API reference¶
This page provides an auto-generated summary of metsim’s API. For more details and examples, refer to the relevant chapters in the main part of the documentation.
MetSim¶
The main object of the MetSim package. The MetSim object is used to set up and launch forcing generation and/or disaggregation routines.
The MetSim object uses a class dictionary to refer to the model setup, which can be modified after instantiation if necessary. Before calling run or launch on the instance it is required to call the load function to ensure that all of the required parameters have been set and that the input data is sufficient to provide the output specified.
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class
metsim.metsim.
MetSim
(params: dict)[source]¶ MetSim handles the distribution of jobs that write to a common file by launching muliple processes and queueing up their writeback so that work can be done while IO is happening.
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read
(fpath: str) → xarray.core.dataset.Dataset[source]¶ Dispatch to the right function based on the file extension
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read_binary
(fpath: str) → xarray.core.dataset.Dataset[source]¶ Reads a binary forcing file (VIC 4 format)
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metsim.metsim.
wrap_run
(func: <built-in function callable>, loc: dict, ds: xarray.core.dataset.Dataset, disagg: bool)[source]¶ Iterate over a chunk of the domain. This is wrapped so we can return a tuple of locs and df.
Parameters: - func (callable) – The function to call to do the work
- loc (dict) – Some subset of the domain to do work on
- met_data (xr.Dataset) – Input forcings and domain
- disagg (bool) – Whether or not we should run a disagg routine
Returns: A list of tuples arranged as (location, output)
Return type: results
Physics¶
physics
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metsim.physics.
atm_pres
(elev: float) → float[source]¶ Atmospheric pressure (Pa) as a function of elevation (m)
[1] Iribane, J.V., and W.L. Godson, 1981. Atmospheric Thermodynamics, 2nd Edition. D. Reidel Publishing Company, Dordrecht, The Netherlands (p. 168) Parameters: elev – Elevation in meters Returns: Atmospheric pressure (Pa) Return type: pressure
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metsim.physics.
calc_pet
(rad: pandas.core.series.Series, ta: pandas.core.series.Series, dayl: pandas.core.series.Series, pa: float, dt: float=0.2) → pandas.core.series.Series[source]¶ Calculates the potential evapotranspiration for aridity corrections in calc_vpd(), according to Kimball et al., 1997
Parameters: - rad – daylight average incident shortwave radiation (W/m2)
- ta – daylight average air temperature (deg C)
- dayl – daylength (s)
- pa – air pressure (Pa)
- dt – offset for saturation vapor pressure calculation
Returns: Potential evapotranspiration (cm/day)
Return type: pet
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metsim.physics.
solar_geom
[source]¶ Flat earth assumption
Parameters: - elev – Elevation in meters
- lat – Latitude in decimal format
Returns: (tiny_rad_fract, daylength, flat_potrad, tt_max0)
Return type: sg
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metsim.physics.
svp
[source]¶ Compute the saturated vapor pressure.
[1] Maidment, David R. Handbook of hydrology. McGraw-Hill Inc., 1992 Equation 4.2.2. Parameters: - temp – Temperature (degrees Celsius)
- a – (optional) parameter
- b – (optional) parameter
- c – (optional) parameter
Returns: Saturated vapor pressure (Pa)
Return type:
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metsim.physics.
svp_slope
(temp: pandas.core.series.Series, a: float=0.61078, b: float=17.269, c: float=237.3)[source]¶ Compute the gradient of the saturated vapor pressure as a function of temperature.
[1] Maidment, David R. Handbook of hydrology. McGraw-Hill Inc., 1992. Equation 4.2.3. Parameters: temp – Temperature (degrees Celsius) Returns: Gradient of d(svp)/dT. Return type: dsvp_dT
MtClim¶
MTCLIM
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metsim.methods.mtclim.
calc_prec
(df: pandas.core.frame.DataFrame, params: dict)[source]¶ Adjust precitation according to ratio of isohyet ratio of the given site to some base value.
Parameters: - df – Dataframe containing daily precipitation timeseries.
- params – Dictionary containing isohyet values
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metsim.methods.mtclim.
calc_snowpack
(df: pandas.core.frame.DataFrame, snowpack: float=0.0)[source]¶ Estimate snowpack as swe.
Parameters: - df – Dataframe with daily timeseries of precipitation and minimum temperature.
- snowpack – (Optional - defaults to 0) Initial snowpack
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metsim.methods.mtclim.
calc_srad_hum
(df: pandas.core.frame.DataFrame, sg: dict, elev: float, params: dict, win_type: str='boxcar')[source]¶ Calculate shortwave, humidity
Parameters: - df – Dataframe containing daily timeseries
- elev – Elevation in meters
- params – A dictionary of parameters from the MetSim object
- win_type – (Optional) The method used to calculate the 60 day rolling average of precipitation
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metsim.methods.mtclim.
calc_t_air
(df: pandas.core.frame.DataFrame, elev: float, params: dict)[source]¶ Adjust temperatures according to lapse rates and calculate t_day
Parameters: - df – Dataframe with daily max and min temperatures
- elev – Elevation in meters
- params – Dictionary containing parameters from a MetSim object. Lapse rates are used in this calculation.
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metsim.methods.mtclim.
run
(forcing: pandas.core.frame.DataFrame, params: dict, elev: float, lat: float, disagg=True)[source]¶ Run all of the mtclim forcing generation
Parameters: - forcing – The daily forcings given from input
- solar_geom – Solar geometry of the site
Returns: Dataframe of daily or subdaily forcings
Return type: forcing
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metsim.methods.mtclim.
sw_hum_iter
(df: pandas.core.frame.DataFrame, sg: dict, pa: float, pva: pandas.core.series.Series, parray: pandas.core.series.Series, dtr: pandas.core.series.Series, params: dict)[source]¶ Calculated updated values for dewpoint temperature and saturation vapor pressure.
