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Volcanic Ash Cloud Observation using Ground-based Ka-band Radar and Near-Infrared Lidar Ceilometer during the Eyjafjallajökull eruption

机译:使用地面的KA波段雷达和近红外LIDAR Ceirometer爆发的火山灰云观察

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摘要

Active remote sensing techniques can probe volcanic ash plumes, but their sensitivity at a given distance depends upon the sensor transmitted power, wavelength and polarization capability. Building on a previous numerical study at centimeter wavelength, this work aims at i) simulating the distal ash particles polarimetric response of millimeter-wave radar and multi-wavelength optical lidar; ii) developing and applying a model-based statistical retrieval scheme using a multi-sensor approach. The microphysical electromagnetic forward model of volcanic ash particle distribution, previously set up at microwaves, is extended to include non-spherical particle shapes, vesicular composition, silicate content and orientation phenomena for both millimeter and optical bands. Monte Carlo generation of radar and lidar signatures are driven by random variability of volcanic particle main parameters, using constraints from available data and experimental evidences. The considered case study is related to the ground-based observation of the Eyjafjallajökull (Iceland) volcanic ash plume on May 15, 2010, carried out by the Atmospheric Research Station at Mace Head (Ireland) with a 35-GHz Ka-band Doppler cloud radar and a 1064-nm ceilometer lidar. The detection and estimation of ash layer presence and composition is carried out using a Bayesian approach, which is trained by the Monte Carlo model-based dataset. Retrieval results are corroborated exploiting auxiliary data such as those from a ground-based microwave radiometer also positioned at Mace Head.
机译:有源遥感技术可以探测火山灰羽毛,但它们在给定距离的灵敏度取决于传感器透射功率,波长和偏振能力。在厘米波长的先前数值研究中构建,这项工作目的是i)模拟毫米波雷达的远端灰分粒子响应和多波长光学利达; ii)使用多传感器方法开发和应用基于模型的统计检索方案。先前在微波上设置的火山灰颗粒分布的微神科电磁前向模型延伸到包括非球形颗粒形状,尿布组合物,毫米和光带的硅酸盐含量和取向现象。 Monte Carlo产生雷达和激光雷达签名是由Volcanic粒子主要参数的随机可变性驱动的,使用来自可用数据和实验证据的限制。被审议的案例研究与2010年5月15日的Eyjafjallajökull(冰岛)火山灰羽流的基于地面观察有关,由Mace Head(爱尔兰)的大气研究站进行了一个35 Ghz Ka带多普勒云进行雷达和1064纳米CeiLometer Lidar。使用贝叶斯方法进行灰分地存在和组合物的检测和估计,其由蒙特卡罗模型的数据集接受训练。检索结果是证实利用辅助数据,例如来自地面微波辐射计的辅助数据,也定位在术前头部。

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