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气象:2017,43(2):213-220
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高光谱红外云检测方案阈值的客观判定方法
(1.南京信息工程大学,南京 210044;2.国家气象中心,北京 100081)
Objective Determination Scheme of Threshold in High Spectral Resolution Infrared Cloud Detection
(1.Nanjing University of Information Science and Technology, Nanjing 210044;2.National Meteorological Centre, Beijing 100081)
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投稿时间:2015-12-14    修订日期:2016-10-27
中文摘要: GRAPES 3Dvar大气红外高光谱云检测采用McNally and Watts(2003)的晴空通道方案,该方案基于模拟观测与实际观测偏差(B-O)进行检测,其中阈值的选取与预报模式有关。传统对云检测阈值的选取多采用主观判定方法,如图像对比分析等。文章在主观判定的基础上提出两种新的较为客观的阈值判定方法:(1)视场偏差分析:通过分析模拟观测与实际观测之间偏差的垂直分布,观察不同阈值下的云顶高度来评估阈值选取的合理性;(2)云检测指标评分:通过建立一系列云检测指标,对其进行评分来选取最优的阈值。并结合上述三种方法,将其应用于IASI辐射率资料的云检测阈值判定,其中梯度阈值对最后的云检测效果影响不大,保持与欧洲中心相同的值;偏差阈值则采用综合的分析方法进行判定,从而克服了单纯利用云图对比而带来的视觉误差。分析结果表明,偏差阈值取2.0 K、窗区通道梯度阈值取0.4 K,非窗区通道梯度阈值取0.02 K 时云检测的效果最好。最后进行的为期1个月的试验显示:通过对比不同云检测偏差阈值的同化效果,选取的云检测阈值是有效的。
Abstract:The clear channel scheme presented by McNally and Watts (2003) is used to detect cloud radiances in GRAPES 3Dvar system measured by high spectral resolution infrared sounders. This scheme is based on the bias of simulated radiances and actual radiances, and the determination of bias is related to forecast model. Traditionally, we use subjective determination schemes to select threshold in cloud detection, such as image contrast analysis. This paper presents two new and objective schemes based on the subjective scheme. (1) Analysis of view field bias: observe the cloud top pressure under different thresholds to evaluate the rationality by analyzing the vertical distribution of bias between simulated observation and actual observation. (2) Evaluation of cloud detection index: establish a series of cloud evaluation indices to select appropriate thresholds. By combining the three methods mentioned above, we determine the IASI radiances cloud detection. The gradient threshold keeps the same value as that from European Center for it has a weak effect in cloud detection. The bias threshold is determined by a comprehensive analysis method, thus overcoming the visual error of the pure use of image. The results show that bias threshold selects 2.0 K, gradient threshold selects 0.4 K for window channels and 0.02 K for non window channels. Finally, a month long experiment shows that the cloud detection bias threshold is effective by comparing the assimilation results.
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基金项目:公益性行业(气象)科研专项(GYHY201206002和GYHY201506003)共同资助
引用文本:
邓松,李刚,张华,2017.高光谱红外云检测方案阈值的客观判定方法[J].气象,43(2):213-220.
DENG Song,LI Gang,ZHANG Hua,2017.Objective Determination Scheme of Threshold in High Spectral Resolution Infrared Cloud Detection[J].Meteor Mon,43(2):213-220.