Regionally Enhanced Global Data Assimilation (REG DA): An Evaluation of the Limited Area Model Performance
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Regionally Enhanced Global (REG) Data Assimilation (DA) is a method of global data assimilation in which high-resolution information from a single or multiple Limited Area Model (LAM) domains is blended with the global model information to create a regionally enhanced analysis of the global atmospheric state. This approach has been demonstrated to benefit both local and global model forecasts in idealized studies but has never been tested on operational numerical weather prediction models. This study investigates the limited area model forecast performance of an implementation of the REG DA approach on the operational 4D-Var data assimilation system, global model, and limited area model of the U.S. Navy. This implementation is called REG 4D-Var. The results of analysis-forecast experiments with the system show that the approach leads to small, but statistically significant overall forecast improvements and large and significant forecast improvements for Hurricane Sandy.
Brainard, Adam R (2017). Regionally Enhanced Global Data Assimilation (REG DA): An Evaluation of the Limited Area Model Performance. Master's thesis, Texas A & M University. Available electronically from