Post-Processing

Post-processing techniques are used to make forecasts more useful and usable.

Value is added to the raw model output using a number of different methods. Very short range forecasts are improved by accounting for differences in model grids and resolutions and integrating model forecasts with latest observations. Systematic errors in longer range forecasts are corrected using statistical techniques and used to generate optimal site-specific forecasts from raw model fields. Forecast data is also to fed into downstream application models such as MORST which delivers to OpenRoad customers.  More sophisticated downstream processing can also assess the impact of weather on a customer's problem, thus making Met Office products more focused on customer needs.

Key Aims

  • to improve systematic, near-surface errors in model forecasts using physically and/or statistically based techniques.
  • to tailor model output to specific customer needs.

Current Projects

  • Monitoring: Continuous monitoring is essential for diagnosing problems.
  • Nowcasting: making use of current observations can improve the quality of model forecasts.
  • Ensembles: Using Ensemble forecasts to enhance local forecasts by providing more information on forecast uncertainty.

Last updated: 15 April 2014