Posted in 2021
About data on curvilinear or rotated regional grids
- 29 November 2021
- visualization
2D Climate data can be sampled using different grid types and topologies, which might make a difference when it comes to data analysis and visualization. As the grid lines of regular or rectilinear grids are aligned with the axes of the geopgraphical lat-lon coordinate system, these model grids are relatively easy to deal with. A common, but more complex case is that of a curvilinear or a rotated (regional) grid. In this blog article we want to illuminate this case a bit; we describe how to identify a curvilinear grid, and we demonstrate how to visualize the data using the “normal” cylindric equidistant map projection.
Data can not only be stored in different file formats (e.g. netCDF, GRIB), but also in different data structures. Besides its spatial dimension (e.g. 1D, 2D, 3D), we need to have a closer look at the grid and the topology used. As the time dependency of the data is encoded as the time dimension, a variable might be called a 3D variable although the spatial grid is only 2D.

DKRZ CDP Updates Nov 21
- 25 November 2021
including the new ICON-ESM-LR model primarily published at DKRZ.
A first ensemble set of simulations from the ESM ICON-ESM-LR for the DECK experiments is available including the experiments
How to re-enable the deprecated python kernels?
- 12 November 2021
- Jupyterhub
As you propably know, we will rename/remove some unused/outdated/ python modules, please see the details here. Since the jupyterhub kernels are based on modules, the deprecated kernels will no longer be available as default kernels in jupyter notebooks/labs.
NO PANIC, if you have been working with those deprecated kernels and want to continue using them in your notebooks, please follow the steps below.
Deprecated Python environments
- 12 November 2021
- Jupyterhub
Since several years, we are offering Python environments on Mistral. Many of them are not updated any more and should not be used for new development. However, older scripts may rely on these environments and the versions of their installed packages.
How to install R packages in different locations?
- 25 October 2021
- Jupyterhub
The default location for R packages is not writeble and you can not install new packages. On demand we install new packages system-wide and for all users. However, it possible to install packages in different locations than root and here are the steps:
create a directory in $HOME
e.g. ~/R/libs
DKRZ CDP Updates July 21
- 02 July 2021
We proudly 🥳 announce that the CDP is extended by new sets of CMIP6 data primarily published at DKRZ. We also published new versions of corrected variables for the MPI-ESM1-2 Earth System Models.
The ensemble set of simulations from the ESM MPI-ESM1-2-HR for the dcppA-hindcast experiment is completed by another 5 realizations (8.5TB). In total, this set consists of about 10 realizations for 60 initialization years in the interval from 1960-2019 resulting in 595 realizations and 31 TB. For each realization, about 100 variables are available for a simulation time of about 10 years.
DKRZ CMIP Data Pool
- 23 June 2021
We proudly announce new publications of model simulations when we publish them at our DKRZ ESGF node. We also keep you updated about the status and the services around the CMIP Data Pool. Find extensive documentions under this link.
How to install jupyter kernel for Matlab
- 10 June 2021
- Jupyterhub
In this tutorial, I will describe the steps to create a kernel for Matlab on Levante. get the matlab_kernel
working in Jupyterhub on Levante.
conda environment with python 3.9

Requested MovieWriter (ffmpeg) not available
- 06 May 2021
- Jupyterhub
Do you want to create videos / animations with ffmpeg
from your
jupyter notebook? you need ffmpeg-python
(conda) which requires
ffmpeg
software on Mistral (module)
conda env with ffmpeg-python
and ipykernel
How to containerIze your jupyter kernel?
- 04 May 2021
- Jupyterhub
We have seen in this blog post how to encapsulate a jupyter notebook (server) in a singularity container . In this tutorial, I am going to describe how you can run a jupyter kernel in a container and make it available in the jupyter*.
Possible use case for this is to install a supported PyTorch
version
and work with jupyter notebooks (see GLIBC and the container-based workaround).
Webpack and Django
- 29 March 2021
I recently started to modernize the JavaScript part of a medium sized Django site we run at DKRZ to manage our projects. We have used a version of this site since 2002 and the current Django implementation was initially developed in 2011.
Back then JavaScript was in the form of small scripts embedded into the Django templates. jQuery was used abundantly. All in all, JavaScript was handled very haphazardly because we wanted to get back to working with Python as soon as possible.
Create a kernel from your own Julia installation
- 23 March 2021
- Jupyterhub
We already provide a kernel for Julia based on the module julia/1.7.0.
In order to use it, you only need to install ÌJulia:
Connect Spyder IDE to a remote kernel on Mistral
- 23 March 2021
- Jupyterhub
I am just describing spontaneously what worked for me to connect my local Spyder instance to a remote node on Mistral THAT YOU CAN CONNECT TO VIA SSH FROM YOUR LOCAL MACHINE!!!!
This is just a draft tutorial that will be updated/optimized afterwards.

Python environment locations
- 04 March 2021
- Jupyterhub
Kernels are based on python environments created with conda
,
virtualenv
or other package manager. In some cases, the size of the
environment can tremendously grow depending on the installed packages.
The default location for python files is the $HOME
directory. In
this case, it will quickly fill your quota. In order to avoid this, we
suggest that you create/store python files in other directories of the
filesystem on Mistral.
The following are two alternative locations where you can create your Python environment:
Transition from Mistral to Levante for projects
- 03 March 2021
In this post we want to answer a few questions which may arise for project administrators and principal investigators at DKRZ. Some of the dates for requesting new resource allocations will be different in 2021. From 2022 on we will return to the usual schedule.
Your project will be automatically extended with the same resources as for the current allocation period. After July 1, 2021, you can continue working on Mistral as you did before.
How to quickly create a test kernel
- 16 February 2021
- Jupyterhub
This is a follow up on Kernels. In
some cases, the process of publishing new Python modules can take long.
In the meantime, you can create a test kernel
to use it in
Jupyterhub. Creating new conda environments and using them as kernels
has been already described here. In
this example, we are not going to create a new conda env but only the
kernel configuration files.
in this tutorial, I will take the module python3/2021-01. as an example.
CF Python package added to the software tree
- 19 January 2021
- Jupyterhub
According to this link:
The Python cf package is an Earth Science data analysis library that is built on a complete implementation of the CF data model. The cf package implements the CF data model 1 for its internal data structures and so is able to process any CF-compliant dataset. It is not strict about CF-compliance, however, so that partially conformant datasets may be ingested from existing datasets and written to new datasets. This is so that datasets that are partially conformant may nonetheless be modified in memory.
SLURM update / Memory use
- 05 January 2021
- Jupyterhub
Slurm config on Mistral has been updated to fix an issue related to memory use.
Prior the update, some Slurm jobs continue consuming the available
memory (and even swap) of the allocated node and exceed the allocated
memory (set in sbatch
or srun
). If this occurs, it also affect
other jobs/users.
