Abstract
Abstract. Optimal estimation retrieval is a form of nonlinear regression which determines the most probable circumstances that produced a given observation, weighted against any prior knowledge of the system. This paper applies the technique to the estimation of aerosol backscatter and extinction (or lidar ratio) from two-channel Raman lidar observations. It produces results from simulated and real data consistent with existing Raman lidar analyses and additionally returns a more rigorous estimate of its uncertainties while automatically selecting an appropriate resolution without the imposition of artificial constraints. Backscatter is retrieved at the instrument's native resolution with an uncertainty between 2 and 20%. Extinction is less well constrained, retrieved at a resolution of 0.1–1 km depending on the quality of the data. The uncertainty in extinction is > 15%, in part due to the consideration of short 1 min integrations, but is comparable to fair estimates of the error when using the standard Raman lidar technique. The retrieval is then applied to several hours of observation on 19 April 2010 of ash from the Eyjafjallajökull eruption. A depolarising ash layer is found with a lidar ratio of 20–30 sr, much lower values than observed by previous studies. This potentially indicates a growth of the particles after 12–24 h within the planetary boundary layer. A lower concentration of ash within a residual layer exhibited a backscatter of 10 Mm−1 sr−1 and lidar ratio of 40 sr.
Highlights
Aerosols impact the Earth’s radiation budget both directly, by reflecting solar radiation back into space (Haywood and Shine, 1995), and indirectly, by altering the properties and distribution of clouds (Lohmann and Feichter, 2005) or reacting with other species (Colbeck, 1998)
That is clearly an unrealistic expectation but is a fair representation of the impact that dead time has on the observations for this system
An optimal estimation retrieval scheme for aerosol scatter- ratio. These possibilities were assessed by considering their ing properties from Raman lidar observations was proposed, ability to process simulated data
Summary
Aerosols impact the Earth’s radiation budget both directly, by reflecting solar radiation back into space (Haywood and Shine, 1995), and indirectly, by altering the properties and distribution of clouds (Lohmann and Feichter, 2005) or reacting with other species (Colbeck, 1998). The lack of knowledge about the global distribution and composition of aerosols is currently the single greatest source of uncertainty in estimates of net radiative forcing and is a factor in the ability to predict the impacts of climate change (IPCC, 2007). Despite its exceptionally high spatial and temporal resolution, lidar is not as widely applied as other techniques in the study of aerosol’s effect on climate as a single lidar samples only one location. With the launch of a space-based lidar (Winker et al, 2009) and the development of networks across northern America (Welton et al, 2000), Europe (Pappalardo et al, 2005), and Asia (Sugimoto et al, 2008), the importance and availability of lidar data increases
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