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Balance characteristics of multivariate background error covariance for rainy and dry seasons and their impact on precipitation forecasts of two rainfall events

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Abstract

Atmospheric moisture content or humidity is an important analysis variable of any meteorological data assimilation system. The humidity analysis can be univariate, using humidity background (normally short-range numerical forecasts) and humidity observations. However, more and more data assimilation systems are multivariate, analyzing humidity together with wind, temperature and pressure. Background error covariances, with unbalanced velocity potential and humidity in the multivariate formulation, are generated from weather research and forecasting model forecasts, collected over a summer rainy season and a winter dry season. The unbalanced velocity potential and humidity related correlations are shown to be significantly larger, indicating more important roles unbalanced velocity potential and humidity play, in the rainy season than that in the dry season. Three cycling data assimilation experiments of two rainfall events in the middle and lower reaches of the Yangtze River are carried out. The experiments differ in the formulation of the background error covariances. Results indicate that only including unbalanced velocity potential in the multivariate background error covariance improves wind analyses, but has little impact on temperature and humidity analyses. In contrast, further including humidity in the multivariate background error covariance although has a slight negative effect on wind analyses and a neutral effect on temperature analyses, but significantly improves humidity analyses, leading to precipitation forecasts more consistent with China Hourly Merged Precipitation Analysis.

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Acknowledgments

This work is jointly sponsored by the 973 Program (2013CB430102), the Special Fund for Meteorological Scientific Research in Public Interest (GYHY201506002), the National Natural Science Foundation of China (41205082), Quality Control, Fusion and Reanalysis of Meteorological Observations and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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Authors and Affiliations

  1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, NUIST, Nanjing, 210044, China

    Yaodeng Chen, Xue Xia & Jinzhong Min

  2. Key Laboratory of Meteorological Disaster of Ministry of Education, NUIST, Nanjing, 210044, China

    Yaodeng Chen, Xue Xia & Jinzhong Min

  3. Meteorological Service Singapore, Centre for Climate Research Singapore, Singapore, 537054, Singapore

    Xiang-Yu Huang

  4. National Center for Atmospheric Research, Boulder, CO, USA, 80301

    Syed R. H. Rizvi

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  1. Yaodeng Chen
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  2. Xue Xia
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  3. Jinzhong Min
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  4. Xiang-Yu Huang
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  5. Syed R. H. Rizvi
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Correspondence to Yaodeng Chen.

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Responsible Editor: F. Mesinger.

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Chen, Y., Xia, X., Min, J. et al. Balance characteristics of multivariate background error covariance for rainy and dry seasons and their impact on precipitation forecasts of two rainfall events. Meteorol Atmos Phys 128, 579–600 (2016). https://doi.org/10.1007/s00703-016-0434-4

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  • DOI: https://doi.org/10.1007/s00703-016-0434-4

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