This study focuses on a critical aspect of this development: the implementation and evaluation of surface data assimilation within the MPAS-JEDI framework for RRFS. This methodology encompasses several critical components: selection of appropriate observation types, implementation of quality control procedures, and application of surface observation corrections. These elements are crucial for improving the accuracy of near-surface analysis within the RRFS, which in turn leads to more reliable short-term weather forecasts.
To evaluate the effectiveness of our approach, we have conducted a series of experiments in retrospective modes. These experiments assessed the impact of surface data assimilation on forecast accuracy and compared the results against outputs from the Gridpoint Statistical Interpolation (GSI) system, which is currently used in RRFS. This comparative analysis is a significant step towards aligning JEDI and GSI systems, ultimately enhancing our operational analysis and forecasting abilities.
The preliminary results from these efforts, which will be shared at the conference, are expected to demonstrate how the new RRFS system could enhance our understanding of atmospheric processes, particularly in the near-surface layer. We anticipate that these findings will highlight improvements in forecast accuracy, especially for elements such as temperature, humidity, and wind in the lower troposphere. The insights gained from this study will not only contribute to the ongoing development of RRFS but also inform future improvements in numerical weather prediction across various scales and applications.
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