NWP Essentials: Data Assimilation

NWP Essentials: Data Assimilation

After completing this lesson, you will be able to:

  • Describe the goal of data assimilation (DA), and why a model might produce a better forecast if its analysis does not fit a perfect observation too closely
  • State the major assumptions of DA and why NWP is used as the background field
  • Describe the major shortcomings of DA
  • Explain how errors in observations and model increments are applied to produce the analysis
  • Explain how 3D-VAR and 4D-VAR handle background error covariances
  • Identify examples of bad analysis