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Spectral Transformation Using a Cubed-Sphere Grid for a Three-Dimensional Variational Data Assimilation System

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  • 1 Korea Institute of Atmospheric Prediction Systems, Seoul, South Korea
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Abstract

Atmospheric numerical models using the spectral element method with cubed-sphere grids (CSGs) are highly scalable in terms of parallelization. However, there are no data assimilation systems for spectral element numerical models. The authors devised a spectral transformation method applicable to the model data on a CSG (STCS) for a three-dimensional variational data assimilation system (3DVAR). To evaluate the 3DVAR system based on the STCS, the authors conducted observing system simulation experiments (OSSEs) using Community Atmosphere Model with Spectral Element dynamical core (CAM-SE). They observed root-mean-squared error reductions: 24% and 34% for zonal and meridional winds (U and V), respectively; 20% for temperature (T); 4% for specific humidity (Q); and 57% for surface pressure (Ps) in analysis and 28% and 27% for U and V, respectively; 25% for T; 21% for Q; and 31% for Ps in 72-h forecast fields. In this paper, under the premise that the same number of grid points is set, the authors show that the use of a greater polynomial degree, np, produces better performance than use of a greater element count, ne, on equiangular coordinates in terms of the wave representation.

Current affiliation: Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York.

Corresponding author address: Hyo-Jong Song, Korea Institute of Atmospheric Prediction Systems, 4F, Hankuk Computer Building, 35 Boramae-ro 5-gil, Dongjak-gu, Seoul 156-849, South Korea. E-mail: hsong2@albany.edu

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

Abstract

Atmospheric numerical models using the spectral element method with cubed-sphere grids (CSGs) are highly scalable in terms of parallelization. However, there are no data assimilation systems for spectral element numerical models. The authors devised a spectral transformation method applicable to the model data on a CSG (STCS) for a three-dimensional variational data assimilation system (3DVAR). To evaluate the 3DVAR system based on the STCS, the authors conducted observing system simulation experiments (OSSEs) using Community Atmosphere Model with Spectral Element dynamical core (CAM-SE). They observed root-mean-squared error reductions: 24% and 34% for zonal and meridional winds (U and V), respectively; 20% for temperature (T); 4% for specific humidity (Q); and 57% for surface pressure (Ps) in analysis and 28% and 27% for U and V, respectively; 25% for T; 21% for Q; and 31% for Ps in 72-h forecast fields. In this paper, under the premise that the same number of grid points is set, the authors show that the use of a greater polynomial degree, np, produces better performance than use of a greater element count, ne, on equiangular coordinates in terms of the wave representation.

Current affiliation: Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York.

Corresponding author address: Hyo-Jong Song, Korea Institute of Atmospheric Prediction Systems, 4F, Hankuk Computer Building, 35 Boramae-ro 5-gil, Dongjak-gu, Seoul 156-849, South Korea. E-mail: hsong2@albany.edu

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

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