Understanding Assimilation Systems: How Models Create Their Initial Conditions - version 2

Understanding Assimilation Systems: How Models Create Their Initial Conditions - version 2
After completing this module, students should be able to: 1. State the 2 primary sources (types. of information used in making a model analysis 2. List 3 advantages of using a model short-range forecast as a first guess for the analysis 3. State the fundamental assumption of data assimilation in NWP models and describe something which can happen if this assumption is violated. The answer to this question is the same regardless of the assimilation method (e.g., 3d-var, 4d-var, ensemble Kalman filter, etc.) 4. Describe what can happen to the analysis and forecast when a model can resolve and create features smaller than the network of observations assimilated can resolve in the vicinity of the feature 5. State one reason why a model might produce a better forecast if its analysis does not fit a perfect observation too closely 6. Given observations of a weather feature showing a first guess error in placement of the feature, describe how the feature will differ in a 3d-var analysis, 4d-var analysis, and ensemble Kalman filter analysis 7. List 4 scenarios that can produce a poor analysis leading to a poor forecast and describe the cause or nature of the analysis problem