Vol.26, NO.4, 2015
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Article Navigation >  Journal of Applied Meteorological Science  > 2015  >  26(4): 385-396
Tian Fuyou, Zheng Yongguang, Zhang Tao, et al. Sensitivity analysis of short-duration heavy rainfall related diagnostic parameters with point-area verification. J Appl Meteor Sci, 2015, 26(4): 385-396. DOI:  10.11898/1001-7313.20150401.
Citation: Tian Fuyou, Zheng Yongguang, Zhang Tao, et al. Sensitivity analysis of short-duration heavy rainfall related diagnostic parameters with point-area verification. J Appl Meteor Sci, 2015, 26(4): 385-396. DOI:  10.11898/1001-7313.20150401.
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Sensitivity Analysis of Short-duration Heavy Rainfall Related Diagnostic Parameters with Point-area Verification

DOI: 10.11898/1001-7313.20150401
  • 1.

    Key Laboratory of Cloud-Precipitation Physics and Severe Storms (LACS), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049

  • 3.

    National Meteorological Center, Beijing 100081

  • Received Date: 2014-07-18
  • Rev Recd Date: 2015-03-11
  • Publish Date: 2015-07-31
    Abstract
  • The knowledge about the short-duration heavy rainfall related diagnostic parameters is very important for improving the accuracy, and it can help understand the possible mechanism of meso-scale system producing short-duration heavy rainfall. The data collections of basic datum stations (verification stations), automatic meteorological observation stations (AMOSs), and 6-hours NCEP final analysis data (FNL) diagnostic parameters from 2011 to 2012 during 1 June and 31 August are used. By considering characteristics of rain gauge distribution, the total precipitable water and the best lifted index obtained from NCEP FNL are firstly analyzed with the "point-area" verification method to reveal the sensitivities of short-duration heavy rainfall to the environment conditions. Values of diagnostic parameters for a specific basic datum stations (verification stations) is obtained by adopting bilinear interpolation method.Results show that the popularly used verification is just special cases of "point-area" verification: One could be reached by setting infinitesimal searching radius, the other can be reached by setting the record threshold infinite. Both the total precipitable water and best lifted index have optimum thresholds indicating short-duration heavy rainfall, and the short-duration heavy rainfall in 3 hours can only be directly affected by the moisture and instability within the radius of 140 km. A searching radius and a record threshold of 140 km and 2 are supposed, respectively, for 1°×1° NCEP dataset. A total precipitable water of 55 mm divides the threat score (T) into monotone increasing and monotone decreasing parts, indicating under-forecast and over-forecast, respectively. A best lifted index of -2 ℃ divides the threat score into over-forecast and under-forecast parts. It is found that the total precipitable water and K index are equal better while both got the same highest threat scores of 0.275 with the bias (B) desirable and the false alarm ratio (F) and the hit rate (H) in the reasonable range. Short-duration heavy rainfall is most sensitive to parameters concerning the environment water vapor, nine of the top ten diagnostic parameters are water vapor related parameters. Parameters indicating environment instability conditions are also influencing, but parameters used to represent dynamic conditions and vertical wind shear conditions are lower ranked.
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    References(26)

  • Fig. 1  The distribution of the verification stations, the automatic meteorological observation stations (AMOSs)

    (a) the distribution of 1887 verification stations (circles), (b) the distribution of the AMOSs obtained from the automatic observations during 2009 and 2010

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    Fig. 2  Schematic diagram of the point-area verification method

    (the lattice field indicates the numerical analysis field, the solid black dots represent the basic datum station, verification stations, and the gray triangles are the AMOSs, the black circle denotes the searching coverage around verification stations)

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    Fig. 3  Sketch map of a, b, c and d affected by the point-area verification

    (the black box represents the total sample space, the dotted ellipse indicates the forecast field while the solid black polygon represents the observational field, the dashed polygon is the observational field with the point-area verification method, the observational field of the point-area is definitely enlarged)

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    Fig. 4  Variation of scores with the searching radius and total precipitable water when the basic datum station is considered a short-duration heavy rainfall record while at least one AMOS has a record of short-duration heavy rainfall reported

    (R=0 represents results obtained with the traditional point-point verification method) (a) threat scores, (b) bias, (c) false alarm ratio, (d) hit rate

