import numpy as np
import pandas as pd
from dynamic_characterization.classes import CharacterizedRow
[docs]
def characterize_co2(
series,
period: int | None = 100,
cumulative: bool | None = False,
) -> CharacterizedRow:
"""
Calculate the cumulative or marginal radiative forcing (CRF) from CO2 for each year in a given period.
If `cumulative` is True, the cumulative CRF is calculated. If `cumulative` is False, the marginal CRF is calculated.
Takes a single row of the TimeSeries Pandas DataFrame (corresponding to a set of (`date`/`amount`/`flow`/`activity`).
For each year in the given period, the CRF is calculated.
Units are watts/square meter/kilogram of CO2.
Returns
-------
A CharacterizedRow object (namedtuple) with the following fields:
- date: datetime64[s]
- amount: float
- flow: str
- activity: str
Notes
-----
See also the relevant scientific publication on CRF: https://doi.org/10.5194/acp-13-2793-2013
See also the relevant scientific publication on the numerical calculation of CRF: http://pubs.acs.org/doi/abs/10.1021/acs.est.5b01118
See Also
--------
characterize_methane: The same function for CH4
"""
# functional variables and units (from publications listed in docstring)
RE = 1.76e-15 # Radiative forcing (W/m2/kg)
alpha_0, alpha_1, alpha_2, alpha_3 = 0.2173, 0.2240, 0.2824, 0.2763
tau_1, tau_2, tau_3 = 394.4, 36.54, 4.304
decay_term = lambda year, alpha, tau: alpha * tau * (1 - np.exp(-year / tau))
date_beginning: np.datetime64 = series.date.to_numpy()
dates_characterized: np.ndarray = date_beginning + np.arange(
start=0, stop=period, dtype="timedelta64[Y]"
).astype("timedelta64[s]")
decay_multipliers: np.ndarray = np.array(
[
RE
* (
alpha_0 * year
+ decay_term(year, alpha_1, tau_1)
+ decay_term(year, alpha_2, tau_2)
+ decay_term(year, alpha_3, tau_3)
)
for year in range(period)
]
)
forcing = pd.Series(data=series.amount * decay_multipliers, dtype="float64")
if not cumulative:
forcing = forcing.diff(periods=1).fillna(0)
return CharacterizedRow(
date=np.array(dates_characterized, dtype="datetime64[s]"),
amount=forcing,
flow=series.flow,
activity=series.activity,
)
[docs]
def characterize_methane(
series, period: int = 100, cumulative=False
) -> CharacterizedRow:
"""
Calculate the cumulative or marginal radiative forcing (CRF) from CH4 for each year in a given period.
If `cumulative` is True, the cumulative CRF is calculated. If `cumulative` is False, the marginal CRF is calculated.
Takes a single row of the TimeSeries Pandas DataFrame (corresponding to a set of (`date`/`amount`/`flow`/`activity`).
For earch year in the given period, the CRF is calculated.
Units are watts/square meter/kilogram of CH4.
Parameters
----------
series : array-like
A single row of the TimeSeries dataframe.
period : int, optional
Time period for calculation (number of years), by default 100
cumulative : bool, optional
Should the RF amounts be summed over time?
Returns
-------
A CharacterizedRow object (namedtuple) with the following fields:
- date: datetime64[s]
- amount: float
- flow: str
- activity: str
Notes
-----
See also the relevant scientific publication on CRF: https://doi.org/10.5194/acp-13-2793-2013
See also the relevant scientific publication on the numerical calculation of CRF: http://pubs.acs.org/doi/abs/10.1021/acs.est.5b01118
See Also
--------
characterize_co2: The same function for CO2
"""
# functional variables and units (from publications listed in docstring)
f1 = 0.5 # Unitless
f2 = 0.15 # Unitless
alpha = 1.27e-13 # Radiative forcing (W/m2/kg)
tau = 12.4 # Lifetime (years)
date_beginning: np.datetime64 = series.date.to_numpy()
dates_characterized: np.ndarray = date_beginning + np.arange(
start=0, stop=period, dtype="timedelta64[Y]"
).astype("timedelta64[s]")
decay_multipliers: list = np.array(
[
(1 + f1 + f2) * alpha * tau * (1 - np.exp(-year / tau))
for year in range(period)
]
)
forcing = pd.Series(data=series.amount * decay_multipliers, dtype="float64")
if not cumulative:
forcing = forcing.diff(periods=1).fillna(0)
return CharacterizedRow(
date=np.array(dates_characterized, dtype="datetime64[s]"),
amount=forcing,
flow=series.flow,
activity=series.activity,
)
