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谭燕, 黄伟, 杨玉华, 等. 2022. 华东区域中尺度集合预报系统的改进及2020年梅雨期降水试验[J]. 大气科学, 46(6): 1437−1453. doi: 10.3878/j.issn.1006-9895.2203.21097
引用本文: 谭燕, 黄伟, 杨玉华, 等. 2022. 华东区域中尺度集合预报系统的改进及2020年梅雨期降水试验[J]. 大气科学, 46(6): 1437−1453. doi: 10.3878/j.issn.1006-9895.2203.21097
TAN Yan, HUANG Wei, YANG Yuhua, et al. 2022. Improvement in the Mesoscale Ensemble Forecast System in East China and A Precipitation Experiment in the 2020 Meiyu Season [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1437−1453. doi: 10.3878/j.issn.1006-9895.2203.21097
Citation: TAN Yan, HUANG Wei, YANG Yuhua, et al. 2022. Improvement in the Mesoscale Ensemble Forecast System in East China and A Precipitation Experiment in the 2020 Meiyu Season [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1437−1453. doi: 10.3878/j.issn.1006-9895.2203.21097

华东区域中尺度集合预报系统的改进及2020年梅雨期降水试验

Improvement in the Mesoscale Ensemble Forecast System in East China and A Precipitation Experiment in the 2020 Meiyu Season

  • 摘要: 考虑区域模式预报中不确定性的各种来源,分别引入初始场误差、侧边界误差和模式误差构建新一代华东区域中尺度集合预报系统,并对2020年梅雨期降水开展为期一个月的集合预报试验。通过不同时空尺度典型个例的分析可以看出,所选取的随机物理倾向扰动方案中的参数具备一定的通用性,且在参数调优中加强随机过程的影响,系统中低层的风场和湿度场有明显的反馈,集合系统的离散度得到较大改善,对预报的影响大小依次为:格点方差、随机扰动场的去相关空间和随机扰动场的去相关时间。一个月的梅雨期降水评估结果显示:集合系统升级后对各时次各量级的降水TS(Threat Score)评分均有所提升,但仍然存在着降水强度偏大的问题;从概率预报的角度来看,系统升级后,对中到大雨预报的准确率和可信度提升明显,对强降水事件的描述更准确;形势场的检验结果表明,系统的预报偏差问题得到了部分程度地改善,对大气中低层风场、湿度场和地面变量的预报效果较好。相比原华东区域中尺度集合预报系统,升级后的系统,其整体优势可概括为:预报误差减小、集合离散度明显增加,降水预报的能力在各时段各量级均有提升,其中物理过程的不确定性对于捕捉强降水事件有明显的影响,使得系统的预报可信度增加。

     

    Abstract: By considering the various sources of uncertainty in regional model forecasts, the initial condition uncertainty (IC), lateral boundary condition uncertainty (BC), and model uncertainty (PHY) are introduced to construct a new generation of East China regional mesoscale ensemble forecast systems (SWARMS-ENV2s). Experiments were performed during the 2020 Meiyu season. Selecting typical cases to adjust the parameters of the stochastically perturbed parameterization tendency (SPPT) shows that the parameter selection has certain universality, and as the influence of the random process is strengthened, the low-level wind and humidity fields in the system have obvious feedback, and the ensemble spread can be improved. The influences of the above three parameters on the forecast were as follows: variance in grid point space, spatial length scale (or spatial decorrelation), and temporal decorrelation time. Comparing SWARMS-ENV2 with SWARMS-ENV1 shows that the root mean square error (RMSE) of SWARMS-ENV2 is reduced, and the ensemble spread is obviously increased, the precipitation forecast capability is improved in all forecast periods for different magnitudes of precipitation in terms of the TS score and the probability forecast score, the uncertainty of physical processes has an obvious influence on heavy precipitation events, and the forecast reliability of the system is improved.

     

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