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Impact of a consistent height reassignment method for INSAT derived cloud motion vectors

机译:一致的高度重分配方法对INSAT导出的云运动矢量的影响

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Cloud Motion Vector wind (CMV) data is one of the major products derived from current day operational geostationary satellites and continues to be a useful source of information for global data assimilation systems. The CMV fields result basically from the apparent cloud motion estimated through a sequence of very high resolution radiometer (VHRR) images and the corresponding cloud level is inferred from observed brightness temperatures (BT) in the infrared window channel. The main problem in CMV production is associated with the assignment of correct height to the cloud tracked motions in view of the known limitations associated with the cloud emissivity characteristics and estimation of BTs. Despite these inherent uncertainties satellite derived CMVs are widely used to augment the conventional upper air wind data especially over the oceanic regions. In this study, an attempt is made to improve the quality of INSAT derived CMVs through an appropriate height re-assignment by employing first guess fields (short range forecasts) of a global forecast model. Two experimental height re-assignment methods (EXP-I: Taking first-guess height field into consideration; EXP-II: Taking first-guess height and wind fields into consideration) for correcting assigned heights of CMVs are employed to examine the consistency of the fields. An objective assessment of these reassignment methods is carried out through verification of CMVs against collocated Radiosonde/Rawinsonde (TEMP) and Pilot Balloon (PILOT) wind reports received through Global Telecommunication System (GTS) at New Delhi for a period of 1–21 November, 1996. The collocation statistics clearly show a relative improvement in the quality of CMVs EXP-II. The assimilation-forecast cycle is repeated by subjecting directly derived CMVs to EXP-II method for whole of the above 21-day period and the impact of improved CMVs is examined in terms of objective skill scores (RMSE, Anomaly Correlation Coefficient). The impact of the improved CMVs in the synoptic scales is further examined by conducting assimilation experiments for two typical cases of tropical circulation systems formed over the Bay of Bengal during December 1996 and May 1997, respectively.
机译:云运动矢量风(CMV)数据是从当今运行的对地静止卫星中获取的主要产品之一,并且仍然是全球数据同化系统的有用信息来源。 CMV场基本上是由通过一系列非常高分辨率的辐射计(VHRR)图像估算的视云运动产生的,并且从红外窗口通道中观察到的亮度温度(BT)可以推断出相应的云水平。鉴于与云发射率特性和BT的估计有关的已知限制,CMV生产中的主要问题与为云跟踪的运动分配正确的高度有关。尽管存在这些固有的不确定性,但衍生自卫星的CMV被广泛用​​于增强常规的高空风数据,尤其是在海洋区域。在这项研究中,通过使用全局预测模型的第一个猜测字段(短程预测),尝试通过适当的高度重新分配来提高INSAT衍生的CMV的质量。为了校正CMV的指定高度,采用了两种实验性的高度重新分配方法(EXP-I:考虑到第一猜测的高度场; EXP-II:考虑到第一猜测的高度和风场)来校正CMV的分配高度。领域。通过针对11月1日至21日通过新德里的全球电信系统(GTS)接收到的并列的探空仪(Ra)和探空仪(TEMP)和飞行员气球(PILOT)风报告进行验证,对CMV进行了客观评估, 1996年。并置统计数据清楚地表明,CMV EXP-II的质量相对有所提高。在上述21天的整个时间段内,通过对直接派生的CMV进行EXP-II方法重复进行同化-预测循环,并根据客观技能得分(RMSE,异常相关系数)检查改进的CMV的影响。通过对分别在1996年12月和1997年5月在孟加拉湾形成的两个热带环流系统的典型案例进行同化实验,进一步检验了改进的CMV在天气尺度上的影响。

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