Abstract

Abstract. A methodology to identify and characterize cirrus clouds has been developed and applied to the multichannel-multiwavelength Rayleigh–Mie–Raman (RMR) lidar in Rome Tor Vergata (RTV). A set of 167 cirrus cases, defined on the basis of quasi-stationary temporal period conditions, has been selected in a data set consisting of about 500 h of nighttime lidar sessions acquired between February 2007 and April 2010. The derived lidar parameters (effective height, geometrical and optical thickness and mean back-scattering ratio) and the cirrus mid-height temperature (estimated from the radiosonde data of Pratica di Mare, WMO, World Meteorological Organization, site no. 16245) of this sample have been analyzed by the means of a clustering multivariate analysis. This approach identified four cirrus classes above the RTV site: two thin cirrus clusters in mid- and upper troposphere and two thick cirrus clusters in mid-upper troposphere. These results, which are very similar to those derived through the same approach at the lidar site of the Observatoire de Haute-Provence (OHP), allows characterization of cirrus clouds over the RTV site and attests to the robustness of such classification. To acquire some indications about the cirrus generation methods for the different classes, analyses of the extinction-to-backscatter ratio (lidar ratio, LReff, in terms of frequency distribution functions and dependencies on the mid-height cirrus temperature, have been performed. A preliminary study relating some meteorological parameters (e.g., relative humidity, wind components) to cirrus clusters has also been conducted. The RTV cirrus results, recomputed through the cirrus classification by Sassen and Cho (1992), show good agreement with other midlatitude lidar cirrus observations for the relative occurrence of subvisible (SVC), thin and opaque cirrus classes (10%, 49% and 41%, respectively). The overall mean value of cirrus optical depth is 0.37 ± 0.18, while most retrieved LReff values range between 10–60 sr, and the estimated mean value is 31 ± 15 sr, similar to LR values of lower latitude cirrus measurements. The obtained results are consistent with previous studies conducted with different systems and confirm that cirrus classification based on a statistical approach seems to be a good tool both to validate the height-resolved cirrus fields calculated by models and to investigate the key processes governing cirrus formation and evolution. However, the lidar ratio and optical depth analyses are affected by some uncertainties (e.g., lidar error noise, multiple scattering effects, supercooled water clouds) that reduce the confidence of the results. Future studies are needed to improve the characterization of the cirrus optical properties and, thus, the determination of their radiative impact.

Highlights

  • Despite extensive research dedicated to cirrus observations in conjunction with relevant parameters in the last decades, the report of the International Panel for Climate Change (IPCC, Solomon et al, 2007) demonstrated that the estimation of cirrus radiative impact is still one of the largest sources of uncertainty in global climate models (GCMs) parameterizations

  • It is important to specify that nighttime lidar measurements were performed only in the absence of precipitation and for lower tropospheric thick extinguishing clouds

  • Dupont et al (2010) have shown with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) (CloudAerosol Lidar with Orthogonal Polarization) data that neither nighttime nor ground conditions introduced any obvious bias in cirrus climatology using ground-based lidar at midlatitudes

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Summary

Introduction

Despite extensive research dedicated to cirrus observations in conjunction with relevant parameters (temperature, humidity, aerosols, wind, waves, etc.) in the last decades, the report of the International Panel for Climate Change (IPCC, Solomon et al, 2007) demonstrated that the estimation of cirrus radiative impact is still one of the largest sources of uncertainty in global climate models (GCMs) parameterizations. Statistical description of cirrus clouds at midlatitude was derived (Goldfarb et al, 2001) and three distinct cirrus classes with different optical properties were identified (Keckhut et al, 2006) This type of classification could be useful both for the validation of height-resolved cirrus fields as reproduced by models and for the investigation of the key processes controlling cirrus formation and evolution. The only detailed study on cirrus clouds conducted above this region (i.e., central Italy) was the work of Gobbi et al (2004), which provided the first statistics of cirrus occurrence during one year of lidar data over RTV sites but without discriminating the types of the observed cirrus Given these considerations, the objective of the present study has been to adapt and apply a methodology to identify and characterize cirrus, using measurements of RMR lidar system.

The Rayleigh–Mie–Raman lidar system
Cirrus principal parameters
Multivariate clustering analysis
Cirrus classification
Lidar ratio analysis
IV: Episodic highly tropopause cirrus scattering cirrus
III: Thin tropopause cirrus
Summary and conclusions
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