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Improvements in WRF simulation skills of southeastern United States summer rainfall: physical parameterization and horizontal resolution

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Abstract

Realistic regional climate simulations are important in understanding the mechanisms of summer rainfall in the southeastern United States (SE US) and in making seasonal predictions. In this study, skills of SE US summer rainfall simulation at a 15-km resolution are evaluated using the weather research and forecasting (WRF) model driven by climate forecast system reanalysis data. Influences of parameterization schemes and model resolution on the rainfall are investigated. It is shown that the WRF simulations for SE US summer rainfall are most sensitive to cumulus schemes, moderately sensitive to planetary boundary layer schemes, and less sensitive to microphysics schemes. Among five WRF cumulus schemes analyzed in this study, the Zhang–McFarlane scheme outperforms the other four. Further analysis suggests that the superior performance of the Zhang–McFarlane scheme is attributable primarily to its capability of representing rainfall-triggering processes over the SE US, especially the positive relationship between convective available potential energy and rainfall. In addition, simulated rainfall using the Zhang–McFarlane scheme at the 15-km resolution is compared with that at a 3-km convection-permitting resolution without cumulus scheme to test whether the increased horizontal resolution can further improve the SE US rainfall simulation. Results indicate that the simulations at the 3-km resolution do not show obvious advantages over those at the 15-km resolution with the Zhang–McFarlane scheme. In conclusion, our study suggests that in order to obtain a satisfactory simulation of SE US summer rainfall, choosing a cumulus scheme that can realistically represent the convective rainfall triggering mechanism may be more effective than solely increasing model resolution.

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Notes

  1. When computing the skill scores of rainfall simulations, we only consider the rainfall over the terrestrial SE US.

  2. The 5-day spin-up time is determined based on our 15-day test simulation with various spin-up periods ranging from 0 day to 10 days. The rainfall bias over the SE US domain is calculated and it is found that rainfall bias sharply decreases when spin-up time increases to 3 days and is stabilized afterward. Thus, spin-up period longer than 3 days is needed to ensure the numerical stability of the simulation results. We choose a 5-day period to further ensure the adequacy of the spin-up time.

  3. The wave number in the DCT algorithm can be converted to wavelength by the relationship, where \(k ={ \sqrt {m^{2} + n^{2} } }\) is the spatial wave number (m and n are the zonal and meridional wave numbers, respectively), and L is the length of the analysis domain (Denis et al. 2002).

  4. The MYNN-3 scheme is not compatible with the Zhang–McFarlane scheme; thus the UW planetary boundary layer physics scheme is used in the simulation with the Zhang–McFarlane scheme.

  5. The Lin microphysics scheme is used for the 10-yr simulation. For the simulation with Zhang–McFarlane (BMJ) cumulus scheme, UW (MYNN3) planetary boundary layer schemes are used.

  6. The influence of data interpolation methods on the calculation of PCC and RMSE is noticed. Thus, multiple interpolation methods, including the nearest neighbor, kriging, bi-linear interpolation, and cubic spline, are compared. The specific PCC and RMSE values do vary among different methods. However, the conclusion does not change based on qualitative comparison of the 3-km simulation and the Zhang–McFarlane 15-km simulation.

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Acknowledgments

The authors thank Drs. Fei Chen, Xin-Zhong Liang, and Liang Guo for insightful discussions and comments; Drs. Ying Li, Lin Zhao, and Mr. Haifeng Zhuo for technical support; and the two anonymous reviewers who provide numerous helpful suggestions to improve the manuscript. This work is supported by NSF AGS 1147608, NIH-1R21AG044294-01A1, and NSF-EF-1065730.

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  1. Earth and Ocean Sciences, Nicholas School of the Environment and Earth Sciences, Duke University, 322 Old Chem. Bldg, P.O. Box 90227, Durham, NC, 27708, USA

    Laifang Li & Wenhong Li

  2. Department of Watershed Sciences and Department of Plants, Soils and Climate, Utah State University, Logan, UT, 84322, USA

    Jiming Jin

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  1. Laifang Li
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Correspondence to Laifang Li.

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Li, L., Li, W. & Jin, J. Improvements in WRF simulation skills of southeastern United States summer rainfall: physical parameterization and horizontal resolution. Clim Dyn 43, 2077–2091 (2014). https://doi.org/10.1007/s00382-013-2031-2

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