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Parameterizing ice clouds using airborne observations and triple-frequency doppler radar data

使用机载观测和三频多普勒雷达数据参数化冰云

基本信息

批准号：
NE/P012426/1
负责人：
Jonathan Crosier
金额：
$ 77.65万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FP012426%2F1
关键词：
Parameterizing ice clouds using airborne

项目摘要

Ice clouds have an important role in the atmosphere, influencing radiative transfer and precipitation formation. The global climatic impact of all clouds types is estimated as a cooling effect. This net cooling effect results from the opposing impacts from liquid clouds (which cool by reflecting sunlight back into space) and ice clouds (which warm through a "greenhouse" effect). Unfortunately there is a lack of understanding of many of the physical processes occurring in ice clouds due to the complexity of ice particle processes and interactions between atmospheric motions, water vapour, and aerosol particles. This means ice clouds are a source of significant uncertainty in climate simulations, and can lead to errors in weather forecasts. Establishing which models are the most accurate remains difficult due to the lack of observations of ice cloud properties. Remote sensing techniques (e.g. radar) can provide observations of ice clouds over large areas on a continuous basis, making them ideal for assessing model skill. However, these techniques do not typically directly measure the atmospherically relevant quantity (e.g. mass of condensed water in a volume of air), and a retrieval must be used to obtain comparable data. These retrievals must invoke several assumptions about the properties of the ice clouds, properties which in reality are highly uncertain. We propose a project to collect a new dataset using in-situ observations from a research aircraft to directly observed ice cloud properties. At the same time, three different radars operating at difference wavelengths will scan the same clouds to allow a variety of radar retrievals to be developed and evaluated. We will obtain data during overpasses from a variety of different satellites which observe ice clouds from space. This work will improve fundamental understanding of ice cloud properties, and lead to improved remote sensing retrievals, both of which will lead to improved model accuracy and reduced uncertainty.

冰云在大气中起着重要作用，影响辐射传输和降水形成。所有云类型对全球气候的影响估计为冷却效应。这种净冷却效应是由液态云（通过将阳光反射回太空而冷却）和冰云（通过“温室”效应变暖）的相反影响造成的。不幸的是，由于冰粒子过程的复杂性以及大气运动、水蒸气和气溶胶粒子之间的相互作用，人们对冰云中发生的许多物理过程缺乏了解。这意味着冰云是气候模拟中显著不确定性的来源，并可能导致天气预报的错误。由于缺乏对冰云特性的观测，确定哪些模型是最准确的仍然很困难。遥感技术（如雷达）可以提供对大面积冰云的连续观测，使其成为评估模型技术的理想工具。然而，这些技术通常不直接测量大气相关量（例如，一定体积的空气中冷凝水的质量），并且必须使用检索来获得可比数据。这些检索必须调用几个假设的性质的冰云，在现实中是非常不确定的属性。我们提出了一个项目，收集一个新的数据集，从研究飞机直接观察到冰云特性的现场观测。与此同时，在不同波长下工作的三个不同雷达将扫描相同的云层，以便开发和评估各种雷达检索。我们将在立交桥期间从各种不同的卫星上获得数据，这些卫星从太空观测冰云。这项工作将提高对冰云特性的基本认识，并改进遥感检索，这两者都将提高模型的准确性，减少不确定性。