pycontrails.models.cocip.CocipParams

class pycontrails.models.cocip.CocipParams(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=(10.0, 10.0), met_latitude_buffer=(10.0, 10.0), met_level_buffer=(40.0, 40.0), met_time_buffer=(np.timedelta64(0, 'h'), np.timedelta64(0, 'h')), process_emissions=True, dt_integration=np.timedelta64(30, 'm'), dz_m=200.0, effective_vertical_resolution=2000.0, smooth_true_airspeed=True, smooth_true_airspeed_window_length=7, smooth_true_airspeed_polyorder=1, humidity_scaling=None, preprocess_lowmem=False, compute_tau_cirrus_in_model_init='auto', filter_sac=True, filter_initially_persistent=True, persistent_buffer=None, verbose_outputs=False, compute_atr20=False, global_rf_to_atr20_factor=0.0151, initial_wake_vortex_depth=0.5, sedimentation_impact_factor=0.5, default_nvpm_ei_n=1000000000000000.0, wind_shear_enhancement_exponent=0.5, nvpm_ei_n_enhancement_factor=1.0, min_ice_particle_number_nvpm_ei_n=10000000000000.0, max_depth=1500.0, unterstrasser_ice_survival_fraction=False, radiative_heating_effects=False, contrail_contrail_overlapping=False, dz_overlap_m=500.0, radius_threshold_um=<factory>, habits=<factory>, habit_distributions=<factory>, rf_sw_enhancement_factor=1.0, rf_lw_enhancement_factor=1.0, min_altitude_m=6000.0, max_altitude_m=13000.0, max_seg_length_m=40000.0, max_age=np.timedelta64(20, 'h'), min_tau=1e-06, max_tau=10000000000.0, min_n_ice_per_m3=1000.0, max_n_ice_per_m3=1e+20)

Bases: AdvectionBuffers

Model parameters required by the CoCiP models.

__init__(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=(10.0, 10.0), met_latitude_buffer=(10.0, 10.0), met_level_buffer=(40.0, 40.0), met_time_buffer=(np.timedelta64(0, 'h'), np.timedelta64(0, 'h')), process_emissions=True, dt_integration=np.timedelta64(30, 'm'), dz_m=200.0, effective_vertical_resolution=2000.0, smooth_true_airspeed=True, smooth_true_airspeed_window_length=7, smooth_true_airspeed_polyorder=1, humidity_scaling=None, preprocess_lowmem=False, compute_tau_cirrus_in_model_init='auto', filter_sac=True, filter_initially_persistent=True, persistent_buffer=None, verbose_outputs=False, compute_atr20=False, global_rf_to_atr20_factor=0.0151, initial_wake_vortex_depth=0.5, sedimentation_impact_factor=0.5, default_nvpm_ei_n=1000000000000000.0, wind_shear_enhancement_exponent=0.5, nvpm_ei_n_enhancement_factor=1.0, min_ice_particle_number_nvpm_ei_n=10000000000000.0, max_depth=1500.0, unterstrasser_ice_survival_fraction=False, radiative_heating_effects=False, contrail_contrail_overlapping=False, dz_overlap_m=500.0, radius_threshold_um=<factory>, habits=<factory>, habit_distributions=<factory>, rf_sw_enhancement_factor=1.0, rf_lw_enhancement_factor=1.0, min_altitude_m=6000.0, max_altitude_m=13000.0, max_seg_length_m=40000.0, max_age=np.timedelta64(20, 'h'), min_tau=1e-06, max_tau=10000000000.0, min_n_ice_per_m3=1000.0, max_n_ice_per_m3=1e+20)