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    Fig. 5  Scores variation with the total precipitable water and the AMOS number of short-duration heavy rainfallwith 140 km searching radius

    (R=0 represents results obtained with the traditional point-point verification method) (a) threat scores, (b) bias, (c) false alarm ratio, (d) hit rate

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    Fig. 6  The same as in Fig. 4, but for the best lifted index

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    Fig. 7  The same as in Fig. 5, but for the best lifted index

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    Table  1  Names and units of parameters

    物理量名称 单位
    大气水汽总量 mm
    比湿 g·kg-1
    相对湿度 %
    水汽通量散度 g·s-1·cm-2·hPa-1
    温度平流 K·s-1
    涡度平流 s-2
    风场散度 s-1
    垂直风切变 m·s-1
    最佳对流有效位能 J·kg-1
    850 hPa与500 hPa温差 ℃
    850 hPa温度 ℃
    (最优)抬升指数 ℃
    总指数 ℃
    K指数 ℃
    沙氏指数 ℃
    假相当位温 K
    DownLoad: Download CSV

    Table  2  Parameters listed in descending order of TS, all the scores are obtained with the setting of 140 km searching radius and at least two AMOSs short-duration heavy rainfall reported

    物理量 最佳阈值 单位 T B F H
    大气水汽总量 52 mm 0.275 1.645 0.653 0.570
    K指数 35 ℃ 0.275 1.596 0.649 0.560
    850 hPa比湿 13 g·kg-1 0.263 1.542 0.657 0.629
    850 hPa假相当位温 342 K 0.261 1.860 0.682 0.591
    700 hPa假相当位温 342 K 0.256 1.938 0.691 0.599
    925 hPa比湿 15 g·kg-1 0.248 1.705 0.685 0.537
    925 hPa假相当位温 348 K 0.235 1.564 0.688 0.488
    沙氏指数 0 ℃ 0.235 1.714 0.699 0.516
    最佳对流有效位能 500 J·kg-1 0.226 1.972 0.722 0.548
    700 hPa相对湿度 80 % 0.215 1.882 0.729 0.510
    抬升指数 -3 ℃ 0.213 1.523 0.709 0.443
    最优抬升指数 -3 ℃ 0.205 1.403 0.709 0.409
    850 hPa相对湿度 85 % 0.192 1.363 0.721 0.380
    925 hPa水汽通量散度 -1×10-7 g·s-1·cm-2·hPa-1 0.180 1.308 0.731 0.352
    850 hPa水汽通量散度 -1×10-7 g·s-1·cm-2·hPa-1 0.149 1.006 0.741 0.260
    700 hPa水汽通量散度 0×10-7 g·s-1·cm-2·hPa-1 0.151 2.938 0.824 0.516
    925 hPa散度 -1×10-5 s-1 0.147 1.048 0.750 0.262
    925 hPa温度平流 5×10-6 K·s-1 0.144 1.621 0.797 0.329
    850 hPa温度平流 10×10-6 K·s-1 0.143 1.607 0.797 0.326
    500 hPa温度平流 10×10-6 K·s-1 0.136 1.724 0.811 0.326
    500 hPa温度 20 ℃ 0.129 1.401 0.805 0.273
    0~3 km垂直风切变 7 m·s-1 0.129 1.705 0.818 0.310
    500 hPa涡度平流 2×10-10 s-2 0.120 1.734 0.831 0.293
    925 hPa涡度平流 0×10-10 s-2 0.119 1.746 0.833 0.291
    总指数 44 ℃ 0.117 1.444 0.823 0.255
    0~1 km垂直风切变 7 m·s-1 0.117 1.260 0.812 0.236
    850 hPa散度 -1×10-5 s-1 0.116 0.842 0.773 0.191
    850 hPa涡度平流 1×10-10 s-2 0.114 1.558 0.832 0.261
    0~6 km垂直风切变 11 m·s-1 0.089 1.609 0.867 0.214
    850 hPa与500 hPa温差 24 ℃ 0.072 1.838 0.896 0.191
    DownLoad: Download CSV
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    • Received : 2014-07-18
    • Accepted : 2015-03-11
    • Published : 2015-07-31
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