pycontrails.models.pcc

Probability of persistent contrail coverage (PCC).

Classes

PCC(met, surface[, params])	Potential Contrail Coverage Algorithm.
PCCParams([copy_source, ...])	PCC Model Parameters.

class pycontrails.models.pcc.PCC(met, surface, params=None, **params_kwargs)

Bases: Model

Potential Contrail Coverage Algorithm.

Determines the potential of ambient atmosphere to allow contrail formation at grid points.

Parameters:

• met (MetDataset) – Dataset containing met_variables variables.

• surface (MetDataset) – Surface level dataset containing “air_pressure”.

• params (dict[str, Any], optional) – Override PCC model parameters with dictionary. See PCCParams for model parameters.

• **params_kwargs – Override PCC model parameters with keyword arguments. See PCCParams for model parameters.

Notes

Based on Ponater et al. (2002)

Slingo1980(T, p, iwc, q, rh_crit_old, rh_crit_new)

Apply Slingo scheme described in Wood and Field, 1999.

Relationships between Total Water, Condensed Water, and Cloud Fraction in Stratiform Clouds Examined Using Aircraft Data

Parameters:

• T (xarray:DataArray) – Air Temperature, [\(K\)]

• p (xarray:DataArray) – Air Pressure, [\(Pa\)]

• iwc (xarray:DataArray) – Cloud ice water content, [\(kg \ kg^{-1}\)]

• q (xarray:DataArray) – Specific humidity

• rh_crit_old (xarray:DataArray) – Critical relative humidity, [\([0 - 1]\)]

• rh_crit_new (xarray:DataArray) – Critical relative humidity, [\([0 - 1]\)]

Returns:

xarray:DataArray – Probability of cirrus formation, [\([0 - 1]\)]

Smith1990(T, p, iwc, q, rh_crit_old, rh_crit_new)

Apply Smith Scheme described in Rap et al. (2009).

Parameterization of contrails in the UK Met OfficeClimate Model;

Parameters:

• T (xarray:DataArray) – Air Temperature, [\(K\)]

• p (xarray:DataArray) – Air Pressure, [\(Pa\)]

• iwc (xarray:DataArray) – Cloud ice water content, [\(kg \ kg^{-1}\)]

• q (xarray:DataArray) – Specific humidity

• rh_crit_old (xarray:DataArray) – Critical relative humidity, [\([0 - 1]\)]

• rh_crit_new (xarray:DataArray) – Critical relative humidity, [\([0 - 1]\)]

Returns:

xarray:DataArray – Probability of cirrus formation, [\([0 - 1]\)]

Sundqvist1989(T, p, iwc, q, rh_crit_old, rh_crit_new)

Apply Sundqvist scheme described in Ponater et al. (2002).

Contrails in a comprehensive global climate model: Parameterization and radiative forcing results

Parameters:

• T (xarray:DataArray) – Air Temperature, [\(K\)]

• p (xarray:DataArray) – Air Pressure, [\(Pa\)]

• iwc (xarray:DataArray) – Cloud ice water content, [\(kg \ kg^{-1}\)]

• q (xarray:DataArray) – Specific humidity

• rh_crit_old (xarray:DataArray) – Critical relative humidity, [\([0 - 1]\)]

• rh_crit_new (xarray:DataArray) – Critical relative humidity, [\([0 - 1]\)]

Returns:

xarray:DataArray – Probability of cirrus formation, [\([0 - 1]\)]

b_contr()

Calculate critical relative humidity threshold of contrail formation.

Returns:

xarray.DataArray – Critical relative humidity of contrail formation, [\([0 - 1]\)]

Notes

Instead of using a prescribed threshold relative humidity for rh_crit_old the threshold relative humidity now change with pressure.

This equation is described in Roeckner et al. 1996, Eq.57 THE ATMOSPHERIC GENERAL CIRCULATION MODEL ECHAM-4: MODEL DESCRIPTION AND SIMULATION OF PRESENT-DAY CLIMATE

default_params

alias of PCCParams

downselect_met()

Downselect met domain to the max/min bounds of source.

