The radiative energy balance of the Earth is controlled by aerosols, clouds and gases, which scatter and absorb incident solar radiation and emitted infra-red radiation. Climate models simulate this process, along with the dynamical energy redistribution of the atmosphere and oceans. In the last decade, climate models have become increasingly accurate as they include complex processes in a more physical manner. However, the climatic effect of one component, atmospheric aerosols, continues to remain highly uncertain. Aerosols are short lived airborne particulate matter of both natural and anthropogenic (human) origin that have complex interactions with atmospheric radiation, clouds, and chemistry. A complete understanding of aerosols is limited by the inability of satellite remote sensing instruments to consistently measure all of the aerosol optical parameters that are required to define their radiative effects. This is mainly because the retrieval of these parameters is often underdetermined, and because the aerosol optical signal is difficult to separate from other signals, such as surface reflection. However, a new class of instruments, called scanning polarimeters, have the potential to vastly improve orbital retrieval of aerosol optical properties. These instruments use multiple angles, spectral bands and polarization states, to provide measurements with maximized information content that can differentiate aerosols from other scatterers.
This research is an investigation of the aerosol retrieving potential of scanning polarimeters that uses data collected by the Research Scanning Polarimeter (RSP) during several field campaigns. The RSP is the airborne prototype of the Aerosol Polarimetry Sensor (APS), soon to be launched into orbit as part of the NASA Glory orbital mission. Field campaign data have already been used to verify the capability of RSP (and APS) to retrieve aerosol properties in cloudless areas over the ocean and land surfaces. Here I have continued that work, and investigate the potential for aerosol property retrieval in more complicated scenes, such as aerosols lofted above clouds or extremely large aerosol loads near forest fires. As part of this, I constructed an automated aerosol and cloud retrieval technique that combines a first-principles based atmospheric radiative transfer model with the Levenberg-Marquardt nonlinear optimization approach. This method retrieves optical parameters and quantifies their uncertainties, which provide an assessment of optimization success. The software is very flexible, and was also used to determine the sensitivity of the RSP measurements to various parameters that determine atmospheric radiative transfer.
The retrieval software is applied to investigate two scenarios. The first analysis is of data collected near an extremely optically thick and weakly absorbing forest fire smoke (aerosol) plume in northern Canada. This well characterized plume is used to test the importance of assumptions about aerosol vertical distribution during retrieval. It also allows for an evaluation of simple approaches to merging aerosol vertical profile data from remote sensing instruments. A second study is performed for aerosols suspended above clouds, evaluating the theoretical feasibility of these observations, and then applying the method to data from a particular field campaign. This scene involved aerosols originating in central Mexico suspended over low altitude marine stratocumulus clouds in the Gulf of Mexico. These studies will guide both operational APS algorithms and the design of future aerosol remote sensing instruments.
An additional component of this thesis is an examination of the capability of RSP and APS instruments to estimate the surface bidirectional reflectance distribution function (BRDF) across the solar spectrum, and therefore constrain the surface radiation balance. The retrieved BRDF can also be used to validate similar products from other instruments that will have higher spatial resolution and global coverage than APS, but poorer angular sampling, such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The BRDF and broadband albedo are estimated using RSP data collected in central Oklahoma, and a good agreement is found with both direct surface measurements and MODIS remote sensing observations.