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

 Available online: https://www.meted.ucar.edu/training_module.php?id=704

Understanding Assimilation Systems: How Models Create Their Initial Conditions, is part of the "NWP Training Series: Effective Use of NWP in the Forecast Process." This module explains the data assimilation process, including the role of the model itself as well as the observations. It provides learners an appreciation for how models use data as a function of model resolution and data type, how data influence the analysis, the limitations of data assimilation systems, the importance of initial conditions on the quality of NWP guidance, as well as the challenges of assessing the quality of NWP guidance based on the initial conditions. The differences between 3d-var with isotropic background covariances, anisotropic background covariances, 4d-var, and ensemble Kalman filter are conceptually illustrated. Back in 2000, the subject matter expert for this module was Dr. Ralph Petersen of the National Centers for Environmental Prediction, Environmental Modeling Center (NCEP/EMC). Revisions to the module were made in 2009 by Drs. Bill Bua and Stephen Jascourt, from the NWP team at UCAR/COMET.

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Language(s): English

Tags: Weather forecasting ; Numerical weather prediction ; Data assimilation ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists Add tag