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Ice","monthly_downloads":2543,"total_dependent_repos":1,"total_dependent_packages":0,"readme":"# openAMUNDSEN\n\nopenAMUNDSEN is a modular snow and hydroclimatological modeling framework written in Python.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/17546246/115751189-3afe4c00-a399-11eb-8bfa-87d0a86c2119.gif\" /\u003e\n\u003c/p\u003e\n\n## Overview\n\nopenAMUNDSEN is a fully distributed model, designed primarily for resolving the mass and energy\nbalance of snow and ice covered surfaces in mountain regions.\nTypically, it is applied in areas ranging from the point scale to the regional scale (i.e., up to\nsome hundreds to thousands of square kilometers), using a spatial resolution of 10–100 m and a\ntemporal resolution of 1–3 h, however its potential applications are very versatile. Additional documentation can be found at [doc.openamundsen.org](https://doc.openamundsen.org/).\n\nMain features include:\n\n* Spatial interpolation of scattered meteorological point measurements using a combined lapse\n  rate – inverse distance weighting scheme\n* Calculation of solar radiation taking into account terrain slope and orientation, hill shading\n  and atmospheric transmission losses and gains due to scattering, absorption, and reflections\n* Adjustment of precipitation using several correction functions for wind-induced undercatch and\n  redistribution of snow using terrain-based parameterizations\n* Simulation of the snow and ice mass and energy balance using either a multilayer scheme or a\n  bulk-layering scheme using separate layers for new snow, old snow, firn and ice\n* Modification of the meteorological variables for inside-canopy conditions in forested areas and\n  calculation of forest snow processes (interception, sublimation and melt unload)\n* Calculation of snowmelt using the surface energy balance or a temperature index/enhanced\n  temperature index method\n* Calculation of evapotranspiration for snow-free surfaces using the FAO Penman-Monteith method\n* Usage of arbitrary timesteps (e.g. 10 minutes, daily) while resampling forcing data to the\n  desired time resolution if necessary\n* Flexible output of time series including arbitrary model variables for selected point locations in\n  NetCDF or CSV format\n* Flexible output of gridded model variables, either for specific dates or periodically (e.g., daily\n  or monthly), optionally aggregated to averages or sums in NetCDF, GeoTIFF or ASCII Grid format\n* Live view window for displaying the model state in real time\n\n## Quick start\n\n### Installation\n\nopenAMUNDSEN is a Python package and compatible with all major platforms (Linux, macOS,\nWindows) and architectures.\n\nTo help keep its dependencies separated from other Python packages installed on your system, we\nrecommend to install it either from within a conda environment (if you are using the\n[conda](https://docs.conda.io/en/latest/) package manager) or a standard Python [virtual\nenvironment](https://docs.python.org/3/tutorial/venv.html).\n\n#### Using conda\n\nWhen using conda, the recommended steps to install openAMUNDSEN are:\n\n1. Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) (recommended) or\n   [Anaconda](https://www.anaconda.com/products/individual#Downloads) by downloading and executing\n   the installer for your operating system and architecture.\n2. From the terminal, create a conda environment for openAMUNDSEN by running\n\n   `conda create --name openamundsen`\n3. Activate the environment by running\n\n   `conda activate openamundsen`\n4. Install openAMUNDSEN by running\n\n   `conda install --channel=conda-forge openamundsen`\n\n#### Using virtualenv\n\nIf you want to install openAMUNDSEN in a virtual environment instead:\n\n1. Create a virtualenv in the current working directory by running\n\n   `python3 -m venv openamundsen`\n\n2. Activate the environment by running\n\n   `source openamundsen/bin/activate`\n\n3. Install openAMUNDSEN by running\n\n   `pip install openamundsen`\n\n### Examples\n\nExample data sets for running the model can be downloaded from\nhttps://github.com/openamundsen/openamundsen-examples.\n\n## Setting up a model run\n\n### Input data\n\nRequired input data for running the model is at the least:\n\n* a digital elevation model (DEM) as an [Arc/Info ASCII\n  Grid](https://en.wikipedia.org/wiki/Esri_grid) (.asc) file in a projected coordinate reference\n  system, with the same spatial resolution in which the model should be run,\n* and time series of the meteorological variables air temperature, precipitation, relative humidity,\n  global radiation and wind speed in NetCDF or CSV format.