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SPECIFICTION
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MPTRAC vs VaMPy

MPTRAC

slcs-jsc.github.io

VaMPy

kvslab.github.io

Side by side

FigureMPTRACVaMPy
Price fromFreeFree
Free planYesYes
Free trial——
Open source——
WebYes—
Windows—Yes
macOS—Yes
LinuxYesYes
Self-hostedYesYes
Plans published21
Spec score · Physics Simulation Software8.7 (#7)8.4 (#17)
Assessment toolsNoNo
Authoring toolsNoNo
Delivery modehybriddesktop
Free planYesYes
Lesson libraryNoNo
Offline accessYesYes
PurposeMPTRAC is a Lagrangian particle dispersion model for analyzing atmospheric transport in the free troposphere and stratosphere.VaMPy is a collection of automated scripts to prepare, run, and analyze vascular morphologies.
3D visualsYesYes

Plans

MPTRAC

  • Open-source MPTRACPrice not published
  • MPTRACFree

MPTRAC pricing →

VaMPy

  • VaMPyFree

VaMPy pricing →

In their own words

MPTRAC

MPTRAC is a Lagrangian particle dispersion model for studying atmospheric transport in the free troposphere and stratosphere. It calculates forward and backward air-parcel trajectories from meteorological wind fields, with stochastic diffusion, subgrid wind variation and mixing between parcels. Optional process parameterizations cover convection, sedimentation, decay, gas-phase and aqueous-phase chemistry, and wet and dry deposition. Listed research applications include long-range pollutant transport, volcanic ash dispersion, tracer experiments and atmospheric chemistry.

The browser-based Web Runner can configure, run and visualize simulations, then download results without local installation. It is limited to 10,000 air parcels, 30 days and 10 minutes of model runtime. The model needs meteorological input, normally netCDF files on a regular pressure, longitude and latitude grid. MPTRAC supports hybrid MPI, OpenMP and OpenACC parallelization, and offers visualization interfaces for Gnuplot and ParaView. It is GPL-3.0 software for Linux, self-hosted and web use; listed pricing includes free.

Visit slcs-jsc.github.io

VaMPy

VaMPy has no write-up on Specifiction yet; its figures are above.

Visit kvslab.github.io