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Quality Control of Cloud Derived Wind Vectors from Geostationary Meteorological Satellites with Its Application to Data Assimilation System
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摘要: 静止卫星云导风的同化一直以来是卫星资料同化中亟待解决的问题之一，其质量控制是关键。该文针对全球静止卫星云导风的红外、水汽和可见光通道在空间区域的误差特点确定相应的QI阈值，进行了质量控制和等距稀疏化工作，并考察了质量控制前后云导风数量的垂直分布特征。研究表明:质量控制后，红外通道云导风占绝大多数，水汽通道次之，可见光通道最少。最后，文章设计了3个试验方案进行数值模拟，对比分析了云导风在质量控制前后对预报的改进效果，结果表明：加入经质量控制的云导风资料后能有效减小分析场在中高层的误差；经过质量控制后的云导风对全球中短期数值预报 (1~5 d) 有明显的正预报效果。Abstract: Cloud derived wind vectors can provide plenty of useful information for synoptic analysis and numerical weather prediction, because of their excellent spatial and temporal coverages. It is of great value to apply cloud derived wind vectors efficiently with the purpose of improving the initial fields and numerical forecasts. The quality control of cloud derived wind vectors from geostationary meteorological satellites has been one of the important and difficult problems to be solved in satellite data assimilation. It has a direct impact on the prediction level of numerical weather prediction model. On the basis of the statistical analysis of global cloud derived wind vectors for 14 months, the quality indicator threshold of the five channels of cloud derived wind vectors in tropics, north and south hemisphere extra-tropics in high, middle and low levels from five global geostationary meteorological satellites in business operation, is analyzed and calculated. The error characteristics of infrared, water vapor and visible channels in the space area of cloud derived wind vectors are investigated, and the corresponding QI thresholds are determined. On the basis of quality control and equal-distance thinning scheme, the vertical distribution characteristics of cloud derived wind vectors are analyzed. It shows that after quality control, the number of infrared channel accounts for the vast majority, water vapor channel follows, and visible light channel the least.Furthermore, the method of innovation vector and zero-order Bessel fitting function which is based on the theory of least square method are adopted together to partition background and observation error variances in the observation space, and thus estimate the observation error of cloud derived wind vectors and the quality control coefficient according to the statistical distribution of the innovation vectors. On this basis, in order to validate the assumption on uncorrelated observation errors required in three-dimensional variational method, observation errors are inflated to avoid the influence carried by correlated errors.Finally, three numerical simulation schemes are designed and the forecast improvement and impact of cloud derived wind vectors before and after quality control are analyzed. Results show that the cloud derived wind vectors after quality control can effectively reduce the error of analysis field at high level. And the global short-term and medium-term forecast ability can be improved obviously by assimilating cloud derived wind vectors after quality control. In particular, there is a clear improvement in forecast ability in terms of wind, geopotential height and temperature fields in tropics. The forecast improvement above high levels appears better than those of middle and low levels in northern and southern hemisphere extra-tropics.
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图 1 赤道外北半球MTSAT-2R云导风u分量与探空的平均偏差与标准差
Fig. 1 The mean and standard deviation for the u-component speed bias of MTSAT-2R cloud derived wind vectors against radiosonde observations in the Northern Hemisphere extra-tropics
图 2 云导风数量的垂直分布廓线
(a) 质量控制前，(b) 质量控制后
Fig. 2 Vertical distribution profile for the number of cloud derived wind vectors
(a) before quality control, (b) after quality control
图 3 分析场风场u分量与探空风场的偏差的垂直分布
Fig. 3 Vertical distribution for the u-component speed bias of analysis wind field against radiosonde observations
表 1 云导风QI阈值 (单位：%)
Table 1 QI thresholds for cloud derived wind vectors (unit:%)
卫星 通道 高层 中层 低层 NH TR SH NH TR SH NH TR SH GOES-11 IR 50 70 50 60 50 VIS WV3 WV5 90 50 50 85 75 WV7 GOES-13 IR 50 50 50 70 VIS 95 95 WV3 WV5 50 50 96 WV7 NETEOSAT-9 IR 95 79 98 96 78 91 81 VIS WV3 96 80 98 98 84 WV5 79 82 86 85 86 92 95 96 WV7 FY-2E IR 94 94 94 96 VIS WV3 WV5 WV7 89 92 93 95 MTSAT-2R IR 94 75 95 96 85 85 84 85 VIS 85 75 84 WV3 94 80 93 WV5 94 84 WV7 
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表 2 各层云导风的观测误差均方差
Table 2 Observation errors of cloud derived wind vectors at each level
层次/hPa 云导风高度范围/hPa 观测误差均方差/(m·s-1) 1000 (925, 1000] 4.6 850 (775, 925] 5.4 700 (600, 775] 6.2 500 (450, 600] 6.8 400 (350, 450] 7.2 300 (275, 350] 7.8 250 (225, 275] 6.3 200 (175, 225] 6.6 150 (125, 175] 7.3 100 [75, 125] 6.6
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