. Livneh, J. W. Oyler, S. W. Running, B. Nijssen,\n    and D. P. Lettenmaier, 2013a: Global evaluation of MTCLIM and\n    related algorithms for forcing of ecological and hydrological\n    models, Agr. Forest. Meteorol., 176, 38-49,\n    \u003cdoi:10.1016/j.agrformet.2013.03.003\u003e.\n\n[2]: Bristow, K.L., and G.S. Campbell, 1984. On the relationship between\n    incoming solar radiation and daily maximum and minimum temperature.\n    Agricultural and Forest Meteorology, 31:159-166.\n\n[3]: Running, S.W., R.R. Nemani, and R.D. Hungerford, 1987. Extrapolation of\n    synoptic meteorological data in mountainous terrain and its use for\n    simulating forest evaporation and photosynthesis. Canadian Journal of\n    Forest Research, 17:472-483.\n\n[4]: Glassy, J.M., and S.W. Running, 1994. Validating diurnal climatology of\n    the MT-CLIM model across a climatic gradient in Oregon. Ecological\n    Applications, 4(2):248-257.\n\n[5]: Kimball, J.S., S.W. Running, and R. Nemani, 1997. An improved method for\n    estimating surface humidity from daily minimum temperature. Agricultural\n    and Forest Meteorology, 85:87-98.\n\n[6]: Thornton, P.E., and S.W. Running, 1999. An improved algorithm for\n    estimating incident daily solar radiation from measurements of\n    temperature, humidity, and precipitation. Agricultural and Forest\n    Meteorology, 93:211-228.\n\n[7]: Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019. A\n    deterministic approach for approximating the diurnal cycle of\n    precipitation for large-scale hydrological simulations. 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