Methods

__init__([copy_source, ...])
as_dict()	Convert object to dictionary.

Attributes

compute_atr20	Add additional metric of ATR20 and global yearly mean RF to model output.
compute_tau_cirrus_in_model_init	Compute "tau_cirrus" variable in pressure-level met data during model initialization.
contrail_contrail_overlapping	Experimental.
copy_source	Copy input source data on eval
default_nvpm_ei_n	Default nvpm_ei_n value if no data provided and emissions calculations fails.
downselect_met	Downselect input MetDataset` to region around source.
dt_integration	Apply Euler's method with a fixed step size of dt_integration.
dz_m	Difference in altitude between top and bottom layer for stratification calculations, [\(m\)].
dz_overlap_m	Experimental.
effective_vertical_resolution	Vertical resolution (m) associated to met data.
filter_initially_persistent	Filter out waypoints if they don't satisfy the initial persistent criteria Passing in a non-default value is unusual, but is included to allow for false negative calibration and model uncertainty studies.
filter_sac	Filter out waypoints if the don't satisfy the SAC criteria Note that the SAC algorithm will still be run to calculate T_critical_sac for use estimating initial ice particle number.
global_rf_to_atr20_factor	Constant factor used to convert global- and year-mean RF, [\(W m^{-2}\)], to ATR20, [\(K\)], given by [Yin et al., 2023].
humidity_scaling	Humidity scaling
initial_wake_vortex_depth	Initial wake vortex depth scaling factor.
interpolation_bounds_error	If True, points lying outside interpolation will raise an error
interpolation_fill_value	Used for outside interpolation value if interpolation_bounds_error is False
interpolation_localize	Experimental.
interpolation_method	Interpolation method.
interpolation_q_method	Experimental.
interpolation_use_indices	Experimental.
max_age	Max age of contrail evolution.
max_altitude_m	Maximum altitude domain in simulation, [\(m\)] If set to None, this check is disabled.
max_depth	Upper bound for contrail plume depth, constraining it to realistic values.
max_n_ice_per_m3	Maximum contrail ice particle number per volume of air to prevent unrealistic values.
max_seg_length_m	Maximum contrail segment length in simulation to prevent unrealistic values, [\(m\)].
max_tau	Maximum contrail optical depth to prevent unrealistic values.
met_latitude_buffer	Met latitude buffer [WGS84] for evolution by advection.
met_level_buffer	Met level buffer [\(hPa\)] for evolution by advection.
met_longitude_buffer	Met longitude [WGS84] buffer for evolution by advection.
met_time_buffer	Met time buffer for input to Flight.downselect_met() Only applies when downselect_met is True.
min_altitude_m	Minimum altitude domain in simulation, [\(m\)] If set to None, this check is disabled.
min_ice_particle_number_nvpm_ei_n	Lower bound for nvpm_ei_n to account for ambient aerosol particles for newer engines, [\(kg^{-1}\)]
min_n_ice_per_m3	Minimum contrail ice particle number per volume of air.
min_tau	Minimum contrail optical depth.
nvpm_ei_n_enhancement_factor	Multiply flight black carbon number by enhancement factor.
persistent_buffer	Continue evolving contrail waypoints persistent_buffer beyond end of contrail life.
preprocess_lowmem	Experimental.
process_emissions	Determines whether Cocip.process_emissions() runs on model Cocip.eval() Set to False when input Flight includes emissions data.
radiative_heating_effects	Experimental.
rf_lw_enhancement_factor	Scale longwave radiative forcing.
rf_sw_enhancement_factor	Scale shortwave radiative forcing.
sedimentation_impact_factor	Sedimentation impact factor.
smooth_true_airspeed	Smoothing parameters for true airspeed.
smooth_true_airspeed_polyorder
smooth_true_airspeed_window_length
unterstrasser_ice_survival_fraction	Experimental.
verbose_outputs	Add additional values to the flight and contrail that are not explicitly necessary for calculation.
verify_met	Call _verify_met() on model instantiation.
wind_shear_enhancement_exponent	Parameter denoted by \(n\) in eq.
radius_threshold_um	Radius threshold for regime bins, [\(\mu m\)] This is the row index label for habit_distributions.
habits	Particle habit (shape) types.
habit_distributions	Mix of ice particle habits in each radius regime.

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.

compute_atr20 = False

Add additional metric of ATR20 and global yearly mean RF to model output. These are not standard CoCiP outputs but based on the derivation used in the first supplement to [Yin et al., 2023]. ATR20 is defined as the average temperature response over a 20 year horizon.

