@@ -22,10 +22,15 @@ To read in and work with the HDF4 files, it is helpful to create a dedicated pyt
* pythonenv: create dedicated python environment
* core: functions to read-in data and compute heating rates per granule
* solar: functions to derive daily-mean insolation, adapted from climlab of Brian Rose (see the readme file in the solar directory)
* papavasileiou2018: crh computed from 2b-flxhr-lidar r04 in Papavasileiou et al., 2018, https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3768; downloaded from https://zenodo.org/records/7236564/files/domain-mean_data.zip (--> cloudsat_calipso_global_3d_acre_multiyear_clim.nc)
To generate the binned heating rates, run ``make_binned_heating.py`` followed by ``postprocess_binned_heating.py``. The first script generates heating rates for each year, the second merges the year and takes care of the time and vertical axes, plus adds some metadata.
To validate the code, I also computed CRH for R04. To this end, the Cloudsat data center was so kind to provide the R04 files for download. The analysis scripts mirror those of R05, and are named ``make_binned_heating_R04.py`` and ``postprocess_binned_heating_R04.py``.
``how_to_best_compute_radheating.ipynb`` illustrates that heating rates should be diagnosed by using the flux divergence divided by the pressure level thickness - this is the approach implemented in the core function.
``plot_zonaltimemean_crh.ipynb`` does a time-mean zonal-mean plot of cloud-radiative heating - this is a sanity check.
``plot_zonaltimemean_crh.ipynb`` does a time-mean zonal-mean plot of cloud-radiative heating - this is a sanity check. The plots include CRH data from R05 and R04 computed by me, and R04 CRH data from Papavasileiou et al., 2018.
``sbatch_jet_submit.sh`` is a sample script to run the analysis script on one of the IMG Jet nodes using SLURM.