\n\nOptionally, a region of interest (ROI) file can be additionally supplied defining a subset of the\nDEM area in which the model should be applied.\nAll model calculations are then only performed for the pixels which are marked as 1 in the ROI file.\n\n#### Spatial input data\n\nThe DEM file must be named `dem_{domain}_{resolution}.asc`, where `{domain}` refers to the (freely\nselectable) name of the respective model domain, and `{resolution}` to the spatial resolution in m.\nAccordingly, the ROI file (if available) is named `roi_{domain}_{resolution}.asc`.\n\n#### Meteorological input data\n\nMeteorological input time series must be provided in the same or higher temporal resolution in which\nthe model should be run.\nFor each point location, a CSV or NetCDF file covering the entire time series must be provided.\n\n##### CSV input\n\nWhen using CSV as input format, the input files should have one or more of the following columns\n(columns for variables not available can be omitted):\n\n* `date`: timestamp as a\n  [`pd.to_datetime`](https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html)-compatible\n  string (e.g. `YYYY-MM-DD HH:MM`)\n* `temp`: air temperature (K)\n* `precip`: precipitation sum (kg m\u003csup\u003e-2\u003c/sup\u003e)\n* `rel_hum`: relative humidity (%)\n* `sw_in`: global radiation (W m\u003csup\u003e-2\u003c/sup\u003e)\n* `wind_speed`: wind speed (m s\u003csup\u003e-1\u003c/sup\u003e)\n\nAdditionally, a `stations.csv` file containing the metadata of the point locations must be provided\ncontaining the following columns:\n\n* `id`: station ID, corresponding to the filename of the respective data file\n* `name`: station name\n* `x`: longitude or projected x coordinate\n* `y`: latitude or projected y coordinate\n* `alt`: altitude (m)\n\n##### NetCDF input\n\nWhen using NetCDF as input format, for each station a NetCDF file containing the meteorological time\nseries and the station metadata is read in (i.e., no additional metadata file is required in this\ncase).\nThe NetCDF files are expected to conform to the following schema (unavailable variables can be\nomitted):\n\n```\nnetcdf dummy {\ndimensions:\n        time = UNLIMITED ;\nvariables:\n        double alt ;\n                alt:_FillValue = NaN ;\n                alt:standard_name = \"surface_altitude\" ;\n                alt:units = \"m\" ;\n        float hurs(time) ;\n                hurs:_FillValue = NaNf ;\n                hurs:standard_name = \"relative_humidity\" ;\n                hurs:units = \"%\" ;\n        double lat ;\n                lat:_FillValue = NaN ;\n                lat:standard_name = \"latitude\" ;\n                lat:units = \"degree_north\" ;\n        double lon ;\n                lon:_FillValue = NaN ;\n                lon:standard_name = \"longitude\" ;\n                lon:units = \"degree_east\" ;\n        float pr(time) ;\n                pr:_FillValue = NaNf ;\n                pr:standard_name = \"precipitation_flux\" ;\n                pr:units = \"kg m-2 s-1\" ;\n        float rsds(time) ;\n                rsds:_FillValue = NaNf ;\n                rsds:standard_name = \"surface_downwelling_shortwave_flux_in_air\" ;\n                rsds:units = \"W m-2\" ;\n        float tas(time) ;\n                tas:_FillValue = NaNf ;\n                tas:standard_name = \"air_temperature\" ;\n                tas:units = \"K\" ;\n        int64 time(time) ;\n                time:standard_name = \"time\" ;\n                time:units = \"hours since 1999-01-01 00:00:00\" ;\n                time:calendar = \"proleptic_gregorian\" ;\n        float wss(time) ;\n                wss:_FillValue = NaNf ;\n                wss:standard_name = \"wind_speed\" ;\n                wss:units = \"m s-1\" ;\n\n// global attributes:\n                :Conventions = \"CF-1.6\" ;\n                :station_name = \"dummy\" ;\n}\n```\n\n### Configuration\n\nThe configuration of an openAMUNDSEN model run can either be read in from a\n[YAML](https://en.wikipedia.org/wiki/YAML) file or be passed directly as a dictionary from within\nPython.\n\nThis is an example of a YAML configuration file:\n\n```yaml\ndomain: rofental # name of the model domain (corresponding to the domain part of the spatial input data filenames)\nstart_date: \"2020-10-01\"\nend_date: \"2021-03-31\"\nresolution: 50  # spatial resolution (m)\ntimestep: h  # temporal resolution as a pandas-compatible frequency string (e.g., \"h\", \"3h\", \"D\")\ncrs: \"epsg:32632\"  # CRS of the input grids\ntimezone: 1  # timezone of the model domain (difference to UTC in h)\nresults_dir: results  # directory for storing the model outputs\n\n# Input data configuration\ninput_data:\n  grids:\n    dir: input/grid  # location of the input grids (DEM, ROI etc.)