| MetSim Links & Badges | |
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| MetSim Documentation | |
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| MetSim Tutorial | https://github.com/UW-Hydro/MetSim-tutorial |
MetSim is a meteorological simulator and forcing disaggregator for hydrologic modeling and climate applications. Metsim is based on MtClim and the preprocessor from version 4 of the VIC hydrologic model.
MetSim consists of 3 main modules that govern the operation of 3 major aspects of its operation:
1. Management of dataset preprocessing and IO
The MetSim object provides high level support for setting up jobs and infrastructure for running simulation/disaggregation steps. It is the main interface through which the other modules are accessed.
2. Simulation of daily meteorological forcings
The base implementation of the meteorological simulator is based off of the algorithms described in[1]. This component has been designed to be flexible in allowing for alternative implementations which may be specified during the setup of the MetSim object. The default implementation allows for the daily simulation of:
- Mean daily temperature
- Incoming shortwave radiation
- Cloud cover fraction
- Potential evapotranspiration
- Vapor pressure
For the "triangle" and "mix" methods of precipitation disaggregation, doumentation can be found here. This will eventually be superceded by a journal article that is currently in review [7].
3. Disaggregation of daily simulation values to sub-daily timesteps
Daily data from given input or simulated via the forcings generation component of MetSim can be disaggregated down to sub-daily values at intervals specified in minutes (provided they divide evenly into 24 hours). The operation of these algorithms is also described in [1]. The variables estimated are:
- Temperature
- Vapor pressure
- Relative and specific humidity
- Air pressure
- Cloud cover fraction
- Longwave radiation
- Shortwave radiation
- Precipitation
- Wind speed
A tutorial for running MetSim and working with input/output data can be run via binder here: https://github.com/UW-Hydro/MetSim-tutorial
MetSim itself is a pure Python package, but its dependencies are not. You should ensure that you have all of the required dependencies:
- Python 3.5 or greater
- xarray (0.10.9 or later)
- pandas (0.19.0 or later)
- numba (0.31.0 or later)
- netCDF4
- scipy
Then, install MetSim with pip or conda:
$ pip install metsim
or
$ conda install -c conda-forge metsim
Alternatively, you can install MetSim directly from the source if you desire to:
$ git clone https://github.com/UW-Hydro/MetSim.git
$ cd MetSim
$ python setup.py install
If you are installing from source you may wish to also run the tests. You can do this from the MetSim directory with the command:
$ pytest --verbose
MetSim provides a simple command line interface which is primarily operated via configuration files. For more information about the options available to be set in the configuration files see the configuration page in the full documentation.
Once installed, MetSim can be used from the command line via:
ms /path/to/configuration [-v] [-n #]
Bracketed flags are optional; -v activates verbose mode to print
messages about the status of a run, and -n activates parallelism. The
number given after the -n flag is the number of processes to run. A
good rule of thumb is to use one less process than the number of
processsors (or threads) that the machine you are running on has.
❗ Users in environments where OpenMP is available may experience
over-utilization of CPU resources, leading to lower performance. If you experience
this issue try setting the OMP_NUM_THREADS environment variable to 1 before running
MetSim.. This can be done in bash and similar shells by running
export OMP_NUM_THREADS=1.
If you use MetSim in your work and would like to cite it you can use our JOSS paper as:
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
Or in BibTeX:
@article{Bennett2020,
doi = {10.21105/joss.02042},
url = {https://doi.org/10.21105/joss.02042},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {47},
pages = {2042},
author = {Andrew Bennett and Joseph Hamman and Bart Nijssen},
title = {MetSim: A Python package for estimation and disaggregation of meteorological data},
journal = {Journal of Open Source Software}
}
[1]: Bohn, T. J., B. Livneh, J. W. Oyler, S. W. Running, B. Nijssen, and D. P. Lettenmaier, 2013a: Global evaluation of MTCLIM and related algorithms for forcing of ecological and hydrological models, Agr. Forest. Meteorol., 176, 38-49, doi:10.1016/j.agrformet.2013.03.003.
[2]: Bristow, K.L., and G.S. Campbell, 1984. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology, 31:159-166.
[3]: Running, S.W., R.R. Nemani, and R.D. Hungerford, 1987. Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evaporation and photosynthesis. Canadian Journal of Forest Research, 17:472-483.
[4]: Glassy, J.M., and S.W. Running, 1994. Validating diurnal climatology of the MT-CLIM model across a climatic gradient in Oregon. Ecological Applications, 4(2):248-257.
[5]: Kimball, J.S., S.W. Running, and R. Nemani, 1997. An improved method for estimating surface humidity from daily minimum temperature. Agricultural and Forest Meteorology, 85:87-98.
[6]: Thornton, P.E., and S.W. Running, 1999. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology, 93:211-228.
[7]: Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019. A deterministic approach for approximating the diurnal cycle of precipitation for large-scale hydrological simulations. Journal of Hydrometeorology (accepted). doi: 10.1175/JHM-D-18-0203.1.