Parameters: - df – Dataframe containing daily timeseries of cloud cover fraction, tfmax, swe, and shortwave radiation
- sg – Solar geometry dictionary, calculated with metsim.physics.solar_geom.
- pa – Air pressure in Pascals
- pva – Vapor presure in Pascals
- parray – 60 day rolling average of precipitation in cm
- dtr – Daily temperature range
- params – A dictionary of parameters from a MetSim object
Returns: A tuple of dewpoint temperature and saturation vapor pressure
Return type: (tdew, svp)
Disagg¶
Disaggregates daily data down to finer grained data using some heuristics
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metsim.disaggregate.
disaggregate
(df_daily: pandas.core.frame.DataFrame, params: dict, solar_geom: dict)[source]¶ Take a daily timeseries and scale it down to a finer time scale.
Parameters: - df_daily – Dataframe containing daily timeseries. Should be the result of one of the methods provided in the methods directory.
- params – A dictionary containing the class parameters of the MetSim object.
- solar_geom – A dictionary of solar geometry variables
Returns: A dataframe with sub-daily timeseries.
Return type: df_disagg
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metsim.disaggregate.
longwave
(air_temp: pandas.core.series.Series, vapor_pressure: pandas.core.series.Series, tskc: pandas.core.series.Series, params: dict)[source]¶ Calculate longwave. This calculation can be performed using a variety of parameterizations for both the clear sky and cloud covered emissivity. Options for choosing these parameterizations should be passed in via the params argument.
Parameters: - air_temp – Sub-daily temperature
- vapor_pressure – Sub-daily vapor pressure
- tskc – Daily cloud fraction
- params – A dictionary of parameters, which contains information about which emissivity and cloud fraction methods to use.
Returns: A sub-daily timeseries of the longwave radiation as well as a sub-daily timeseries of the cloud cover fraction.
Return type: (lwrad, tskc)
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metsim.disaggregate.
prec
(prec: pandas.core.series.Series, ts: float)[source]¶ Splits the daily precipitation evenly throughout the day
Parameters: - prec – Daily timeseries of precipitation
- ts – Timestep to disaggregate down to
Returns: A sub-daily timeseries of precipitation
Return type:
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metsim.disaggregate.
relative_humidity
(vapor_pressure: pandas.core.series.Series, temp: pandas.core.series.Series)[source]¶ Calculate relative humidity from vapor pressure and temperature.
Parameters: - vapor_pressure – A sub-daily timeseries of vapor pressure
- temp – A sub-daily timeseries of temperature
Returns: A sub-daily timeseries of relative humidity
Return type: rh
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metsim.disaggregate.
set_min_max_hour
(disagg_rad: pandas.core.series.Series, n_days: int, ts: float, params: dict)[source]¶ Determine the time at which min and max temp is reached for each day.
Parameters: - disagg_rad – Shortwave radiation disaggregated to sub-daily timesteps.
- n_days – The number of days being disaggregated
- ts – Timestep used for disaggregation
- params – A dictionary of class parameters of the MetSim object.
Returns: A tuple containing 2 timeseries, corresponding to time of min and max temp, respectively
Return type: (t_t_min, t_t_max)
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metsim.disaggregate.
shortwave
(sw_rad: pandas.core.series.Series, daylength: pandas.core.series.Series, day_of_year: pandas.core.series.Series, tiny_rad_fract: <built-in function array>, params: dict)[source]¶ Disaggregate shortwave radiation down to a subdaily timeseries.
Parameters: - sw_rad – Daily incoming shortwave radiation
- daylength – List of daylength time for each day of year
- day_of_year – Timeseries of index of days since Jan-1
- tiny_rad_fract – Fraction of the daily potential radiation during a radiation time step defined by SW_RAD_DT
- params – Dictionary of parameters from the MetSim object
Returns: A sub-daily timeseries of shortwave radiation.
Return type: disaggrad
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metsim.disaggregate.
temp
(df_daily: pandas.core.frame.DataFrame, df_disagg: pandas.core.frame.DataFrame, t_t_min: <built-in function array>, t_t_max: <built-in function array>, ts: float)[source]¶ Disaggregate temperature using a Hermite polynomial interpolation scheme.
Parameters: - df_daily – A dataframe of daily values.
- df_disagg – A dataframe of sub-daily values.
- t_t_min – Times at which minimum daily temperatures are reached.
- t_t_max – Times at which maximum daily temperatures are reached.
- ts – Timestep for disaggregation
Returns: A sub-daily timeseries of temperature.
Return type: temps
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metsim.disaggregate.
vapor_pressure
(vp_daily: pandas.core.series.Series, temp: pandas.core.series.Series, t_t_min: <built-in function array>, n_out: int, ts: float)[source]¶ Calculate vapor pressure. First a linear inerpolation of the daily values is calculated. Then this is compared to the saturated vapor pressure calculated using the disaggregated temperature. When the interpolated vapor pressure is greater than the calculated saturated vapor pressure, the interpolation is replaced with the saturation value.
Parameters: - vp_daily – Daily vapor pressure
- temp – Sub-daily temperature
- t_t_min – Timeseries of minimum daily temperature
- n_out – Number of output observations
- ts – Timestep to disaggregate down to
Returns: A sub-daily timeseries of the vapor pressure
Return type: vp