"""Mwr module, containing the :class:`Mwr` class."""
import numpy as np
from cloudnetpy import utils
from cloudnetpy.constants import G_TO_KG
from cloudnetpy.datasource import DataSource
[docs]
class Mwr(DataSource):
"""Microwave radiometer class, child of DataSource.
Args:
full_path: Cloudnet Level 1b mwr file.
"""
def __init__(self, full_path: str):
super().__init__(full_path)
self._init_lwp_data()
self._init_lwp_error()
[docs]
def rebin_to_grid(self, time_grid: np.ndarray) -> None:
"""Approximates lwp and its error in a grid using mean.
Args:
time_grid: 1D target time grid.
"""
for array in self.data.values():
array.rebin_data(self.time, time_grid)
def _init_lwp_data(self) -> None:
lwp = self.dataset.variables["lwp"][:]
self.append_data(lwp, "lwp")
def _init_lwp_error(self) -> None:
random_error, bias = 0.25, 20
lwp_error = utils.l2norm(self.data["lwp"][:] * random_error, bias * G_TO_KG)
self.append_data(lwp_error, "lwp_error", units="kg m-2")
self.data["lwp_error"].comment = (
"This variable is a rough estimate of the one-standard-deviation\n"
f"error in liquid water path, calculated as a combination of\n"
f"a {bias} g m-2 linear error and a {round(random_error*100)} %\n"
"fractional error."
)