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

Lead Research Organisation: University of Manchester
Department Name: Earth Atmospheric and Env Sciences


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.

Planned Impact

National weather services (e.g. the Met Office) will benefit from this research, through improved representation of ice processes in the atmosphere, and how this should be represented in models. This benefit will be felt in both numerical weather forecasting and climate prediction, providing further benefits to the government, public and private sector organisations and businesses.

The project will benefit earth observation organisations and space agencies such as NASA, JAXA, ESA and EUMETSAT. This is because our data set and the subsequent analysis will provide crucial information for development of the retrievals for a number of space-based remote sensors including EarthCARE, GPM and ICI. The Met Office will also benefit in this regard through its contract to develop the ISMAR demonstrator - our data and results will help them interpret their data and retrieve accurate ice water paths in ice clouds.

Hydrological agencies will also benefit from the impacts of the proposed research. This includes governmental and international environment agencies with an interest in flood risk and hydrology. Our research will lead to improved forecasts of precipitation from ice clouds, which can impact on surface hydrological processes and forecasting flood risks.
Description We have developed a new data analysis technique using dual view/stereo imaging of cloud particles. This technique provides images of particles, as well as the position of the particle in the imaging system, which allows potential artefacts from being out of focus to be removed. When this analysis is done we see a significant alteration in our observations of cloud particle size distribution which demonstrate our previous datasets/understanding are wrong.
Exploitation Route Reanalysis of previous cloud particle imaging datasets required. New cloud particle imaging datasets required. New comparisons with numerical models for cloud, claimte and weather are needed in a variety of environments.
Sectors Environment

Title Cloud particle stereo imaging/analysis 
Description New analysis technique has been developed to extract dual view or "stereo" images from certain in-situ cloud particle imaging systems. This is the first successful implementation of this technique, and was used to investigate cloud particle microphysics from the UKRI-NERC FAAM research aircraft. 
Type Of Material Data analysis technique 
Year Produced 2021 
Provided To Others? Yes  
Impact The new techniques improves data quality by removing imaging artefacts which are a result of diffraction. 
Description Met Office collaboration 
Organisation Meteorological Office UK
Department Scottish Flood Forecasting Service
Country United Kingdom 
Sector Public 
PI Contribution Expertise, instrumentation and flight hours on the NERC FAAM aircraft.
Collaborator Contribution Expertise, instrumentation and flight hours on the NERC FAAM aircraft.
Impact One scientific publication currently under review.
Start Year 2018