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

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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.