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Moisture tracking with WAM2layers

Moisture origins of extreme rain causing floods in 2021

WAM2layers is an atmospheric moisture tracking model. It can be used to determine where precipitation originally evaporated (backtracking), or where evaporated moisture eventually ends up (forward tracking).

In a chain of small projects, we have developed a new version of WAM2layers that is more efficient and user friendly. Moreover, we have incorporated many software engineering best practices. Documentation has been added, the model is packaged and uploaded to PyPI, and sample data is available on the 4TU reposority, making it possible to get started in under 5 minutes.

The model now works with state-of-the-art reanalysis data from ERA5, and facilitates integration of other data sources such as climate model output from CMIP6 or convection-permitting simulations.

Furthermore, we have stimulated online interaction with users through the GitHub discussion forum. The new momentum has inspired initiatives to bring the moisture tracking community together to compare and align their models. This is examplified by the organization of a Lorentz workshop in May 2024.

Funded from:

  • 2021 eScience Center Small-Scale Initiative on Optimization
  • 2022 eScience Center Software Sustainability Project
  • 2023 NWO Open Science Fund

Participating organisations

Environment & Sustainability
Environment & Sustainability
Delft University of Technology
Koninklijk Nederlands Meteorologisch Instituut
Netherlands eScience Center
Wageningen University & Research

Impact

Output

Team

IB
Imme Benedict
Lead applicant
Wageningen University and Research
CW
Chris Weijenborg
RH
Rein Haarsma
Koninklijk Nederlands Meteorologisch Instituut
Niels  Drost
Niels Drost
Programme Manager
Netherlands eScience Center

Related software

WAM2layers

WA

WAM2layers is an atmospheric moisture-tracking model that can be used to determine where precipitation originally evaporated (backtracking), or where evaporated moisture eventually ends up (forward tracking).

Updated 20 months ago
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