Added in version 0.50.0.

compute_tau_cirrus_in_model_init = 'auto'

Compute "tau_cirrus" variable in pressure-level met data during model initialization. Must be one of "auto", True, or False. If set to "auto", "tau_cirrus" will be computed during model initialization iff the met data is dask-backed. Otherwise, it will be computed during model evaluation after the met data is downselected.

Added in version 0.47.1.

contrail_contrail_overlapping = False

Experimental. Radiative effects due to contrail-contrail overlapping Account for change in local contrail shortwave and longwave radiative forcing due to contrail-contrail overlapping.

Added in version 0.45.

copy_source = True

Copy input source data on eval

default_nvpm_ei_n = 1000000000000000.0

Default nvpm_ei_n value if no data provided and emissions calculations fails.

downselect_met = True

Downselect input MetDataset` to region around source.

dt_integration = np.timedelta64(30,'m')

Apply Euler’s method with a fixed step size of dt_integration. Advected waypoints are interpolated against met data once each dt_integration.

dz_m = 200.0

Difference in altitude between top and bottom layer for stratification calculations, [\(m\)]. Used to approximate derivative of “lagrangian_tendency_of_air_pressure” layer.

dz_overlap_m = 500.0

Experimental. Contrail-contrail overlapping altitude interval If contrail_contrail_overlapping is set to True, contrails will be grouped into altitude intervals, and all contrails within each altitude interval are treated as one contrail layer where they do not overlap.

effective_vertical_resolution = 2000.0

Vertical resolution (m) associated to met data. Constant below applies to ECMWF data.

filter_initially_persistent = True

Filter out waypoints if they don’t satisfy the initial persistent criteria Passing in a non-default value is unusual, but is included to allow for false negative calibration and model uncertainty studies.

filter_sac = True

Filter out waypoints if the don’t satisfy the SAC criteria Note that the SAC algorithm will still be run to calculate T_critical_sac for use estimating initial ice particle number. Passing in a non-default value is unusual, but is included to allow for false negative calibration and model uncertainty studies.

global_rf_to_atr20_factor = 0.0151

Constant factor used to convert global- and year-mean RF, [\(W m^{-2}\)], to ATR20, [\(K\)], given by [Yin et al., 2023].

Added in version 0.50.0.

habit_distributions

Mix of ice particle habits in each radius regime. Rows indexes are radius_threshold_um elements. Columns indexes are habits particle habit type. See Table 2 from [Schumann et al., 2011].

habits

Particle habit (shape) types. This is the column index label for habit_distributions. See Table 2 in [Schumann et al., 2011].

humidity_scaling = None

Humidity scaling

initial_wake_vortex_depth = 0.5

Initial wake vortex depth scaling factor. This factor scales max contrail downward displacement after the wake vortex phase to set the initial contrail depth. Denoted \(C_{D0}\) in eq (14) in [Schumann, 2012].

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.

max_age = np.timedelta64(20,'h')

Max age of contrail evolution.

max_altitude_m = 13000.0

Maximum altitude domain in simulation, [\(m\)] If set to None, this check is disabled.

max_depth = 1500.0

Upper bound for contrail plume depth, constraining it to realistic values. CoCiP only uses the ambient conditions at the mid-point of the Gaussian plume, and the edges could be in subsaturated conditions and sublimate. Important when radiative_heating_effects is enabled.

max_n_ice_per_m3 = 1e+20

Maximum contrail ice particle number per volume of air to prevent unrealistic values.

max_seg_length_m = 40000.0

Maximum contrail segment length in simulation to prevent unrealistic values, [\(m\)].

max_tau = 10000000000.0

Maximum contrail optical depth to prevent unrealistic values.

met_latitude_buffer = (10.0, 10.0)

Met latitude buffer [WGS84] for evolution by advection.

met_level_buffer = (40.0, 40.0)

Met level buffer [\(hPa\)] for evolution by advection.

met_longitude_buffer = (10.0, 10.0)

Met longitude [WGS84] buffer for evolution by advection.

