Type I PSC‐particle properties: Measurements at ALOMAR 1995 to 1997

Mehrtens, Hela; Zahn, U. von; Fierli, F.; Nardi, Bruno; Deshler, Terry

doi:10.1029/1999gl900027

Cited by 13 publications

(11 citation statements)

References 10 publications

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“… Stein et al [1999] observed Type Ib during 0100–0600 UT of 17 January at altitudes of 21.5–24 km, and mixtures of Type Ia and Ib during 1400–2300 UT of 19 January 1997 at the altitude range of 20–23 km at Sodankyla, Finland (67°N, 26°E). Mehrtens et al [1999] obtained a better agreement with NAT rather than STS for PSC layers observed at Andenes, Norway (69°N, 16°E) from 18 to 24 km on 19 January 1997. Reichardt et al [2000] observed PSC Ib near 22 km and PSC Ia near 24 km at Esrange, Sweden (67.9°N, 21.1°E) for two hours from 1700 UT on 20 January 1997.…”

Section: Comparison With Other Reportsmentioning

confidence: 74%

“…It is assumed that the PSC size distribution can be represented by a single mode lognormal distribution given by the expression where N is the total number density (particles cm −3 ), R is the median radius (μm), and σ is the geometric mean standard deviation (dimensionless) of r . The single mode distribution has been used in the analysis of lidar measurements of PSCs (see similar study by Dye et al [1992], Mehrtens et al [1999], and Toon et al [2000]). As noted above, the ratio of the theoretical extinction coefficients at two wavelengths, λ i and λ j , is defined as the theoretical relative extinction coefficient, α cal (λ i , λ j ).…”

Section: Analysis Methodsmentioning

confidence: 99%

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Properties of polar stratospheric clouds observed by ILAS in early 1997

et al. 2003

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[1] The relative extinction coefficients for Arctic polar stratospheric clouds (PSCs) are determined using a transmittance-ratio technique from spectral data observed in early 1997 by the Improved Limb Atmospheric Spectrometer (ILAS). The observed relative extinction coefficients are compared with theoretical coefficients calculated by the Mie theory for single mode lognormal size distributions of PSCs. The observed PSCs are then classified into three types: nitric acid droplets (HNO 3 /H 2 O), and nitric acid trihydrate (NAT) solid particles in the form of a-NAT and b-NAT. However, the definitive detection of a-NAT is difficult due to uncertainties of atmospheric temperature measurements and laboratory determination of refractive indices.

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Section: Comparison With Other Reportsmentioning

confidence: 74%

Section: Analysis Methodsmentioning

confidence: 99%

Properties of polar stratospheric clouds observed by ILAS in early 1997

et al. 2003

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“…If the sizes of the scattering particles are comparable to the wavelength of the radiation used for the observation, one can exploit the wavelength dependence of the cloud optical properties to gain insight into the microphysics of the particulate scatterers. Over the years, sophisticated lidar instrumentation and retrieval algorithms have been developed for this purpose that have been successfully applied to stratospheric aerosols [ Del Guasta et al , 1994; Wandinger et al , 1995], polar stratospheric clouds [ Carslaw et al , 1998; Mehrtens et al , 1999], tropospheric aerosols [ Müller et al , 1998, 2001], and contrails [ Sassen et al , 2001]. …”

Section: Introductionmentioning

confidence: 99%

Correlations among the optical properties of cirrus‐cloud particles: Microphysical interpretation

Reichardt

Heß

et al. 2002

J. Geophys. Res.

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[1] Cirrus measurements obtained with a ground-based polarization Raman lidar at 67.9°N in January 1997 reveal a strong positive correlation between the particle optical properties, specifically depolarization ratio d par and extinction-to-backscatter (lidar) ratio S par , for d par < $40%, and an anticorrelation for d par > $40%. Over the duration of the measurements, both particle properties vary systematically. This effect is particularly pronounced in the case of d par , which decreases significantly with time. The analysis of lidar humidity and radiosonde temperature data shows that the measured optical properties stem from scattering by dry solid ice particles, while scattering by supercooled droplets, or by wetted or subliming ice particles, can be excluded. For the microphysical interpretation of the lidar measurements, ray-tracing computations of particle scattering properties have been used. The comparison with the theoretical data suggests that the observed cirrus data can be interpreted in terms of size, shape, and growth of the cirrus particles, the latter under the assumption that the lidar measurements of consecutive cloud segments can be mapped on the temporal development of a single cloud parcel moving along its trajectory: Near the cloud top in the early stage of cirrus development, light scattering by nearly isometric particles that have the optical characteristics of hexagonal columns (short, column-like particles) is dominant. Over time, the ice particles grow, and as the cloud base height extends to lower altitudes characterized by warmer temperatures they become morphologically diverse. For large S par and depolarization values of $40%, the scattering contributions of column-and plate-like particles are roughly the same. In the lower ranges of the cirrus clouds, light scattering is predominantly by plate-like ice particles. This interpretation assumes random orientation of the cirrus particles. Simulations with a simple model suggest, however, that the positive correlation between S par and d par , which is observed for depolarization ratios <40% mainly at low cloud altitudes, can be alternatively explained by horizontal alignment of a fraction of the cirrus particle population.

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“…In this paper, we explore an alternative method based on the comparison between measured and model-simulated Backscatter Coefficients (BC), using a Monte Carlo approach. The formulation of the method is inspired by the works of Beyerle et al (1994) and Mehrtens et al (1999). The retrieval algorithm minimizes a cost function of the misfit between measurements and model simulations with the control variables being the parameters of the PSC size distribution.…”

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confidence: 99%

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

et al. 2008

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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...

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Type I PSC‐particle properties: Measurements at ALOMAR 1995 to 1997

Cited by 13 publications

References 10 publications

Properties of polar stratospheric clouds observed by ILAS in early 1997

Properties of polar stratospheric clouds observed by ILAS in early 1997

Correlations among the optical properties of cirrus‐cloud particles: Microphysical interpretation

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

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