After the model runs, weather data often undergo additional processing steps to improve their quality. The main reasons for post-processing are:

  • Quality control: detection and removal of errors, filling of gaps
  • Accuracy: Improving accuracy by quality control, bias correction, downscaling or other methods
  • Transformation: turning a signal (e.g. reflection) into a meteorological value (radiation)

Post-processing methods may be applied to different data sources:

Some post-processing methods are described in the sub-pages.