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

Abstract

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

North-western Mediterranean sea-breeze circulation in a regional climate system model

“…The sea-breeze depth is estimated from the UHF or lidar wind profiles as the height of the transition between the upper-level synoptic flow which exhibits a strong wind direction shift and an increase of the wind speed. In situations when the synoptic flow and the sea breeze blow in the same direction, we use the PBL depth as a tracer of the sea breeze depth using the lidar signal and following the method described in Morille et al (2007). Since the UHF profilers and the lidars were not available during the whole campaign, the sea-breeze depth and intensity are not available for all IOPs.…”

Investigation of the Planetary Boundary Layer in the Swiss Alps Using Remote Sensing and In Situ Measurements

“…Details of the algorithm can be found in Morille et al (2007). To improve the results, the quality index of Haji and Wauben (2007) is applied to classify the PBL height estimations.…”

Automatic detection of atmospheric boundary layer height using ceilometer backscatter data assisted by a boundary layer model

“…The S × R 2 inflection points, which are tracers of atmospheric stratifications, are searched employing both the derivative of the signal calculated by finite differentiation (gradient method) and the WCT, which consists of the convolution of the signal and a wavelet function. The implemented WCT algorithm employs a step-like Haar wavelet, and follows the scheme previously described in the literature (Brooks, 2003;Haij et al, 2007;Morille et al, 2007). This wavelet is characterized by the size of its non-null part (usually referred to as dilation), which determines the scale of the features that can be revealed by the WCT.…”

“…Technically, the processing is performed either by direct numerical calculation, finding the relative minima of the first derivative of the range-corrected signal, or by means of the discrete wavelet transform (DWT), also sometimes referred to as wavelet covariance transform (WCT) method. The latter, initially employed by Ehret et al (1993), Wang and Lenschow (1995), Kiemle et al (1995), Davis et al(1997), Russell et al (1998), Sullivan et al (1998), Cohn et al (1998), Cohn and Angevine (2000), Davis et al(2000), Brooks (2003), and more recently by Morille et al(2007) and Martucci et al (2010aMartucci et al ( , 2010b, consists in the convolution of the vertical signal with a step function, which is able to detect local discontinuities in the backscatter profile. For each column these signal-processing techniques identify multiple points along the vertical.…”