Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales - Atmosphere...

Area of Science:

• Atmospheric Science
• Meteorological Modeling
• Computational Fluid Dynamics

Background:

• The Model for Prediction Across Scales - Atmosphere (MPAS-A) is a global-scale meteorological model.
• Existing models often require enhancements for accurate retrospective air quality modeling.

Purpose of the Study:

• To implement four-dimensional data assimilation (FDDA) in MPAS-A using analysis nudging.
• To evaluate the performance of FDDA-enabled MPAS-A for meteorological simulations.

Main Methods:

• Adapted the analysis nudging technique for MPAS-A's polygonal Voronoi mesh.
• Constrained MPAS-A simulations using NCEP FNL data on a variable-resolution mesh.
• Conducted test simulations for January and July 2013, comparing with and without FDDA.

Main Results:

• MPAS-A with FDDA demonstrated high fidelity to reference meteorological fields.
• The model maintained conservation of mass, a key physical property.
• FDDA application effectively constrained model errors for temperature, humidity, and wind speed.

Conclusions:

• The modified MPAS-A with FDDA is suitable as a global-scale meteorological driver for air quality modeling.
• Analysis nudging is a viable technique for enhancing meteorological model accuracy.
• FDDA integration improves the reliability of meteorological simulations for retrospective analyses.