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PARSIVEL2对降雪测量的订正方法及误差计算

李遥 牛生杰 吕晶晶

李遥, 牛生杰, 吕晶晶. 2020. PARSIVEL2对降雪测量的订正方法及误差计算[J]. 大气科学, 44(4): 808−815 doi:  10.3878/j.issn.1006-9895.1908.19144
引用本文: 李遥, 牛生杰, 吕晶晶. 2020. PARSIVEL2对降雪测量的订正方法及误差计算[J]. 大气科学, 44(4): 808−815 doi:  10.3878/j.issn.1006-9895.1908.19144
LI Yao, NIU Shengjie, LÜ Jingjing. 2020. PARSIVEL2 Revised Method and Error Calculation for Snow Measurement [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 44(4): 808−815 doi:  10.3878/j.issn.1006-9895.1908.19144
Citation: LI Yao, NIU Shengjie, LÜ Jingjing. 2020. PARSIVEL2 Revised Method and Error Calculation for Snow Measurement [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 44(4): 808−815 doi:  10.3878/j.issn.1006-9895.1908.19144

PARSIVEL2对降雪测量的订正方法及误差计算

doi: 10.3878/j.issn.1006-9895.1908.19144
基金项目: 国家重点研发计划“重大自然灾害监测预警与防范”重点专项2018YFC1507905,国家自然科学基金项目41775134,江苏省研究生科研创新项目 SJKY19_0963
详细信息
    作者简介:

    李遥,女,1994年出生,硕士研究生,主要从事云雾降水物理学研究。E-mail: yao@nuist.edu.cn

    通讯作者:

    牛生杰,E-mail: niusj@nuist.edu.cn

  • 中图分类号: P429

PARSIVEL2 Revised Method and Error Calculation for Snow Measurement

Funds: National Key Research and Development Program of China (Grant 2018YFC1507905), National Natural Science Foundation of China (Grant 41775134), Jiangsu Postgraduate Research Innovation Project (Grant SJKY19_0963)
  • 摘要: 为了获得更加准确的冬季降水数据,针对PARSIVEL2(Particle Size and Velocity)测量降雪时近地面水平风的影响进行了订正及误差计算。订正结果表明:一定风速下,不考虑风的影响会造成小粒子直径的明显低估,而对于同一粒径段的粒子,风速越大,计算过程中对于粒子直径的低估越明显。风速不超过2 m s−1时,其降雪粒子下落末速度计算误差在3%左右,直径计算误差在7%以内(水平偏转角度45°)。在对2018年1月4日南京一次降雪过程中获取的真实雪花谱的分析中可以看出,忽略风的影响会导致雪花谱峰值的偏移和谱的缩窄,这会造成小粒子数浓度的高估和大粒子数浓度的低估,进而影响微物理量的计算。具体表现在雷达反射率因子Z和降雪强度I的低估,及ZI关系拟合系数a值的实际数值会大于计算值,b值则偏小。但当风速较大时,近地面流场比较复杂,垂直向湍流运动不可忽略,此种订正方法很可能不再适用。建议在以后的业务观测中增设防风圈或在后续的数据处理中增加针对风的订正,以排除风对降雪测量的影响。
  • 图  1  雪花受力图

    Figure  1.  Force acting on the snowflake

    图  2  2018年1月4日(a)不同假定风速下每档粒子平均直径;(b)订正后每档粒径与速度之间的关系;(c)不同假定风速下南京降雪雪花谱

    Figure  2.  (a) Average diameter of each particle under different assumptions of wind speed, (b) relationship between particle size and velocity after correction, (c) snowfall spectra distributions in Nanjing on 4 January, 2018, under different assumptions of wind speed

    图  3  2018年1月4日降雪过程不同假定风速下的ZI关系拟合

    Figure  3.  ZI relationship fitting under different assumed wind speeds during the snowfall process on 4 January, 2018

    表  1  不同假定风速下PARSIVEL2雨滴谱仪对应的速度和直径误差及百分比

    Table  1.   PARSIVEL2 raindrop spectrometervelocity and diameter errors and error percentage for different assumed wind speeds

