Yohann Morille

STRAT: An Automated Algorithm to Retrieve the Vertical Structure of the Atmosphere from Single-Channel Lidar Data

Today several lidar networks around the world provide large datasets that are extremely valuable for aerosol and cloud research. Retrieval of atmospheric constituent properties from lidar profiles requires detailed analysis of spatial and temporal variations of the signal. This paper presents an algorithm called Structure of the Atmosphere (STRAT), which is designed to retrieve the vertical distribution of cloud and aerosol layers in the boundary layer and through the free troposphere and to identify near-particle-free regions of the vertical profile and the range at which the lidar signal becomes too attenuated for exploitation, from a single lidar channel. The paper describes each detection method used in the STRAT algorithm and its application to a tropospheric backscatter lidar operated at the SIRTA observatory, in Palaiseau, 20 km south of Paris, France. STRAT retrievals are compared to other means of layer detection and classification; retrieval performances and uncertainties are discussed.

Evaluation of Mixing-Height Retrievals from Automatic Profiling Lidars and Ceilometers in View of Future Integrated Networks in Europe

The determination of the depth of daytime and nighttime mixing layers must be known very accurately to relate boundary-layer concentrations of gases or particles to upstream fluxes. The mixing-height is parametrized in numerical weather prediction models, so improving the determination of the mixing height will improve the quality of the estimated gas and particle budgets. Datasets of mixing-height diurnal cycles with high temporal and spatial resolutions are sought by various end users. Lidars and ceilometers provide vertical profiles of backscatter from aerosol particles. As aerosols are predominantly concentrated in the mixing layer, lidar backscatter profiles can be used to trace the depth of the mixing layer. Large numbers of automatic profiling lidars and ceilometers are deployed by meteorological services and other agencies in several European countries providing systems to monitor the mixing height on temporal and spatial scales of unprecedented density. We investigate limitations and capabilities of existing mixing height retrieval algorithms by applying five different retrieval techniques to three different lidars and ceilometers deployed during two 1-month campaigns. We studied three important steps in the mixing height retrieval process, namely the lidar/ceilometer pre-processing to reach sufficient signal-to-noise ratio, gradient detection techniques to find the significant aerosol gradients, and finally quality control and layer attribution to identify the actual mixing height from multiple possible layer detections. We found that layer attribution is by far the most uncertain step. We tested different gradient detection techniques, and found no evidence that the first derivative, wavelet transform, and two-dimensional derivative techniques have different skills to detect one or multiple significant aerosol gradients from lidar and ceilometer attenuated backscatter. However, our study shows that, when mixing height retrievals from a ultraviolet lidar and a near-infrared ceilometer agreed, they were 25–40% more likely to agree with an independent radiosonde mixing height retrieval than when each lidar or ceilometer was used alone. Furthermore, we point to directions that may assist the layer attribution step, for instance using commonly available surface measurements of radiation and temperature to derive surface sensible heat fluxes as a proxy for the intensity of convective mixing. It is a worthwhile effort to pursue such studies so that within a few years automatic profiling lidar and ceilometer networks can be utilized efficiently to monitor mixing heights at the European scale.

Macrophysical and optical properties of midlatitude cirrus clouds from four ground-based lidars and collocated CALIOP observations

Ground-based lidar and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data sets gathered over four midlatitude sites, two U.S. and two French sites, are used to evaluate the consistency of cloud macrophysical and optical property climatologies that can be derived by such data sets. The consistency in average cloud height (both base and top height) between the CALIOP and ground data sets ranges from −0.4 km to +0.5 km. The cloud geometrical thickness distributions vary significantly between the different data sets, due in part to the original vertical resolutions of the lidar profiles. Average cloud geometrical thicknesses vary from 1.2 to 1.9 km, i.e., by more than 50%. Cloud optical thickness distributions in subvisible, semitransparent, and moderate intervals differ by more than 50% between ground- and space-based data sets. The cirrus clouds with optical thickness below 0.1 (not included in historical cloud climatologies) represent 30–50% of the nonopaque cirrus class. An important part of this work consists in quantifying the different possible causes of discrepancies between CALIOP and surface lidar. The differences in average cloud base altitude between ground and CALIOP data sets can be attributed to (1) irregular sampling of seasonal variations in the ground-based data, (2) day-night differences in detection capabilities by CALIOP, and (3) the restriction to situations without low-level clouds in ground-based data. Cloud geometrical thicknesses are not affected by irregular sampling of seasonal variations in the ground-based data but by the day-night differences in detection capabilities of CALIOP and by the restriction to situations without low-level clouds in ground-based data.

