Journal of the Meteorological Society of Japan. Ser. II
Online ISSN : 2186-9057
Print ISSN : 0026-1165
Articles
Refinement of the Use of Inhomogeneous Background Error Covariance Estimated from Historical Forecast Error Samples and its Impact on Short-Term Regional Numerical Weather Prediction
Yaodeng CHENJia WANGYufang GAOXiaomeng CHENHongli WANGXiang-Yu HUANG
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2018 Volume 96 Issue 5 Pages 429-446

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Abstract

 Background error covariance (BEC) is one of the key components in data assimilation systems for numerical weather prediction. Recently, a scheme of using an inhomogeneous and anisotropic BEC estimated from historical forecast error samples has been tested by utilizing the extended alpha control variable approach (BEC-CVA) in the framework of the variational Data Assimilation system for the Weather Research and Forecasting model (WRFDA). In this paper, the BEC-CVA approach is further examined by conducting single observation assimilation experiments and continuous-cycling data assimilation and forecasting experiments covering a 3-week period. Additional benefits of using a blending approach (BEC-BLD), which combines a static, homogeneous BEC and an inhomogeneous and anisotropic BEC, are also assessed.

 Single observation experiments indicate that the noise in the increments in BEC-CVA can be somehow reduced by using BEC-BLD, while the inhomogeneous and multivariable correlations from BEC-CVA are still taken into account. The impact of BEC-CVA and BEC-BLD on short-term weather forecasts is compared with the three-dimensional variational data assimilation scheme (3DVar) and also compared with the hybrid ensemble transform Kalman filter and 3DVar (ETKF-3DVar) in WRFDA. The results show that BEC-CVA and BEC-BLD outperform the use of 3DVar. BEC-CVA and BEC-BLD underperform ETKF-3DVar, as expected. However, the computational cost of BEC-CVA and BEC-BLD is considerably less expensive because no ensemble forecasts are required.

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© 2018 The Author(s) CC-BY 4.0 (Before 2018: Copyright © Meteorological Society of Japan)
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