pycontrails.models.pcr

Persistent contrail regions (PCR = SAC & ISSR).

Equivalent to (SAC & ISSR)

Functions

pcr(air_temperature, specific_humidity, ...)

Calculate regions of persistent contrail formation.

Classes

PCR([met, params])	Determine points with likely persistent contrails (PCR).
PCRParams([copy_source, ...])	Persistent Contrail Regions (PCR) parameters.

class pycontrails.models.pcr.PCR(met=None, params=None, **params_kwargs)

Bases: Model

Determine points with likely persistent contrails (PCR).

Intersection of Ice Super Saturated Regions (ISSR) with regions in which the Schmidt-Appleman Criteria (SAC) is satisfied.

Parameters:

• met (MetDataset) – Dataset containing “air_temperature”, “specific_humidity” variables

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

• **params_kwargs – Override PCR model parameters with keyword arguments. See PCRGridParams for model parameters.

default_params

alias of PCRParams

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.

classmethod ecmwf_met_variables()

Return an ECMWF-specific list of required meteorology variables.

Returns:

tuple[MetVariable] – List of ECMWF-specific variants of required variables

eval(source=None, **params)

Evaluate potential contrails regions of the met grid.

Parameters:

• source (GeoVectorDataset | Flight | MetDataset | None, optional) – Input GeoVectorDataset or Flight. If None, evaluates at the met grid points.

• **params (Any) – Overwrite model parameters.

Returns:

GeoVectorDataset | Flight | MetDataset – Returns 1 in potential contrail regions, 0 everywhere else. Returns np.nan if interpolating outside meteorology grid.

classmethod generic_met_variables()

Return a model-agnostic list of required meteorology variables.

Returns:

tuple[MetVariable] – List of model-agnostic variants of required variables

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

Get data from other source-compatible object with default set by model parameter key.

Retrieves data with the following hierarchy:

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

2. other.attrs[key]

3. params[key]

4. default

In case 3., the value of params[key] is attached to other.attrs[key] unless set_attr is set to False.

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 other data, other attrs, or model params

Raises:

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

See also

get_source_param, pycontrails.core.vector.GeoVectorDataset.get_data_or_attr

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] unless set_attr is set to False.

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

get_data_param, pycontrails.core.vector.GeoVectorDataset.get_data_or_attr

classmethod gfs_met_variables()

Return a GFS-specific list of required meteorology variables.

Returns:

tuple[MetVariable] – List of GFS-specific variants of required variables

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 = 'Persistent contrail regions'

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.'))

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 and the tuple must include entries for a model-agnostic variable, an ECMWF-specific variable, and a GFS-specific variable. Only one of the three variable in the tuple is required for model evaluation.

name = 'pcr'

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

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

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.pcr.PCRParams(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')), rhi_threshold=1.0, humidity_scaling=None, engine_efficiency=0.3, fuel=<factory>)

Bases: SACParams, ISSRParams

Persistent Contrail Regions (PCR) 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.

copy_source = True

Copy input source data on eval

downselect_met = True

Downselect input MetDataset` to region around source.

engine_efficiency = 0.3

Jet engine efficiency, [\(0 - 1\)]

fuel

Fuel type. Overridden by Fuel provided on input source attributes

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.

rhi_threshold = 1.0

RHI Threshold

verify_met = True

Call _verify_met() on model instantiation.

pycontrails.models.pcr.pcr(air_temperature, specific_humidity, air_pressure, engine_efficiency, ei_h2o, q_fuel)

Calculate regions of persistent contrail formation.

Ice Super Saturated Regions (ISSR) where the Schmidt-Appleman Criteria (SAC) is satisfied.

Parameters of type ArrayLike must have compatible shapes.

Parameters:

• air_temperature (ArrayLike) – A sequence or array of temperature values, [\(K\)]

• specific_humidity (ArrayLike) – A sequence or array of specific humidity values, [\(kg_{H_{2}O} \ kg_{air}^{-1}\)]

• air_pressure (ArrayLike) – A sequence or array of atmospheric pressure values, [\(Pa\)].

• engine_efficiency (float | ArrayLike) – Engine efficiency, [\(0 - 1\)]

• ei_h2o (float) – Emission index of water vapor, [\(kg \ kg^{-1}\)]

• q_fuel (float) – Specific combustion heat of fuel combustion, [\(J \ kg^{-1} \ K^{-1}\)]

Returns:

• pcr (ArrayLike) – PCR state of each point indexed by the ArrayLike parameters.

• sac (ArrayLike) – SAC state

• issr (ArrayLike) – ISSR state