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Post Processing

Post Processing

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Post Processing

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  1. Post Processing

  2. Model Output Can Usually Be Improved with Post Processing • Can remove systematic bias • Can produce probabilistic information from deterministic information • Can provide forecasts for parameters that the model incapable of modeling successfully due to resolution or physics issues (e.g., shallow fog)

  3. Post Processing • Model Output Statistics was the first post-processing method used by the NWS (1969) • Based on multiple linear regression. • Essentially unchanged in 40 years. • Does not consider non-linear relationships between predictors and predictands. • Does take out much of systematic bias.

  4. There are many other post-processing approaches • Neural nets Attempts to duplicate the complex interactions between neurons in the human brain.

  5. Dynamic MOS • MOS equations are updated frequently, not static like the NWS. • Example: DiCast used by the Weather Channel

  6. They don’t MOS!

  7. UW Bias Correction of WRF

  8. And many others…