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.

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.

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].

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.

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 [Unterstrasser, 2016], 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.

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].