Weather data are often submitted to additional processing steps to improve quality. 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 (radiation) or other value
Post-Processing methods are applied to different data sources:
- measurements: quality control , others.
- Observations: transformation, interpolation,
- Weather model: Downscaling, Ensemble, Multimodel, etc.
- or a combination thereof (e.g. MOS; mLM)
Some post-processing methods are described in the sub-pages.