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Comparative Assessment of Two Objective Forecast Models for Cases of Persistent Extreme Precipitation Events in the Yangtze-Huai River Valley in Summer 2016

机译：2016年夏季长江 - 淮河山谷持续极端降水事件两种客观预测模型的比较评估

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摘要

Two persistent extreme precipitation events (PEPEs) that caused severe flooding in the Yangtze-Huai River valley in summer 2016 presented a significant challenge to operational forecasters. To provide forecasters with useful references, the capacity of two objective forecast models in predicting these two PEPEs is investigated. The objective models include a numerical weather prediction (NWP) model from the European Centre for Medium-Range Weather Forecasts (ECMWF), and a statistical downscaling model, the Key Influential Systems Based Analog Model (KISAM). Results show that the ECMWF ensemble provides a skillful spectrum of solutions for determining the location of the daily heavy precipitation (= 25 mm day(-1)) during the PEPEs, despite its general underestimation of heavy precipitation. For lead times longer than 3 days, KISAM outperforms the ensemble mean and nearly one-half or more of all the ensemble members of ECMWF. Moreover, at longer lead times, KISAM generally performs better in reproducing the meridional location of accumulated rainfall over the two PEPEs compared to the ECMWF ensemble mean and the control run. Further verification of the vertical velocity that affects the production of heavy rainfall in ECMWF and KISAM implies the quality of the depiction of ascending motion during the PEPEs has a dominating influence on the models' performance in predicting the meridional location of the PEPEs at all lead times. The superiority of KISAM indicates that statistical downscaling techniques are effective in alleviating the deficiency of global NWP models for PEPE forecasts in the medium range of 4-10 days.
机译：截至2016年夏季长江 - 淮河谷造成严重洪水的两个持续的极端降水事件（PEPES）向运营预报员提出了重大挑战。为了提供具有有用参考的预测，调查了预测这两个佩佩的两个客观预测模型的能力。客观型号包括来自欧洲的中距离天气预报（ECMWF）中心的数值天气预报（NWP）模型，以及基于关键的影响系统的模拟模型（KISAM）的统计缩小模型。结果表明，尽管佩斯在佩斯期间，ECMWF集团提供了一种用于确定每日重沉淀的位置（＆ = 25mm（-1））的位置。对于超过3天的交货时间，Kisam优于ECMWF所有集合成员的集合均值和几乎一半或更多。此外，与ECMWF合奏均值和控制运行相比，Kisam通常在再现两种PEPES上再现累积降雨的子午线时更好地表现更好。进一步验证ECMWF和Kisam在ECMWF和KISAM中产生大量降雨的垂直速度意味着PEPES期间上升运动的质量对模型的性能有主导地影响了在所有交货时期的佩佩斯的优势。 Kisam的优越性表明，统计缩小技术有效地减轻了4-10天的中等范围内Pepe预测的全球NWP模型的缺陷。

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  • 来源
    《Weather and forecasting》 |2018年第1期|共18页
  • 作者

    Zhou Baiquan; Zhai Panmao; Niu Ruoyun;

  • 作者单位

    Chinese Acad Meteorol Sci China Meteorol Adm State Key Lab Severe Weather Beijing Peoples R China;

    Chinese Acad Meteorol Sci China Meteorol Adm State Key Lab Severe Weather Beijing Peoples R China;

    China Meteorol Adm Natl Meteorol Ctr Beijing Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 大气科学（气象学）;
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