\n  meteo:\n    dir: input/meteo  # location of the meteorological input data\n    format: csv  # input format (CSV or NetCDF)\n    crs: \"epsg:4326\"  # CRS of the station coordinates (when using CSV)\n\n# Output data configuration\noutput_data:\n  # Time series (point) outputs configuration\n  timeseries:\n    # List of points to be written\n    points:\n      - x: 642579 # x coordinate in the domain CRS\n        y: 5193069 # y coordinate in the domain CRS\n        name: testpoint # point name (optional)\n\n    add_default_variables: true # write default point output variables\n    variables: # optional additional output variables not written by default\n      - var: surface.turbulent_exchange_coeff\n\n  # Configuration for gridded outputs\n  grids:\n    format: netcdf # \"netcdf\", \"ascii\", \"geotiff\" or \"memory\"\n    layout: grid # \"grid\" (full y/x grid) or \"roi_pixel\" (1D ROI pixels, NetCDF/memory only)\n    variables:\n      - var: meteo.precip # internal variable name\n        name: precip_month # NetCDF output variable name\n        freq: M # write frequency (if not specified, write every timestep)\n        agg: sum # aggregation function (\"sum\", \"mean\" or empty)\n      - var: snow.melt\n        freq: M\n        agg: sum\n      - var: snow.swe\n        freq: D\n\nmeteo:\n  # Spatial interpolation parameters\n  interpolation:\n    temperature:\n      trend_method: fixed # use fixed monthly temperature lapse rates\n\n    precipitation:\n      trend_method: fractional # use fixed monthly fractional precipitation gradients\n      lapse_rate: # (m-1)\n        - 0.00048 # J\n        - 0.00046 # F\n        - 0.00041 # M\n        - 0.00033 # A\n        - 0.00028 # M\n        - 0.00025 # J\n        - 0.00024 # J\n        - 0.00025 # A\n        - 0.00028 # S\n        - 0.00033 # O\n        - 0.00041 # N\n        - 0.00046 # D\n\n    humidity:\n      trend_method: fixed # use fixed monthly dew point temperature lapse rates\n\n  # Precipitation phase determination parameters\n  precipitation_phase:\n    method: wet_bulb_temp # use wet-bulb temperature for precipitation phase determination\n    threshold_temp: 273.65 # threshold temperature (K) in which 50% of precipitation falls as snow\n    temp_range: 1. # temperature range in which mixed precipitation can occur\n\n  # Parameters for adjusting precipitation for wind-induced undercatch and snow redistribution\n  precipitation_correction:\n    - method: wmo\n      gauge: hellmann\n\nsnow:\n  model: multilayer # snow scheme (\"multilayer\" or \"cryolayers\")\n\n  # Number of layers and minimum thicknesses (m) when using the multilayer model\n  min_thickness:\n    - 0.1\n    - 0.2\n    - 0.4\n\n  albedo:\n    min: 0.55 # minimum snow albedo\n    max: 0.85 # maximum snow albedo\n    cold_snow_decay_timescale: 480 # albedo decay timescale for cold (T \u003c 0 °C) snow (h)\n    melting_snow_decay_timescale: 200 # albedo decay timescale for melting snow (h)\n    refresh_snowfall: 0.5 # snowfall amount for resetting albedo to the maximum value (kg m-2 h-1)\n```\n\nOnly few configuration parameters (`domain`, `start_date`, `end_date`, `resolution`, `timezone` and\nthe input data directories) are mandatory, for all other parameters default values are used\notherwise.\nA detailed documentation of all model parameters will be available soon (in\nthe meantime, the available parameters and their default values can be looked up in\n[configschema.yml](./openamundsen/data/configschema.yml)).\n\n### Running the model\n\nWhen the input data and the model configuration have been prepared, a model run can be started either\nusing the `openamundsen` command line utility (`openamundsen config_file.yml`), or from within\nPython using the following syntax:\n\n```python\nimport openamundsen as oa\n\nconfig = oa.read_config('config_file.yml')  # read in configuration file\nmodel = oa.OpenAmundsen(config)  # create OpenAmundsen object and populate unspecified parameters with default values\nmodel.initialize()  # read in input data files, initialize state variables etc.\nmodel.run()  # run the model\n```\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/350833","html_url":"https://ost.ecosyste.ms/projects/350833"}