A cloud parcel is a suspension of particles that form from condensation of water onto hydrophilic aerosol particles. At critical supersaturation when the radius of the particle reaches its critical radius the particle becomes a cloud particle. Super saturation is generated within an air parcel through radiative cooling or adiabatic cooling during convective, frontal or orthographic uplift. Cloud droplets can exist as either liquid water or ice while cloud parcels may contain either liquid water droplets or ice particles or both.
Clouds dominate the planet's energy budget and exert an enormous influence on the Earth's climate system. By scattering incident shortwave solar radiation back to space they exert a cooling effect. They exert a warming effect by absorbing thermal emission from the Earth's surface and lower atmosphere and emitting at cooler temperatures to space. These processes effect atmospheric circulation which in turn affects weather systems and climate processes . Clouds and storm systems affect the vertical transport in the atmosphere. This is important in the redistribution of trace species and latent heat, released when vapour condenses to form water particles. Clouds also provide a medium for aqueous phase chemistry and help to return aerosol to the surface through precipitation. Finally, clouds are a significant part of the hydrological cycle and necessary for redistributing water that is essential for life on Earth.
One of the most challenging problems confronting scientist is the prediction of climate change and in particular its anthropogenic component. Unfortunately, the lack of knowledge related to the response of clouds to natural and anthropogenic forcings makes it a dominate uncertainty in predicting future climate change . Despite the fundamental role clouds have on the climate system there is much we do not know about them. Their complexity at both micro- and macro- physical scales makes them difficult to measure and to predict. In addition, complex feed-backs between radiation, clouds and precipitation must also be understood to accurately represent clouds in numerical weather and climate prediction models.
It is clear that numerical models will play a central role in future climate studies. An essential part of developing these models is evaluating their performance and it is with measurements of cloud distribution and properties that they are validated. In addition, through data assimilation methods cloud measurements provide valuable constraints on the predictions made by models. Measurements can be made both in situ and remotely from the ground or from space but satellite based measurements are the only way to measure cloud properties at the spatial and temporal scales required to accurately validate and constrain numerical prediction.
Satellite measurement methods are now mature enough and their timeseries long enough that it is possible to produce meaningful measurements and climatologies for researchers to use. Unfortunately, significant improvement is required to reduce the uncertainty of these remote measurements. At the heart of the problem is the radiative transfer equation  which relates physical properties to the observed radiances. The physics involved is highly non-linear while the inversion of this equation to retrieve cloud properties is a classically ill-posed and under constrained problem. Properties of interest to researchers include microphysical properties such as thermodynamic phase, particle size and shape distribution, and particle temperature while macrophysical properties of interest include liquid and/or ice content and cloud horizontal and vertical extent. With satellite measurements it is not possible to retrieve a full set of properties and as a result assumptions must be made in the inversion process. At the EODG we rely heavily on inversion algorithms based on the optimal estimation technique  and put significant emphasis on maintaining radiatively consistent retrievals and providing a robust estimate of the retrieval uncertainty.
Instruments used to make radiometric measurements broadly fall into two categories. (1) multispectral passive instruments that measure radiation from a source external to the instrument such as solar radiation or thermal emission (AVHRR, ATSR, IASI, MODIS, MISR, POLDER VIIRS, SEVIRI, etc.). (2) Active instruments such as RADAR and LIDAR that transmit there own radiation and measure the return signal (CloudSat or CALIPSO). Each type has its strength and weaknesses with variations in configuration such as the number of channels and spectral and spatial resolution. In addition, more advanced measurements may be made that include multiple view angles and/or polarization state. At the EODG we use both passive and active measurements from a wide range of instruments.
Within the EODG our interests include
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