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首页> 外文期刊>Atmospheric research >A comparison of the rainfall forecasting skills of the WRF ensemble forecasting system using SPCPT and other cumulus parameterization error representation schemes
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A comparison of the rainfall forecasting skills of the WRF ensemble forecasting system using SPCPT and other cumulus parameterization error representation schemes

机译：使用SPCPT和其他累积参数化误差表示方案的WRF集合预报系统的降雨预报技巧比较

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摘要

The scientific community mainly uses ensemble systems to represent various sources of uncertainties and to produce better forecasts. The inability to choose accurate initial conditions leads to failed forecasts due to the well-known butterfly effect; however, in modern weather models, researchers have paid more attention to inadequacies in the sense of physical parameterization schemes and other dynamic processes. In this study, the uncertainties caused by cumulus parameterization are represented by the stochastic perturbed parameterization tendency (SPPT) scheme, and the results are compared with those of classic schemes, including the multi cumulus parameterization scheme, the parameter-perturbed scheme, and the Gaussian-noise-perturbed tendency scheme. The impacts of these various schemes on the precipitation predictions of the Weather Research and Forecasting (WRF) model are compared for Southeast Asia. The results using the stochastic perturbed cumulus parameterization tendency (SPCPT) perturbation scheme are contrasted with those of the total parameterization tendency perturbation scheme. Compared to the other schemes, the multi cumulus parameterization scheme has better Brier skill scores (BSS) and greater spreads. However, the SPCPT scheme shows significant improvements in the root mean square error (RMSE) and the Gerrity skill score (GSS) for rainfall prediction. This result also implies that the noise pattern is critical to the ensemble system; thus, using a single error representation scheme may be insufficient to estimate the error in the cumulus parameterization process.
机译：科学界主要使用集合系统来表示各种不确定性来源并产生更好的预测。由于众所周知的蝴蝶效应，无法选择准确的初始条件导致预测失败。但是，在现代天气模型中，研究人员更加关注物理参数化方案和其他动态过程方面的不足。在这项研究中，由累积参数化趋势引起的不确定性用随机扰动参数化趋势（SPPT）方案表示，并将结果与​​经典方案的结果进行比较，包括多累积量参数化方案，参数扰动方案和高斯模型-噪声干扰趋势方案。比较了这些不同方案对东南亚天气研究和预报（WRF）模型的降水预测的影响。将使用随机扰动的累积参数化趋势（SPCPT）扰动方案的结果与总参数化趋势扰动方案的结果进行对比。与其他方案相比，多累积参数化方案具有更好的Bri​​er技能得分（BSS）和更大的传播。但是，SPCPT方案显示了降雨预测的均方根误差（RMSE）和Gerrity技能得分（GSS）的显着改善。该结果还暗示了噪声模式对集成系统至关重要。因此，使用单个错误表示方案可能不足以估计累积参数化过程中的错误。

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  • 来源
    《Atmospheric research》 |2019年第4期|160-175|共16页
  • 作者

    Wu Tianjie; Min Jinzhong; Wu Shu;

  • 作者单位

    Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing 210044, Jiangsu, Peoples R China|Nanjing Univ Informat Sci & Technol, Minist Educ, Key Lab Meteorol Disaster, Nanjing 210044, Jiangsu, Peoples R China|Nanjing Univ Informat Sci & Technol, Nanjing 210044, Jiangsu, Peoples R China;

    Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing 210044, Jiangsu, Peoples R China|Nanjing Univ Informat Sci & Technol, Minist Educ, Key Lab Meteorol Disaster, Nanjing 210044, Jiangsu, Peoples R China|Nanjing Univ Informat Sci & Technol, Nanjing 210044, Jiangsu, Peoples R China;

    Univ Wisconsin, Nelson Inst Ctr Climat Res, Madison, WI 53706 USA;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Ensemble forecasts; Stochastic perturbed parameterization tendency; Cumulus parameterization; Precipitation;

    机译：集合预报;随机扰动参数化趋势;积云参数化;降水;

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