2012Unpublished venueRequires access

Diagnosis of surface data assimilation with GRAPES 3D-VAR

Xulin Ma, Xiaolei Zou, Gang Li

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Abstract

The diagnoses analysis of some parameters in the numerical forecast model system is an effective method for improving model and verifying the quality of the analysis results. This paper gives some diagnosis statistical results of the assimilation surface observations with the regional GRAPES forecast and assimilation model. We mainly focus on the effects of assimilation temperature and relative humidity of surface observations on the analysis fields in this research. The horizontal and vertical correlation scale is not well defined in the current version. The physical process in the near surface layer of real atmosphere is excluded in the surface observation operator. Meanwhile, a much better quality control for surface observations is required to improve the analysis in GRAPES model.

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What this paper is about

The diagnoses analysis of some parameters in the numerical forecast model system is an effective method for improving model and verifying the quality of the analysis results. This paper gives some diagnosis statistical results of the assimilation surface observations with the regional GRAPES forecast and assimilation model. We mainly focus on the effects of assimilation temperature and relative humidity of surface observations on the analysis fields in this research. The horizontal and vertical correlation scale is not well defined in the current version. The physical process in the near surface layer of real atmosphere is excluded in the surface observation operator. Meanwhile, a much better quality control for surface observations is required to improve the analysis in GRAPES model.

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Available abstract

The diagnoses analysis of some parameters in the numerical forecast model system is an effective method for improving model and verifying the quality of the analysis results. This paper gives some diagnosis statistical results of the assimilation surface observations with the regional GRAPES forecast and assimilation model. We mainly focus on the effects of assimilation temperature and relative humidity of surface observations on the analysis fields in this research. The horizontal and vertical correlation scale is not well defined in the current version. The physical process in the near surface layer of real atmosphere is excluded in the surface observation operator. Meanwhile, a much better quality control for surface observations is required to improve the analysis in GRAPES model.

Key concepts: Data assimilation, Assimilation (phonology), Meteorology, Relative humidity, Environmental science, Atmospheric model, Focus (optics), Scale (ratio)

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