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Z–I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。
关键词:PARSIVEL2/
降雪观测/
误差订正
Abstract:To obtain accurate winter precipitation data, this study focuses on the correction and error calculation of the influence of near-surface horizontal wind during snowfall measurement using particle size and velocity (PARSIVEL2). Revised results show that under certain wind speeds, ignoring the influence of wind can cause the significant underestimation of large particles’ diameter. By contrast, large wind speeds indicate that the underestimation of same-sized particles’ diameter during calculation is evident. When the wind speed does not exceed 2 m s?1, the calculation error of the falling speed of snowfall particles is approximately 3%, and the calculation error of diameters is within 7%. In the analysis of the real snowflake spectrum obtained during a snowfall in Nanjing on 4 January, 2018, ignoring the influence of wind shifts the peak of the snowflake spectrum and narrows the spectrum, resulting in the overestimated concentration of small particles and underestimated concentration of large particles, which in turn affect the calculation of microphysical quantities. Specifically, radar reflectivity factor Z and snowfall intensity I are underestimated, and the actual value of the Z–I relationship fitting coefficient a is greater than the calculated value, whereas b is small. However, when the wind speed is large, the flow near the ground is complicated, and the vertical turbulent motion cannot be ignored. This correction method may no longer be applicable. Adding windbreakers in future observations or making corrections in subsequent data processing is recommended to eliminate the impact of wind on snowfall measurements.
Key words:PARSIVEL2/
Snowfall observation/
Error correction
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