"""Mwr module, containing the :class:`Mwr` class."""
from os import PathLike
import numpy.ma as ma
import numpy.typing as npt
from cloudnetpy import utils
from cloudnetpy.constants import G_TO_KG
from cloudnetpy.datasource import DataSource
from cloudnetpy.utils import interpolate_1d
[docs]
class Mwr(DataSource):
"""Microwave radiometer class, child of DataSource.
Args:
full_path: Cloudnet Level 1b mwr file.
"""
def __init__(self, full_path: str | PathLike) -> None:
super().__init__(full_path)
self._init_lwp_data()
self._init_lwp_error()
def interpolate_to_grid(self, time_grid: npt.NDArray, max_time: float = 1) -> None:
for key, array in self.data.items():
self.data[key].data = interpolate_1d(
self.time, array.data, time_grid, max_time=max_time
)
def _init_lwp_data(self) -> None:
lwp = self.dataset.variables["lwp"][:]
if "lwp_quality_flag" in self.dataset.variables:
quality_flag = self.dataset.variables["lwp_quality_flag"][:]
lwp[quality_flag != 0] = ma.masked
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."
)