Using Continuous Ground-Based Radar and Lidar Measurements for Evaluating the Representation of Clouds in Four Operational Models

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud parameterization.

Comparison of aerosol chemistry transport model simulations with lidar and Sun photometer observations at a site near Paris

The ability of the aerosol chemistry transport model CHIMERE to simulate the vertical aerosol concentration profiles at a site near the city of Paris is evaluated using routine elastic backscatter lidar and Sun photometer measurements. The comparisons of model aerosols with measurements are carried out over a full year time period between October 2002 and September 2003. The methodology we propose here is new : from the model concentration outputs (optical properties varying with chemical composition and mass vertical distribution) we simulate the lidar backscattering profiles and compare them with the observed ones. The comparisons demonstrate the ability of the model to reproduce correctly the aerosol vertical distributions and their temporal variability. However, the aerosol load within the boundary layer is generally underestimated by the model, in particular during the afternoon hours and the summertime period. Several sensitivity tests indicate that this underestimation may have two origins: the lack of secondary organic and, to a lesser extent, mineral aerosols inside the model. The second deficiency is due to the absence of erosion/resuspension of soil material in the primary aerosol sources considered here; the first deficiency is probably due to incomplete knowledge about the formation of organic species in a photochemically active atmosphere. The results also show that the particles ranging from 0.08 to 1.25 mum in radius represent more than 89 % of the volume backscattering at 532 nm, while the coarse particles are not efficient in terms of optical properties. The missing aerosol mass must therefore be found within the accumulation mode.

Cloud properties derived from two lidars over the ARM SGP site

Active remote sensors such as lidars or radars can be used with other data to quantify the cloud properties at regional scale and at global scale. Relative to radar, lidar remote sensing is sensitive to very thin and high clouds but has a significant limitation due to signal attenuation in the ability to precisely quantify the properties of clouds with a cloud optical thickness larger than 3. The cloud properties for all levels of clouds are derived and distributions of cloud base height (CBH), top height (CTH), physical cloud thickness (CT), and optical thickness (COT) from local statistics are compared. The goal of this study is (1) to establish a climatology of macrophysical and optical properties for all levels of clouds observed over the ARM SGP site and (2) to estimate the discrepancies between the two remote sensing systems (pulse energy, sampling, resolution, etc.). Our first results tend to show that the MPL, which are the primary ARM lidars, have a distinctly limited range within which all of these cloud properties are detectable, especially cloud top and cloud thickness, but this can include cloud base particularly during summer daytime period. According to the comparisons between RL and MPL, almost 50% of situations show a signal to noise ratio too low (smaller than 3) for the MPL in order to detect clouds higher than 7km during daytime period in summer. Consequently, the MPL-derived annual cycle of cirrus cloud base (top) altitude is biased low, especially for daylight periods, compared with those derived from the RL data, which detects cloud base ranging from 7.5 km in winter to 9.5 km in summer (and tops ranging from 8.6 to 10.5 km). The optically thickest cirrus clouds (COT > 0.3) reach 50% of the total population for the Raman lidar and only 20% for the Micropulse lidar due to the difference of pulse energy and the effect of solar irradiance contamination. A complementary study using the cloud fraction derived from the Micropulse lidar for clouds below 5 km and from the Raman lidar for cloud above 5 km allows for better estimation of the total cloud fraction between the ground and the top of the atmosphere. This study presents the diurnal cycle of cloud fraction for each season in comparisons with Long et al.s (2006) cloud fraction calculation derived from radiative flux analysis. Copyright

SIRTA, a multi-sensor platform for clouds and aerosols characterization in the atmosphere: infrastructure, objective and prospective

The SIRTA (Site instrumental de Recherche par Télédétection Atmosphérique) is a ground-based platform located 25km south of Paris in France. The SIRTA observatory was created in 1999 by the French research institute IPSL (Institut Pierre Simon Laplace) to conduct research programs in order to improve the knowledge of radiative and dynamic processes in the atmosphere as well as complex interactions between clouds and aerosols. The objective is to better comprehend climate changes and evolution of environment using a suite a state-of-art active and passive remote sensing instruments. Two ground platforms, a wooden tower, a roof platform and a building (where the lidar operates) are the main facilities of SIRTA. The project team is composed of six persons to ensure the station operations from instrument deployment, maintenance, data transfer and preliminary data analysis. The SIRTA infrastructure enables to conduct many research activities that involve the cloud and aerosol lidar. Some of them will be discussed: the development of the STRAT (Structure of the Atmosphere) algorithm dedicated to automatically discriminate atmospheric layers and retrieve geophysical parameters from lidar profiles, and the CALIPSO validation using the dual-channel backscatter lidar deployed at SIRTA.