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Exploiting multi-wavelength radar Doppler spectra to characterise the microphysics of ice hydrometeors

利用多波长雷达多普勒频谱来表征冰水凝物的微物理特征

基本信息

批准号：
NE/K012444/1
负责人：
Chris Westbrook
金额：
$ 21.47万
依托单位：
University of Reading
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FK012444%2F1
关键词：
Exploiting multi wavelength radar Doppler

项目摘要

The formation of ice particles in cold clouds is a vital component of the hydrological cycle. These particles redistribute water in the troposphere, as well as reflecting and absorbing visible and infrared light. Recent studies have shown that the evolution of these particles and the way in which they are distributed throughout a cloud layer is important if we are going to correctly simulate the earth's present and future climate. This evolution and distribution is a subject of considerable uncertainty however.Remote-sensing techniques such as radar are a powerful tool to probe the ice particles in natural clouds. The small amount of power reflected back to the radar by the ice particles contains information on the mass, shape and dimensions of the particles, while changes in the phase of the reflected wave contain information on how fast those particles fall (the Doppler spectrum).In this project we will develop a new technique to derive the properties of ice particles from radar measurements at 3 different wavelengths. While in the past many assumptions would need to be made a-priori when interpreting the radar data, the extra information content of the full Doppler spectrum at 3 wavelengths allows us to straightforwardly resolve these uncertainties.Once the technique has been developed, we can derive the microphysical properties of the cloud, such as the distribution of ice particles with size, the relationship between a particle's mass and its size, and how fast the particles fall as a function of their size. This information is key to the accurate representation of clouds and precipitation in numerical weather prediction and climate models, and the results will be used to validate/improve those models, in collaboration with the Met Office.We can also use the microphysical information to develop an improved understanding of the mechanisms by which ice particles grow and evolve in clouds, and use this to constrain currently-unknown parameters such as the aggregation efficiency ('stickiness') of natural ice crystals.

冷云中冰粒的形成是水文循环的重要组成部分。这些粒子重新分配对流层中的水，并反射和吸收可见光和红外光。最近的研究表明，如果我们要正确地模拟地球现在和未来的气候，这些粒子的演化和它们在云层中的分布方式是重要的。然而，这种演变和分布是一个相当不确定的主题。雷达等遥感技术是探测自然云中冰粒的有力工具。冰粒反射回雷达的少量能量包含有关冰粒的质量、形状和尺寸的信息，而反射波的相位变化包含有关这些粒子坠落的速度(多普勒谱)的信息。在这个项目中，我们将开发一种新的技术，从3个不同波长的雷达测量中得出冰粒的特性。虽然过去解释雷达数据时需要做出许多先验假设，但3个波长的全多普勒频谱的额外信息含量使我们可以直接解决这些不确定性。一旦技术得到发展，我们就可以推导出云的微物理性质，如冰粒的大小分布，粒子质量与其大小的关系，以及粒子作为其大小的函数下降的速度。这些信息是在数值天气预报和气候模式中准确表示云和降水的关键，结果将被用于验证/改进这些模型，与气象局合作。我们还可以利用微物理信息来更好地理解冰粒在云中生长和演化的机制，并利用这些信息来约束目前未知的参数，如天然冰晶的聚集效率(粘性)。