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.

min_altitude_m = 6000.0

Minimum altitude domain in simulation, [\(m\)] If set to None, this check is disabled.

min_ice_particle_number_nvpm_ei_n = 10000000000000.0

Lower bound for nvpm_ei_n to account for ambient aerosol particles for newer engines, [\(kg^{-1}\)]

min_n_ice_per_m3 = 1000.0

Minimum contrail ice particle number per volume of air.

min_tau = 1e-06

Minimum contrail optical depth.

nvpm_ei_n_enhancement_factor = 1.0

Multiply flight black carbon number by enhancement factor. A value of 1.0 provides no scaling. Primarily used to support uncertainty estimation.

persistent_buffer = None

Continue evolving contrail waypoints persistent_buffer beyond end of contrail life. Passing in a non-default value is unusual, but is included to allow for false negative calibration and model uncertainty studies.

preprocess_lowmem = False

Experimental. If True, attempt to reduce memory consumption during aircraft performance and initial contrail formation/persistent calculations by calling MetDataArray.interpolate() with lowmem=True.

IMPORTANT:

• Memory optimizations used when proprocess_lowmem=True are designed for meteorology backed by dask arrays with a chunk size of 1 along the time dimension. This option may degrade performance if dask if not used or if chunks contain more than a single time step.

• The impact on runtime of setting preprocess_lowmem=True depends on how meteorology data is chunked. Runtime is likely to increase if meteorology data is chunked in time only, but may decrease if meteorology data is also chunked in longitude, latitude, and level.

• Changes to data access patterns with preprocess_lowmem=True alter locations where interpolation is in- vs out-of-bounds. As a consequence, Cocip output with preprocess_lowmem=True is only guaranteed to match output with preprocess_lowmem=False when run with interpolation_bounds_error=True to ensure no out-of-bounds interpolation occurs.

Added in version 0.52.3.

process_emissions = True

Determines whether Cocip.process_emissions() runs on model Cocip.eval() Set to False when input Flight includes emissions data.

radiative_heating_effects = False

Experimental. Radiative heating effects on contrail cirrus properties. Terrestrial and solar radiances warm the contrail ice particles and cause convective turbulence. This effect is expected to enhance vertical mixing and reduce the lifetime of contrail cirrus. This parameter is experimental, and the CoCiP implementation of this parameter may change.

Added in version 0.28.9.

radius_threshold_um

Radius threshold for regime bins, [\(\mu m\)] This is the row index label for habit_distributions. See Table 2 in [Schumann et al., 2011].

rf_lw_enhancement_factor = 1.0

Scale longwave radiative forcing. Primarily used to support uncertainty estimation.

rf_sw_enhancement_factor = 1.0

Scale shortwave radiative forcing. Primarily used to support uncertainty estimation.

sedimentation_impact_factor = 0.5

Sedimentation impact factor. Denoted by \(f_{T}\) in eq. (35) of [Schumann, 2012]. Schumann describes this as “an important adjustable parameter”, and sets it to 0.1 in the original publication. In [Schumann and Graf, 2013], a value of 0.5 is referenced after comparing CoCiP predictions to observations.

smooth_true_airspeed = True

Smoothing parameters for true airspeed. Only used for Flight models. Passed directly to scipy.signal.savgol_filter(). See pycontrails.Flight.segment_true_airspeed() for details.

smooth_true_airspeed_polyorder = 1

smooth_true_airspeed_window_length = 7

unterstrasser_ice_survival_fraction = False

Experimental. Improved ice crystal number survival fraction in the wake vortex phase. Implement [], who developed a parametric model that estimates the survival fraction of the contrail ice crystal number after the wake vortex phase based on the results from large eddy simulations. This replicates Fig. 4 of [Kärcher, 2018].

Added in version 0.50.1.

Changed in version 0.54.7.

verbose_outputs = False

Add additional values to the flight and contrail that are not explicitly necessary for calculation. See also CocipGridParams.verbose_outputs_formation and CocipGridParams.verbose_outputs_evolution.

verify_met = True

Call _verify_met() on model instantiation.

wind_shear_enhancement_exponent = 0.5

Parameter denoted by \(n\) in eq. (39) of [Schumann, 2012].