Editorial Type:
Article
Article Type:
Research Article

Inhomogeneous Background Error Modeling for WRF-Var Using the NMC Method

Hongli Wang National Center for Atmospheric Research, Boulder, Colorado

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Xiang-Yu Huang National Center for Atmospheric Research, Boulder, Colorado

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Juanzhen Sun National Center for Atmospheric Research, Boulder, Colorado

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Dongmei Xu Nanjing University of Information Science and Technology, Nanjing, China

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Man Zhang University of Colorado Boulder, Boulder, Colorado

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Shuiyong Fan Beijing Meteorological Bureau, Beijing, China

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Jiqin Zhong Beijing Meteorological Bureau, Beijing, China

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Print Publication:
01 Oct 2014
DOI:
https://doi.org/10.1175/JAMC-D-13-0281.1
Page(s):
2287–2309
Restricted access

Abstract

Background error modeling plays a key role in a variational data assimilation system. The National Meteorological Center (NMC) method has been widely used in variational data assimilation systems to generate a forecast error ensemble from which the climatological background error covariance can be modeled. In this paper, the characteristics of the background error modeling via the NMC method are investigated for the variational data assimilation system of the Weather Research and Forecasting (WRF-Var) Model. The background error statistics are extracted from short-term 3-km-resolution forecasts in June, July, and August 2012 over a limited-area domain. It is found 1) that background error variances vary from month to month and also have a feature of diurnal variations in the low-level atmosphere and 2) that u- and υ-wind variances are underestimated and their autocorrelation length scales are overestimated when the default control variable option in WRF-Var is used. A new approach of control variable transform (CVT) is proposed to model the background error statistics based on the NMC method. The new approach is capable of extracting inhomogeneous and anisotropic climatological information from the forecast error ensemble obtained via the NMC method. Single observation assimilation experiments show that the proposed method not only has the merit of incorporating geographically dependent covariance information, but also is able to produce a multivariate analysis. The results from the data assimilaton and forecast study of a real convective case show that the use of the new CVT improves synoptic weather system and precipitation forecasts for up to 12 h.

Current affiliations: Cooperative Institute for Research in the Atmosphere, Colorado State University, and NOAA/Earth System Research Laboratory, Boulder, Colorado.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Dr. Hongli Wang, NOAA/ESRL, 325 Broadway, Boulder, CO 80305. E-mail: hongli.wang@noaa.gov

Abstract

Background error modeling plays a key role in a variational data assimilation system. The National Meteorological Center (NMC) method has been widely used in variational data assimilation systems to generate a forecast error ensemble from which the climatological background error covariance can be modeled. In this paper, the characteristics of the background error modeling via the NMC method are investigated for the variational data assimilation system of the Weather Research and Forecasting (WRF-Var) Model. The background error statistics are extracted from short-term 3-km-resolution forecasts in June, July, and August 2012 over a limited-area domain. It is found 1) that background error variances vary from month to month and also have a feature of diurnal variations in the low-level atmosphere and 2) that u- and υ-wind variances are underestimated and their autocorrelation length scales are overestimated when the default control variable option in WRF-Var is used. A new approach of control variable transform (CVT) is proposed to model the background error statistics based on the NMC method. The new approach is capable of extracting inhomogeneous and anisotropic climatological information from the forecast error ensemble obtained via the NMC method. Single observation assimilation experiments show that the proposed method not only has the merit of incorporating geographically dependent covariance information, but also is able to produce a multivariate analysis. The results from the data assimilaton and forecast study of a real convective case show that the use of the new CVT improves synoptic weather system and precipitation forecasts for up to 12 h.

Current affiliations: Cooperative Institute for Research in the Atmosphere, Colorado State University, and NOAA/Earth System Research Laboratory, Boulder, Colorado.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Dr. Hongli Wang, NOAA/ESRL, 325 Broadway, Boulder, CO 80305. E-mail: hongli.wang@noaa.gov
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Journal of Applied Meteorology and Climatology

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