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Use of Meteosat Second Generation optimal cloud analysis fields for understanding physical attributes of growing cumulus clouds

机译:使用Meteosat第二代最佳云分析字段来了解生长的积云的物理属性

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This study develops an understanding on how retrieved cloud parameter fields from the Optimal Cloud Analysis (OCA) algorithm, operating on Meteosat Second Generation (MSG), Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, behave at 5-min time resolutions for growing cumulus clouds. Fields retrieved by the OCA algorithm include cloud optical thickness (t), cloud-top particle effective radius (r_e), cloud-top pressure (p_c), and cloud-top phase. OCA is based on a one-dimensional optimal estimation methodology, and a measure of radiance fit, the cost function (J_m). is a quantity developed as part of the retrieval process and is shown to be useful in delineating mixed phase clouds; it too is evaluated (at 5-min intervals) for the information it provides. Data for 94 growing cumulus cloud events are processed. An "event" is defined as a cumulus cloud that is monitored at 5-min intervals with OCA, as it grows from the "fair weather" or "towering cumulus" stage to near the cumulonimbus stage when precipitation begins. The hypothesis is that OCA products are of high-enough quality to provide unique information about microphysical processes occurring at and near cloud top. The goal through analysis of the 94 events is to identify consistent, repeating patterns in OCA fields during cloud growth that can be in turn used to infer physical processes. Data from the Convective and Orographically-induced Precipitation Study (June and July 2007) and in four regions of Europe on 25 May 2009 are used. The validity of the OCA data is presented with a comparison to CloudSat Precipitation Radar and MODerate resolution Imaging Spectroradiometer retrieved cloud properties, showing good statistical agreements. Subsequently, results from the analysis of OCA fields for all events show that as cumuli deepen, r_e values tend to increase, and then decrease in size as cloud tops glaciate and particle settling begins. The t magnitudes generally increase as clouds deepen, while p_c values and cloud-top temperatures fall as expected. The J_m values exhibit the pattern of spiking in magnitude (over a 5-10-min period), which indicates the increase "misfit" within OCA during the mixed phase, at about the time t values increase substantially as clouds deepen.
机译:这项研究使人们了解了如何从优化云分析(OCA)算法中检索到的云参数字段,在Meteosat第二代(MSG)上运行,旋转增强型可见光和红外成像仪(SEVIRI)数据,以5分钟的时间分辨率生长的行为积云。通过OCA算法检索的字段包括云光学厚度(t),云顶粒子有效半径(r_e),云顶压力(p_c)和云顶相位。 OCA基于一维最佳估计方法,以及辐射适合度的度量,即成本函数(J_m)。是作为检索过程的一部分而开发的量,被证明可用于描绘混合相云;也将对其(每隔5分钟)对其提供的信息进行评估。处理了94个正在增长的积云事件的数据。 “事件”定义为用OCA每隔5分钟监视一次的积云,因为它从“晴天”或“塔状积云”阶段发展到开始降雨时的积雨云阶段附近。假设是,OCA产品具有足够高的质量,可以提供有关在云顶及其附近发生的微物理过程的独特信息。通过分析94个事件,目标是在云生长期间在OCA字段中确定一致的重复模式,从而可以用来推断物理过程。使用了对流和地形诱发的降水研究(2007年6月和2007年7月)以及2009年5月25日在欧洲四个地区的数据。 OCA数据的有效性通过与CloudSat降水雷达和MODerate分辨率成像分光光度计检索到的云特性的比较进行了介绍,显示出良好的统计一致性。随后,对所有事件的OCA场的分析结果表明,随着积云的加深,r_e值趋于增加,然后随着云层顶部冰层和颗粒沉降的开始而减小。 t值通常随着云的加深而增加,而p_c值和云顶温度则按预期下降。 J_m值表现出幅度的峰值模式(在5-10分钟内),这表明在混合阶段OCA内的“失配”增加,大约在时间t值随着云的加深而大大增加。

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