Calibration of a Multichannel Water Vapor Raman Lidar through Noncollocated Operational Soundings: Optimization and Characterization of Accuracy and Variability

Abstract

This paper presents a parametric automatic procedure to calibrate the multichannel Rayleigh-Mie-Raman lidar at the Institute for Atmospheric Science and Climate of the Italian National Research Council (ISAC-CNR) in Tor Vergata, Rome, Italy, using as a reference the operational 0000 UTC soundings at the WMO station 16245 (Pratica di Mare) located about 25 km southwest of the lidar site. The procedure, which is applied to both channels of the system, first identifies portions of the lidar and radiosonde profiles that are assumed to sample the same features of the water vapor profile, taking into account the different time and space sampling. Then, it computes the calibration coefficient with a best-fit procedure, weighted by the instrumental errors of both radiosounding and lidar. The parameters to be set in the procedure are described, and values adopted are discussed. The procedure was applied to a set of 57 sessions of nighttime 1-minsampling lidar profiles (roughly about 300 h of measurements) covering the whole annual cycle (February 2007-September 2008. A calibration coefficient is computed for each measurement session. The variability of the calibration coefficients (;10%) over periods with the same instrumental setting is reduced compared to the values obtained with the previously adopted, operator-assisted, and time-consuming calibration procedure. Reduction of variability, as well as the absence of evident trends, gives confidence both on system stability as well as on the developed procedure. Because of the definition of the calibration coefficient and of the different sampling between lidar and radiosonde, a contribution to the variability resulting from aerosol extinction and to the spatial and temporal variability of the water vapor mixing ratio is expected. A preliminary analysis aimed at identifying the contribution to the variability from these factors is presented. The parametric nature of the procedure makes it suitable for application to similar Raman lidar systems.

Water vapor observations up to the lower stratosphere through the Raman lidar during the Maïdo Lidar Calibration Campaign

“…These values are consistent to those obtained with other instruments (Whiteman et al, 2007;Hoareau et al, 2009;Dionisi et al, 2010;Leblanc et al, 2012). This variability clearly emerges for the 8 h lidar session between the 15:00 UTC on the 11 April and the 00:00 UTC on 12 April (highlighted by the black vertical dotted lines in the plot) where the calibration factor varies from almost 270 to 150.…”

“…In particular, preliminary results on the accuracy of Raman water vapor measurements in the UTLS have been obtained (Whiteman et al, 2011b(Whiteman et al, , 2012Leblanc et al, 2012), and different calibration methodologies have been developed Leblanc et al, 2011;Hoareau et al, 2009;Dionisi et al, 2010;Reichardt et al, 2012, Bock et al, 2013. The aim is to set up a lidar reference network for upper-air climate observations of water vapor such as GRUAN (GCOS Reference Upper-Air Network, Immler et al, 2010).…”

Water vapour profiles from Raman lidar automatically calibrated by microwave radiometer data during HOPE

“…The vertical smoothing amounts to 270 m. Only an altitude region from 2 up to 5 km is regarded for the regression to exclude boundary layer inhomogeneities in the water vapour structure and to avoid differences due to the RS drift in higher altitudes. Using this method, Dionisi et al (2010) found a variability in the calibration factor of about 10 %.…”

Raman Lidar for Meteorological Observations, RALMO – Part 2: Validation of water vapor measurements

“…The influence of different temporal and spatial matching and its impact on the system has been previously discussed in the literature (e.g. Sussmann et al, 2009;Schneider et al, 2010;Vogelmann et al, 2011;Dionisi et al, 2010).…”