pycontrails.models.pcr¶
Persistent contrail regions (PCR = SAC & ISSR).
Equivalent to (SAC & ISSR)
Functions
|
Calculate regions of persistent contrail formation. |
Classes
|
Determine points with likely persistent contrails (PCR). |
|
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” variablesparams (
dict[str
,Any]
, optional) – Override PCR model parameters with dictionary. SeePCRGridParams
for model parameters.**params_kwargs – Override PCR model parameters with keyword arguments. See
PCRGridParams
for model parameters.
- 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 themet
grid points.**params (
Any
) – Overwrite model parameters.
- Returns:
GeoVectorDataset | Flight | MetDataset
– Returns 1 in potential contrail regions, 0 everywhere else. Returnsnp.nan
if interpolating outside meteorology grid.
- long_name = 'Persistent contrail regions'¶
- met¶
Meteorology data
- 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 atuple[MetVariable]
. If element is atuple[MetVariable]
, the variable depends on the data source. Only one variable in the tuple is required.
- name = 'pcr'¶
- params¶
Instantiated model parameters, in dictionary form
- source¶
Data evaluated in model
- 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.
- 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: