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As computers become increasingly proficient at forecasting many aspects of the weather, the role of the human forecaster is changing. In many circumstances, the forecaster's role is still to deduce the future weather, based on current conditions, knowledge, intuition, and experience. You have been taught to use the first two; the third cannot be taught and the fourth will come over time.

More and more, though, forecasters encounter situations in which their role is to evaluate errors in computer model forecasts. These errors are caused by poor data or approximations used by the model. The forecaster must decide whether the computer forecast is in error, and if it is, how to correct it. This module describes the common sources of model error, ways to identify model error, and how to correct a forecast for some simple types of model error.

Learning Objectives

  1. To be able to identify egregious analysis error
  2. To be able to recognize situations in which the model will be sensitive to the details of its parameterizations
  3. To become aware of topographic situations in which models perform poorly
  4. To learn how to adjust for model errors

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  1. Analysis Error
  2. Parameterizations
  3. Parameterization Quiz
  4. Topography and Land-Sea Interface
  5. Assessing Error: Analysis
  6. Assessing Error: Short Range Forecasts
  7. If the Model's Wrong, What's Next?
  8. Summary

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    Questions or Comments

    Technical: E-mail John Fulton < jdfult@nimbus.met.tamu.edu >
    Scientific: E-mail Dr. John Nielsen-Gammon. < nielsen@ariel.met.tamu.edu >

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