Data until 2023 -- processed with CCIC paper: cloud mask is based on 2B-CLDCLASS
Data after 2023 -- processed after CCIC paper: cloud mask is based on 2B-CLDCLASS-LIDAR

Note that you can access the data without downloading it with fsspec's sshfs (https://github.com/fsspec/sshfs). Below an example (if it doesn't work, check the package versions listed after the example):

```python
import ccic
import sshfs # https://github.com/fsspec/sshfs
import xarray as xr

# Establish a remote filesystem connection
fs = sshfs.SSHFileSystem(
    'sun.rss.chalmers.se',
    username='ccic',
    client_keys=['/path/to/authorized/ssh_key']
)

# Get the first CPCIR retrieval of 2025
# The file name could be spelt out, but this shows a method of fsspec
file_path = sorted(list(fs.glob('/record/cpcir/2025/*zarr')))[0]

# Open the remote Zarr file
ds = xr.open_zarr(fs.get_mapper(file_path))
```

Note: the code above was tested with `sshfs` 2024.9.0, and the output of `xarray.show_versions()` is
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.10.13 | packaged by conda-forge | (main, Dec 23 2023, 15:36:39) [GCC 12.3.0]
python-bits: 64
OS: Linux
OS-release: 6.8.0-51-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.14.3
libnetcdf: 4.9.2

xarray: 2024.2.0
pandas: 2.2.1
numpy: 1.26.4
scipy: 1.12.0
netCDF4: 1.6.5
pydap: None
h5netcdf: 1.3.0
h5py: 3.10.0
Nio: None
zarr: 2.17.1
cftime: 1.6.3
nc_time_axis: None
iris: 3.8.1
bottleneck: 1.3.8
dask: 2024.2.1
distributed: 2024.2.1
matplotlib: 3.8.3
cartopy: 0.22.0
seaborn: None
numbagg: None
fsspec: 2024.3.1
cupy: None
pint: 0.23
sparse: None
flox: None
numpy_groupies: None
setuptools: 69.1.1
pip: 24.0
conda: None
pytest: 8.1.1
mypy: None
IPython: 8.17.2
sphinx: 7.2.6
```