Earth Observation Data Group

Abstract:

Lidars are ideally placed to investigate the effects of aerosol and cloud on the climate system due to their unprecedented vertical and temporal resolution. Dozens of techniques have been developed in recent decades to retrieve the extinction and backscatter of atmospheric particulates in a variety of conditions. These methods, though often very successful, are fairly ad hoc in their construction, utilising a wide variety of approximations and assumptions that makes comparing the resulting data products with independent measurements difficult and their implementation in climate modelling virtually impossible. As with its application to satellite retrievals, the methods of non-linear regression can improve this situation by providing a mathematical framework in which the various approximations, estimates of experimental error, and any additional knowledge of the atmosphere can be clearly defined and included in a mathematically ‘optimal’ retrieval method, providing rigorously derived error estimates. In addition to making it easier for scientists outside of the lidar field to understand and utilise lidar data, it also simplifies the process of moving beyond extinction and backscatter coefficients and retrieving microphysical properties of aerosols and cloud particles. Such methods have been applied to a prototype Raman lidar system. A technique to estimate the lidar’s overlap function using an analytic model of the optical system and a simple extinction profile has been developed. This is used to calibrate the system such that a retrieval of the profile extinction and backscatter coefficients can be performed using the elastic and nitrogen Raman backscatter signals.

Abstract:

Extratropical cyclones may have a significant effect on column aerosol properties over ocean. European Centre for Medium Range Weather Forecasts (ECMWF) derived storm-centric composites of Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Along-Track Scanning Radiometer (AATSR) aerosol optical depth and aerosol size parameters are produced for the North Atlantic and the South Atlantic oceans. It is found that retrieved aerosol optical depth and aerosol size both increase near the center of the composite extratropical cyclones. Using composites of ECMWF ERA-Interim reanalysis data, it is demonstrated that wind speed is a considerably more likely explanatory variable than relative humidity for the aerosol observations. A comparison of composites for both MODIS and AATSR, which uses a wind speed dependent sea-surface brightness model in the aerosol retrieval, suggests that although surface brightness effects may contribute towards some of the observations, wind speed dependent emission of sea salt also appears to make a significant contribution to the observed aerosol properties. © 2011 by the American Geophysical Union.

Abstract:

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT has the potential to be particularly useful for studying high, thin clouds, which have been difficult to observe in the past. This paper details the development, implementation and testing of an optimal-estimation- type retrieval for three macrophysical cloud parameters (cloud top height, cloud top temperature and cloud extinction coefficient) from infrared spectra measured by MIPAS. A preliminary estimation of a parameterisation of the optical and geometrical filling of the measurement field-of-view by cloud is employed as the first step of the retrieval process to improve the choice of a priori for the macrophysical parameters themselves. Preliminary application to single-scattering simulations indicates that the retrieval error stemming from uncertainties introduced by noise and by a priori variances in the retrieval process itself is small - although it should be noted that these retrieval errors do not include the significant errors stemming from the assumption of homogeneity and the non-scattering nature of the forward model. Such errors are preliminarily and qualitatively assessed here, and are likely to be the dominant error sources. The retrieval converges for 99% of input cases, although sometimes fails to converge for vetically-thin (<1 km) clouds. The retrieval algorithm is applied to MIPAS data; the results of which are qualitatively compared with CALIPSO cloud top heights and PARASOL cloud opacities. From comparison with CALIPSO cloud products, it must be noted that the cloud detection method used in this algorithm appears to potentially misdetect stratospheric aerosol layers as cloud. This algorithm has been adopted by the European Space Agency's "MIPclouds" project. © Author(s) 2011.