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1.6.0

Getting started

  • Installation
  • Using Docker
  • IDEs for Python

Tutorials & examples

  • Introduction
    • How to use the tutorials and examples?
    • An incomplete introduction to Python
    • Getting started with wradlib
      • Get wradlib version
      • Typical Workflow for Radar-Based RainfallEstimation
      • From Reflectivity to Rainfall
      • Plot Polar Data asPPI
      • wradlib in an hour
  • Data Input - Data Output
  • Attenuation Correction
  • Echo Classification
  • Georeferencing
  • Plotting
  • Interpolation
  • Rainfall Adjustment
  • Zonal Statistics
  • Specials

Help & reference

  • Library Reference
  • Community
  • Developer Guide
  • Bibliography
  • Release Notes
wradlib
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  • Introduction »
  • Getting started with wradlib
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Getting started with wradlib¶

This section provides a collection of example code snippets to make users familiar with \(\omega radlib\).

Examples List

  • Get wradlib version
  • Typical Workflow for Radar-Based RainfallEstimation
    • Introduction
    • Reading the data
    • Clutter removal
    • Attenuation correction
    • Vertical Profile of Reflectivity
    • Conversion of Reflectivity into Rainfall
    • Rainfall accumulation
    • Georeferencing and Projection
    • Gridding
    • Adjustment by rain gage observations
    • Verification and quality control
    • Visualisation and mapping
    • Data export to other applications
  • From Reflectivity to Rainfall
    • An example with real radar data
  • Plot Polar Data asPPI
    • Read a polar data set from the German Weather Service
    • Simple ways to plot this dataset
    • Plotting just one sector
    • Adding a crosshair to the PPI
    • Placing the polar data in a projected Cartesian reference system
    • Some side effects of georeferencing
    • Simple Plot on Mercator-Map using cartopy
    • More decorations and annotations
  • wradlib in an hour
    • Some background, first
    • Your entry points
    • For developers
    • Installation
    • To run our tutorials…
    • Development paradigm
    • Import wradlib
    • Reading and viewing data
    • Addressing observational errors and artefacts
    • Integration with other geodata
    • Merging with other sensors
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