    风级风速/m s−1平均误差及误差百分比
    速度误差/m s−1速度误差百分比直径误差/m s−1直径误差百分比
    1级10.01 0.6%0.021.38%
    2级20.06 3.17%0.126.77%
    3级50.3711.25%0.7322.91%
    5级100.7619.37%1.6341.22%
    下载: 导出CSV

    表  2  2018年1月4日降雪不同风速下的ZI关系拟合系数及雷达反射率因子误差Z'、降雪强度误差I'

    Table  2.   ZI relationship fitting coefficient and radar reflectivity factor error Z', snowfall intensity error I' of snowfall at different wind speeds on 4 January, 2018

    风级vw / m s−1ZI关系拟合系数ZI
    abR2
    无风06751.380.97
    1级16801.380.974%2%
    2级27361.340.9724%7%
    3级510361.320.98111%21%
    5级1014561.320.98261%40%
    下载: 导出CSV

    A  不同假定风速下PARSIVEL2每档对应降雪粒子直径及落速

    A.   Diameter and speed of snowfall particles per class at different assumed wind speeds for PARSVEL2

    不同档位va=0 m s−1(无风)va=1 m s−1(1级)va=2 m s−1(2级)va=5 m s−1(3级)va=10 m s−1(5级)
    vs/ m s−1D/mmvs/ m s−1D/mmvs/ m s−1D/mmvs/ m s−1D/mmvs/ m s−1D/mm
    10.0500.0620.0500.0640.0500.0700.0490.0840.0480.097
    20.1500.1870.1490.1930.1470.2120.1420.2530.1380.294
    30.2500.3120.2480.3220.2420.3540.2310.4230.2200.490
    40.3500.4370.3470.4510.3360.4950.3160.5920.2980.686
    50.4500.5620.4450.5810.4290.6370.3990.7610.3730.883
    60.5500.6870.5430.7100.5220.7790.4810.9310.4461.079
    70.6500.8120.6410.8390.6130.9210.5611.1000.5191.276
    80.7500.9370.7380.9680.7051.0620.6411.2690.5901.472
    90.8491.0620.8361.0940.7981.1960.7231.4270.6621.655
    100.9461.1870.9331.2180.8901.3280.8041.5820.7341.833
    111.0911.3751.0771.4061.0271.5250.9251.8110.8412.098
    121.2811.6251.2661.6551.2091.7841.0842.1130.9822.445
    131.4661.8751.4521.9041.3892.0401.2422.4101.1212.787
    141.6482.1251.6342.1521.5652.2951.3972.7031.2583.124
    151.8262.3751.8122.4011.7392.5491.5502.9931.3933.458
    162.0842.7502.0702.7741.9922.9281.7743.4241.5893.952
    172.4103.2502.3983.2732.3143.4312.0603.9921.8394.602
    182.7153.7502.7043.7712.6183.9322.3294.5542.0735.245
    192.9984.2502.9874.2702.8994.4322.5795.1112.2925.880
    203.2554.7503.2444.7693.1554.9322.8105.6642.4946.511
    213.6995.5003.6905.5173.6025.6773.2176.4802.8537.438
    224.3516.5004.3436.5154.2576.6673.8207.5483.3868.647
    235.0037.5004.9957.5134.9127.6574.4308.6043.9269.838
    245.6548.5005.6478.5125.5678.6485.0459.6494.47311.014
    256.3049.5006.2989.5116.2229.6395.66510.6875.02612.176
    267.27911.0007.27311.0097.20311.1266.60412.2305.86613.898
    278.57913.0008.57413.0088.51013.1127.86614.2707.00116.162
    289.87815.0009.87315.0079.81615.1019.14016.2938.15218.394
    2911.17617.00011.17217.00611.12017.09110.42218.3059.31620.600
    3012.47519.00012.47119.00612.42319.08311.71020.30710.49322.784
    3114.42521.50014.42221.50514.37821.57513.63822.80012.25625.487
    3217.02724.50017.02424.50416.98324.56616.21325.78214.62228.699
    下载: 导出CSV
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  • 收稿日期:  2019-04-14
  • 网络出版日期:  2019-09-29
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