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Software & models

What we build.

Models, frameworks and emulators developed or co-maintained by AMC-Lahti researchers. Each entry links to its reference publication when available.

TopoFlow

Topography-aware pollutant-flow learning for high-resolution air quality

Neural model that explicitly incorporates terrain topography to predict pollutant transport at high spatial resolution. Used for fine-scale air-quality forecasting in regions with complex orography.

Language: PyTorch
Maintainers: Ammar Kheder, Helmi Toropainen, Wenqing Peng, Zhi-Song Liu, Michael Boy

Reference

Kheder, A., Toropainen, H., Peng, W., Antão, S., Chen, J., Boy, M., Liu, Z.-S.. TopoFlow: topography-aware pollutant flow learning for high-resolution air quality prediction, npj Climate and Atmospheric Science (2026).

DOI: 10.1038/s41612-026-01417-5 Project page

SOSAA

Column model for biosphere–atmosphere interactions

One-dimensional chemistry-transport model coupling boundary-layer meteorology, gas-phase chemistry, and aerosol dynamics. Co-developed and maintained at AMC-Lahti for studies on biogenic VOCs, new-particle formation, and air-quality processes.

Language: Fortran 95
Maintainers: Michael Boy, Petri Clusius, Carlton Xavier, Benjamin Foreback

Reference

Clusius, P., Foreback, B., Xavier, C., Roldin, P., Boy, M., et al.. SOSAA: a column model for biosphere–atmosphere interactions, Atmospheric Chemistry and Physics, 26, 1967 (2026).

DOI: 10.5194/acp-26-1967-2026

FLEXPART-SOSAA

Lagrangian air-mass trajectories coupled with column chemistry

Coupling of the FLEXPART particle-dispersion model with the SOSAA column model, used to track the chemical evolution of air masses arriving at a site (e.g., Beijing severe-haze events, Arctic transport).

Language: Fortran + Python
Maintainers: Benjamin Foreback, Petri Clusius

Reference

Foreback, B., et al.. A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, Big Earth Data (2024).

DOI: 10.1080/20964471.2024.2316230

ARCA box

Atmospherically Relevant Chemistry and Aerosol box model

Zero-dimensional process model for gas-phase chemistry coupled with aerosol formation and growth. Used for in-depth process studies and for generating training data for machine-learning emulators.

Language: Fortran + Python
Maintainers: Petri Clusius, Carlton Xavier, Michael Boy

Reference

Clusius, P., Xavier, C., Pichelstorfer, L., Zhou, P., Olenius, T., Roldin, P., Boy, M.. Atmospherically Relevant Chemistry and Aerosol box model — ARCA box (v1.2), Geoscientific Model Development, 15, 7257–7286 (2022).

DOI: 10.5194/gmd-15-7257-2022

Neural emulator for atmospheric chemistry ODE

AI surrogate for stiff chemistry kinetics

Neural-network emulator that learns to integrate the stiff systems of ODEs describing atmospheric chemistry, enabling fast and differentiable predictions for air-quality forecasting and inverse problems.

Language: PyTorch
Maintainers: Zhi-Song Liu, Petri Clusius, Michael Boy

Reference

Liu, Z., Clusius, P., Boy, M.. Neural network emulator for atmospheric chemical ODE, Neural Networks, 184, 107106 (2025).

DOI: 10.1016/j.neunet.2024.107106

Want to contribute, use one of these in your work, or list your model here? Reach out at contact@amc-lahti.fi.