Override this method if special handling is needed in met down-selection.

• source must be defined before calling downselect_met().

• This method copies and re-assigns met using met.copy() to avoid side-effects.

Raises:

• ValueError – Raised if source is not defined. Raised if source is not a GeoVectorDataset.

• TypeError – Raised if met is not a MetDataset.

eval(source=None, **params)

Evaluate PCC model.

Currently only implemented to work on the met data input.

Parameters:

• source (MetDataset | None, optional) –

  Input MetDataset.

  If None, evaluates at the met grid points.

• **params (Any) – Overwrite model parameters before eval

Returns:

MetDataArray – PCC model output

get_source_param(key, default=<object object>, *, set_attr=True)

Get source data with default set by parameter key.

Retrieves data with the following hierarchy:

1. source.data[key]. Returns np.ndarray | xr.DataArray.

2. source.attrs[key]

3. params[key]

4. default

In case 3., the value of params[key] is attached to source.attrs[key].

Parameters:

• key (str) – Key to retrieve

• default (Any, optional) – Default value if key is not found.

• set_attr (bool, optional) – If True (default), set source.attrs[key] to params[key] if found. This allows for better post model evaluation tracking.

Returns:

Any – Value(s) found for key in source data, source attrs, or model params

Raises:

KeyError – Raises KeyError if key is not found in any location and default is not provided.

See also

-

property hash

Generate a unique hash for model instance.

Returns:

str – Unique hash for model instance (sha1)

property interp_kwargs

Shortcut to create interpolation arguments from params.

The output of this is useful for passing to interpolate_met().

Returns:

dict[str, Any] – Dictionary with keys

• ”method”

• ”bounds_error”

• ”fill_value”

• ”localize”

• ”use_indices”

• ”q_method”

as determined by params.

long_name = 'Potential contrail coverage'

met

Meteorology data

met_required = False

Require meteorology is not None on __init__()

met_variables = (MetVariable(short_name='t', standard_name='air_temperature', long_name='Air Temperature', level_type='isobaricInhPa', ecmwf_id=130, grib1_id=11, grib2_id=(0, 0, 0), units='K', amip='ta', description='Air temperature is the bulk temperature of the air, not the surface (skin) temperature.'), MetVariable(short_name='q', standard_name='specific_humidity', long_name='Specific Humidity', level_type='isobaricInhPa', ecmwf_id=133, grib1_id=51, grib2_id=(0, 1, 0), units='kg kg**-1', amip='hus', description='Specific means per unit mass. Specific humidity is the mass fraction of water vapor in (moist) air.'), MetVariable(short_name='ciwc', standard_name='specific_cloud_ice_water_content', long_name='Specific cloud ice water content', level_type='isobaricInhPa', ecmwf_id=247, grib1_id=None, grib2_id=(0, 1, 84), units='kg kg**-1', amip=None, description="This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box."))

Required meteorology pressure level variables. Each element in the list is a MetVariable or a tuple[MetVariable]. If element is a tuple[MetVariable], the variable depends on the data source. Only one variable in the tuple is required.

name = 'pcc'

optional_met_variables

Optional meteorology variables

params

Instantiated model parameters, in dictionary form

processed_met_variables

Set of required parameters if processing already complete on met input.

require_met()

Ensure that met is a MetDataset.

Returns:

MetDataset – Returns reference to met. This is helpful for type narrowing met when meteorology is required.

Raises:

ValueError – Raises when met is None.

require_source_type(type_)

Ensure that source is type_.

Returns:

_Source – Returns reference to source. This is helpful for type narrowing source to specific type(s).

Raises:

ValueError – Raises when source is not _type_.

set_source(source=None)

Attach original or copy of input source to source.

Parameters:

source (MetDataset | GeoVectorDataset | Flight | Iterable[Flight] | None) – Parameter source passed in eval(). If None, an empty MetDataset with coordinates like met is set to source.

See also

-

meth:eval

set_source_met(optional=False, variable=None)

Ensure or interpolate each required met_variables on source .

For each variable in met_variables, check source for data variable with the same name.

