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CN 11-1768/O4

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面向资料同化的风廓线雷达风场特征分析及其质控方法
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厦门市气象局

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国家自然科学基金


Wind Characteristics Analysis of Wind Profiler Radar and Its Quality Control Method for Data Assimilation
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Laboratory of Straits Meteorology,Xiamen Meteorological Bureau

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    摘要:

    以美国国家环境预报中心全球预报系统(National Centers for Environmental Prediction/Global Forecast System,NCEP/GFS)0.5°×0.5°分析场作为数值预报背景场,结合地面降水资料,面向资料同化分析了2017年1-12月逐日00、06、12、18 UTC福建12部L波段风廓线雷达(其中CFL-03系列3部、CFL-06系列9部)水平风产品质量特征,并初步探讨了不同质量控制方案的影响差异。结果表明:(1)CFL-06系列雷达在水平风的最大探测高度、有效数据获取率和低层水平风质量等方面明显优于CFL-03系列。(2)相同系列的不同风廓线雷达探测水平风的数据获取率、有效探测高度、标准差、相关系数及偏差的垂直分布特征等存在极大差异,该差异与风廓线雷达所处的地理位置(沿海或内陆)、海拔高度等并无直接关系。(3)各雷达站探测u风速相对背景场存在明显系统性负偏差,小于背景场,不满足资料同化对背景场的无偏需求,资料同化时需进行偏差订正;v风则相对较好。(4)降水对风廓线雷达探测影响较大,有降水时数据获取率在中低层有所减小,但在中高层则大幅提高;u、v风标准差在中低层有所增加,而在中高层v风标准差有所增加,u风标准差则大幅降低。(5)针对不同风廓线雷达,提出了不同高可信度区间和不同有效探测高度两种质量控制方案,并与固定有效探测高度方案进行了对比,结果表明,这两种质量控制方案皆具有明显优势。不同高可信度区间方案的质量控制效果更为显著,不同雷达站水平测风数据得到更加充分和有效识别,既减少了雷达资料不必要损失,又可将质量差的数据进一步剔除;该方案在有降水情形下也有较好效果。

    Abstract:

    With 0.5°×0.5° analysis fields of National Centers for Environmental Prediction/Global Forecast System (NCEP/GFS) as numerical forecast background and using surface precipitation data, quality characteristics of wind products from twelve L-band wind profiler radars in Fujian Province, including three CFL-03 radars and nine CFL-06 radars, were analyzed firstly at 00, 06, 12, and 18 UTC in a day from January to December 2017 aiming at data assimilation. Then different quality control (QC) schemes and their different effects were preliminary discussed. The results indicate that: (1) Winds detected by CFL-06 radars are obviously better than those from CFL-03 radars in the aspects of maximum detection height, effective data availability and horizontal wind quality in low levels. (2) Great differences exist in horizontal winds detected by different wind profiler radars with same model regarding its data availability, effective detection height, and vertical distribution of standard deviation, correlation coefficient and bias. These differences have no direct relationship with geographical location of wind profiler radars, i.e. coastal area or inland, and height above sea level. (3) The wind profiler radar products have obviously systematic negative bias relative to GFS u-wind field, that is, the u-winds detected by wind profiler radars are lower than GFS background field. This doesn’t meet the no-bias requirement for data assimilation. So bias corrections are necessary in data assimilation. Whereas v winds are relatively better than u wind. (4) Precipitation impacts greatly wind profiler radar detection. In precipitation days, the data availability reduces in middle-low levels but greatly enhances in middle-high levels. The standard deviations of u and v winds both increase in middle-low levels, whereas the standard deviations of v-winds increase and those of u-winds greatly reduce in middle-high levels. (5) Two QC schemes, i.e. different high-confidence range scheme and different effective detection-height scheme, are advanced up for different wind profiler radars to compare with fixed effective detection-height scheme. The results show that the two QC schemes both have obvious advantages. The QC effect of different high-confidence range scheme is much more obvious, with horizontal wind data of different radars more fully and effectively identified. The scheme does not only reduce unnecessary loss of radar data, but also further eliminates poor quality data. It also achieves good results in precipitation condition.

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  • 收稿日期:2019-09-19
  • 最后修改日期:2019-12-17
  • 录用日期:2020-04-23
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