Statistical estimation of stratospheric particle size distribution by combining optical modelling and lidar scattering measurements

Abstract

Abstract.A method for estimating the stratospheric particle size distribution from multiwavelength lidar measurements is presented. It is based on matching measured and modelsimulated backscatter coefficients. The lidar backscatter coefficients measured at the three commonly used wavelengths 355, 532 and 1064 nm are compared to a precomputed lookup table of model-calculated values. The optical model assumes that particles are spherical and that their size distribution is unimodal. This inverse problem is not trivial because the optical model is highly non-linear with a strong sensitivity to the size distribution parameters in some cases. The errors in the lidar backscatter coefficients are explicitly taken into account in the estimation. The method takes advantage of the statistical properties of the possible solution cluster to identify the most probable size distribution parameters. In order to discard model-simulated outliers resulting from the strong non-linearity of the model, solutions farther than one standard deviation of the median values of the solution cluster are filtered out, because the most probable solution is expected to be in the densest part of the cluster. Within the filtered solution cluster, the estimation algorithm minimizes a cost function of the misfit between measurements and model simulations.Two validation cases are presented on Polar Stratospheric Cloud (PSC) events detected above the ALOMAR observatory (69 • N -Norway). A first validation is performed against optical particle counter measurements carried out in January 1996. In non-depolarizing regions of the cloud (i.e. spherical particles), the parameters of an unimodal size distribution and those of the optically dominant mode of a bimodal size distribution are quite successfully retrieved, especially for the median radius and the geometrical standard deviation. As expected, the algorithm performs poorly when Correspondence to: J. Jumelet (julien.jumelet@aero.jussieu.fr) solid particles drive the backscatter coefficient. A small bias is identified in modelling the refractive index when compared to previous works that inferred PSC type Ib refractive indices. The accuracy of the size distribution retrieval is improved when the refractive index is set to the value inferred in the reference paper.Our results are then compared to values retrieved with another similar method that does not account for the effect of the measurements errors and the non-linearity of the optical model on the likelihood of the solution. The case considered is a liquid PSC observed over northern Scandinavia on January 2005. An excellent agreement is found between the two methods when our algorithm is applied without any statistical filtering of the solution cluster. However, the solution for the geometrical standard deviation appears to be rather unlikely with a value close to unity (σ ≈1.04). When our algorithm is applied with solution filtering, a more realistic value of the standard deviation (σ ≈1.27) is found. This highlights the importance of taking int...

Size distribution time series of a polar stratospheric cloud observed above Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) (69°N) and analyzed from multiwavelength lidar measurements during winter 2005

“…Because of these two limitations, the regularization technique is not necessarily the most suited approach for retrieving stratospheric particle size distributions from lidar data. A semiempirical approach to retrieve the particle size distribution from three commonly measured particle backscatter has recently been developed and validated [ Jumelet et al , 2008]. This size distribution retrieval method was inspired by the works of Beyerle et al [1994], and Mehrtens et al [1999].…”

Subgrid-scale cirrus observed by lidar at mid-latitude: variability effects of the cloud optical depth

“…20,21 For a given cirrus cloud, those parameters vary over the three spatial dimensions due to small-scale water vapor structures and variability in time. Despite the difficulty to deduce microphysical properties from lidar measurements, 22 the vertical structure, as well as its temporal evolution associated with air masses passing over a given location, is well described by a ground-based lidar.…”

“…4). Combined measurements at two wavelengths 22 such as lidar and mid-infrared radiometry measurements, to obtain the cloud optical depth distribution and to estimate solar albedo, show biases as large as 25%.…”

Improved stratospheric aerosol extinction profiles from SCIAMACHY: validation and sample results

“…Measurement techniques employed to study stratospheric aerosol parameters include in situ particle sampling, e.g., by optical means (e.g., Deshler, 2008), single or multiwavelength LIDAR observations (e.g., Jumelet et al, 2008;Hofmann et al, 2009), satellite solar (e.g., Thomason, 1991;McCormick and Veiga, 1992;Lumpe et al, 1997) or stellar (e.g., Vanhellemont et al, 2010) occultation measurements, as well as satellite observations of limb-scattered solar radiation (Bourassa et al, 2007;Taha et al, 2011;Ovigneur et al, 2011;Ernst et al, 2012).…”