Dr. Willi Schimmel

Scientific programmer · atmospheric modelling

Postdoc at TROPOS. I write numerical solvers and ML models for clouds and chemistry — Cminor for stiff multiphase systems, VoodooNet in the operational Cloudnet chain.

Industrial mechanic → applied mathematics → numerical solvers → operational ML

Email willischimmel@gmx.de
LinkedIn Willi Schimmel
Code github.com/KarlJohnsonnn
ORCID 0000-0001-8428-6445
Base Leipzig, Germany

Operational ML VoodooNet in Cloudnet

Numerics Cminor v1.0 · GMD 2025

Invited talk EGU 2025 · AS1.14

Experience

Post-Doc Researcher

2023 – present

TROPOS Leipzig · Modeling of Atmospheric Processes

Cloud-seeding simulations with COSMO-SPECS in hectometer-scale large-eddy mode; ETH Zurich CLOUDLAB collaboration.
3D spectral-bin microphysics, cloud tracking, Lagrangian analysis, and radar forward operators, validated in Switzerland and Chile.
Continue Cminor / mechanism-reduction work published in GMD 2025.

PhD Researcher

2018 – 2022

Leipzig University · Remote Sensing & Arctic System / TROPOS

Built a PyTorch + CUDA model for hydrometeor classification in Doppler cloud-radar data.
Multi-sensor retrieval methods and data-processing workflows for atmospheric targets.
Code integrated into the operational Cloudnet processing chain, maintained by FMI.

Research Assistant

2014 – 2018

TROPOS Leipzig · Modeling of Atmospheric Processes

Fortran solver for atmospheric chemistry and combustion kinetics.
Sparse linear algebra and numerical optimization for stiff ODE systems.
Graph-theoretical methods to reduce complex reaction networks.

Industrial Mechanic

2006 – 2010

Sitec Industrietechnologie GmbH · Chemnitz

Customized assembly systems, laser machine tools, and industrial prototypes.

Education

2022 Dr. rer. nat., Atmospheric Science / Mathematics, Leipzig University Thesis: neural-network hydrometeor classification from Doppler cloud radar
2017 M.Sc., Applied Mathematics, HTWK Leipzig Thesis: numerical simulation of chemical multiphase processes in the troposphere
2015 B.Sc., Applied Mathematics, HTWK Leipzig
2010 Industrial Mechanic Apprenticeship, Sitec Chemnitz

Toolkit

Python Fortran CUDA PyTorch Bash R MATLAB C/C++ sparse LA stiff ODEs HPC xarray numpy Linux Git LaTeX

Selected projects

since 2014 Cminor v1.0

Fortran integrator and graph-based reduction for large multiphase mechanisms. Rosenbrock methods, sparse Jacobians, GMD publication.

2020 – operational VoodooNet

PyTorch/CUDA cloud-radar classifier now in the CloudnetPy chain maintained by FMI.

2023 – present COSMO-SPECS cloud seeding

LES ensembles and radar forward operators compared with CLOUDLAB field data. Manuscript in preparation.

Talks & publications

EGU General Assembly 2025 · Session AS1.14 · Invited / solicited talk Combined Remote-Sensing, In-Situ and Modelling of Cloud Microphysical Perturbations in Supercooled Stratus Clouds

CLOUDLAB/PolarCAP results on aerosol–cloud interactions, field data, and COSMO-SPECS ensembles. Abstract doi:10.5194/egusphere-egu25-12739.

3rd Leibniz MMS Days 2018 · Leipzig · Conference talk Numerical simulation of large atmospheric multiphase mechanisms and detailed combustion kinetics

Fortran solver for large kinetic systems: Rosenbrock methods, sparse Jacobians, TROPOS and ChemKin formats. Archived with doi:10.5446/35355.

Ohneiser, K., Seifert, P., Schimmel, W., et al. (2026). Ice-nucleating particle depletion in the wintertime boundary layer in the pre-Alpine region during stratus cloud conditions. Atmospheric Chemistry and Physics.
Ohneiser, K., Seifert, P., Schimmel, W., et al. (2025). Impact of seeder-feeder cloud interaction on precipitation formation. Atmospheric Chemistry and Physics.
Rug, L., Schimmel, W., Hoffmann, F., Knoth, O. (2025). Cminor v1.0 chemical mechanism integrator. Geoscientific Model Development.
Merker, J., Schimmel, W. (2023). On Sensitivity of Data Models w.r.t. Training Data. In Proceedings of the 2023 6th International Conference on Mathematics and Statistics, ACM.
Schimmel, W., Kalesse-Los, H., Maahn, M., et al. (2022). Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks. Atmospheric Measurement Techniques.
Vogl, T., Maahn, M., Kneifel, S., Schimmel, W., Moisseev, D., Kalesse-Los, H. (2022). Using artificial neural networks to predict riming from Doppler cloud radar observations. Atmospheric Measurement Techniques.
Kalesse-Los, H., Schimmel, W., Luke, E., Seifert, P. (2022). Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network. Atmospheric Measurement Techniques.
Hamadneh, T., Merker, J., Schimmel, W., Schuldt, G. (2022). Simplicial Bernstein form and positivity certificates for solutions obtained in a stationary digital twin by Bernstein Bubnov-Galerkin method. In 2022 5th International Conference on Mathematics and Statistics, ACM.