For GeoVectorDataset sources, try to interpolate met if variable does not exist.

For MetDataset sources, try to get data from met if variable does not exist.

Parameters:

• optional (bool, optional) – Include optional_met_variables

• variable (MetVariable | Sequence[MetVariable] | None, optional) – MetVariable to set, from met_variables. If None, set all variables in met_variables and optional_met_variables if optional is True.

Raises:

• ValueError – Variable does not exist and source is a MetDataset.

• KeyError – Variable not found in source or met.

source

Data evaluated in model

surface

transfer_met_source_attrs(source=None)

Transfer met source metadata from met to source.

update_params(params=None, **params_kwargs)

Update model parameters on params.

Parameters:

• params (dict[str, Any], optional) – Model parameters to update, as dictionary. Defaults to {}

• **params_kwargs (Any) – Override keys in params with keyword arguments.

class pycontrails.models.pcc.PCCParams(copy_source=True, interpolation_method='linear', interpolation_bounds_error=False, interpolation_fill_value=nan, interpolation_localize=False, interpolation_use_indices=False, interpolation_q_method=None, verify_met=True, downselect_met=True, met_longitude_buffer=(0.0, 0.0), met_latitude_buffer=(0.0, 0.0), met_level_buffer=(0.0, 0.0), met_time_buffer=(np.timedelta64(0, 'h'), np.timedelta64(0, 'h')), cloud_model='Smith1990', rh_crit_factor=0.7, fuel=<factory>, engine_efficiency=0.35, humidity_scaling=None)

Bases: ModelParams

PCC Model Parameters.

as_dict()

Convert object to dictionary.

We use this method instead of dataclasses.asdict to use a shallow/unrecursive copy. This will return values as Any instead of dict.

Returns:

dict[str, Any] – Dictionary version of self.

cloud_model = 'Smith1990'

Cloud model Options include “Smith1990”, “Sundqvist1989”, “Slingo1980”

copy_source = True

Copy input source data on eval

downselect_met = True

Downselect input MetDataset` to region around source.

engine_efficiency = 0.35

Engine efficiency

fuel

Fuel type

humidity_scaling = None

Humidity scaling

interpolation_bounds_error = False

If True, points lying outside interpolation will raise an error

interpolation_fill_value = nan

Used for outside interpolation value if interpolation_bounds_error is False

interpolation_localize = False

Experimental. See pycontrails.core.interpolation.

interpolation_method = 'linear'

Interpolation method. Supported methods include “linear”, “nearest”, “slinear”, “cubic”, and “quintic”. See scipy.interpolate.RegularGridInterpolator for the description of each method. Not all methods are supported by all met grids. For example, the “cubic” method requires at least 4 points per dimension.

interpolation_q_method = None

Experimental. Alternative interpolation method to account for specific humidity lapse rate bias. Must be one of None, "cubic-spline", or "log-q-log-p". If None, no special interpolation is used for specific humidity. The "cubic-spline" method applies a custom stretching of the met interpolation table to account for the specific humidity lapse rate bias. The "log-q-log-p" method interpolates in the log of specific humidity and pressure, then converts back to specific humidity. Only used by models calling to interpolate_met().

interpolation_use_indices = False

Experimental. See pycontrails.core.interpolation.

met_latitude_buffer = (0.0, 0.0)

Met latitude buffer for input to Flight.downselect_met(), in WGS84 coordinates. Only applies when downselect_met is True.

met_level_buffer = (0.0, 0.0)

Met level buffer for input to Flight.downselect_met(), in [\(hPa\)]. Only applies when downselect_met is True.

met_longitude_buffer = (0.0, 0.0)

Met longitude buffer for input to Flight.downselect_met(), in WGS84 coordinates. Only applies when downselect_met is True.

met_time_buffer = (np.timedelta64(0,'h'), np.timedelta64(0,'h'))

Met time buffer for input to Flight.downselect_met() Only applies when downselect_met is True.

rh_crit_factor = 0.7

Critical RH Factor for the model to cirrus clouds

verify_met = True

Call _verify_met() on model instantiation.