fluxfootprints documentation

fluxfootprints is a Python package for micrometeorological flux-footprint analysis. It provides several footprint models behind a single, consistent interface, together with tooling to build footprint climatologies from eddy-covariance tower data, summarise them by day and month, and export the results as GeoPackages, GeoTIFFs, and CSV statistics.

Available models

All models subclass BaseFootprintModel and share the same run() / get_footprint_climatology() API:

Class	Type	Reference
FFPModel	Parameterisation	Kljun et al. (2015)
ffp_climatology_new	Parameterisation	Kljun et al. (2015), xarray implementation
KormannMeixnerModel	Analytical	Kormann & Meixner (2001)
WangFootprintModel	Analytical (convective BL only)	Wang et al. (2006)
LSFootprintModelAdapter	Lagrangian stochastic	Backward particle model

Background

• Flux Footprints: Key Concepts
  • What is a flux footprint and how is it represented mathematically?
  • How are footprint values calculated in modelling?
  • What meteorological parameters are crucial for footprint prediction models?
  • How does atmospheric stability affect the footprint?
• Outline of Flux Measurement Footprint Estimation Approaches
  • 1. Analytical Models and (Semi-)Empirical Parameterizations
  • 2. Lagrangian Particle Models / Lagrangian Stochastic (LS) Models
  • 3. Large Eddy Simulations (LES)
  • 4. Ensemble-Averaged Closure Models / Eulerian Models
• Validation against Chu et al. (2021)
  • What is compared
  • Sites
  • Grid handling
  • Results
  • Tolerances and why
  • Qualitative findings reproduced
  • The climatology model upstream
  • Limitations

Representativeness

• Footprint representativeness
  • Grid conventions
  • The seven equations
  • Climatology metrics (Sect. 2.2)
  • The surface fields
  • Categorical evaluation (Sect. 2.4)
  • Continuous evaluation (Sect. 2.4)
  • Thresholds at a glance
  • Running the analysis
  • Figures
  • API reference
  • References
• Worked example
  • 1. Build a monthly day/night climatology
  • 2. Load a land-cover raster and an EVI stack
  • 3. Run assess_representativeness
  • 4. The four figures
  • 5. Exporting in the published layouts
  • What to take away

API reference

• fluxfootprints package
  • Model interface
  • Footprint models
  • Workflow and export helpers
  • Benchmark data preparation
  • Meteorological data retrieval
  • Module contents
• fluxfootprints
  • fluxfootprints package

Tutorials

• Kljun FFP quick-start
• Kljun FFP (xarray) quick-start
• Package-level climatology calculation
• Background
• Parameters and Libraries
• Lagrangian stochastic model
• Wang footprint model
• Background
• Parameters and Libraries
• Animating footprint time series
• Bootstrap footprint uncertainty

Data and post-processing

• Background
• Import Libraries
• Parameters
• Download NLDAS Data and Format

Note

docs/notebooks/multiply_rasters.ipynb and docs/notebooks/openet_comparison.ipynb are not listed above: they are built on the separate micromet package rather than on fluxfootprints, so they are excluded from this documentation.

Indices and tables

• Index

• Module Index

• Search Page