Correction technique for Raman water vapor lidar signal-dependent bias and suitability for water vapor trend monitoring in the upper troposphere

Abstract

The MOHAVE-2009 campaign brought together diverse instrumentation for measuring atmospheric water vapor. We report on the participation of the ALVICE (Atmospheric Laboratory for Validation, Interagency Collaboration and Education) mobile laboratory in the MOHAVE-2009 campaign. In appendices we also report on the performance of the corrected Vaisala RS92 radiosonde measurements during the campaign, on a new radiosonde based calibration algorithm that reduces the influence of atmospheric variability on the derived calibration constant, and on other results of the ALVICE deployment. The MOHAVE-2009 campaign permitted the Raman lidar systems participating to discover and address measurement biases in the upper troposphere and lower stratosphere. The ALVICE lidar system was found to possess a wet bias which was attributed to fluorescence of insect material that was deposited on the telescope early in the mission. Other sources of wet biases are discussed and data from other Raman lidar systems are investigated, revealing that wet biases in upper tropospheric (UT) and lower stratospheric (LS) water vapor measurements appear to be quite common in Raman lidar systems. Lower stratospheric climatology of water vapor is investigated both as a means to check for the existence of these wet biases in Raman lidar data and as a source of correction for the bias. A correction technique is derived and applied to the ALVICE lidar water vapor profiles. Good agreement is found between corrected ALVICE lidar measurments and those of RS92, frost point hygrometer and total column water. The correction is offered as a general method to both quality control Raman water vapor lidar data and to correct those data that have signal-dependent bias. The influence of the correction is shown to be small at regions in the upper troposphere where recent work indicates detection of trends in atmospheric water vapor may be most robust. The correction shown here holds promise for permitting useful upper tropospheric water vapor profiles to be consistently measured by Raman lidar within NDACC (Network for the Detection of Atmospheric Composition Change) and elsewhere, despite the prevalence of instrumental and atmospheric effects that can contaminate the very low signal to noise measurements in the UT

Raman Lidar for Meteorological Observations, RALMO – Part 1: Instrument description

“…Therefore experimental calibration against a reference instrument, such as a balloon-borne sonde or microwave radiometer, is commonly used. In this approach C L is represented as a product of a range-independent part, referred to as calibration constant, and two range-dependent correction functions named usually "overlap" and "temperature" corrections (Whiteman, 1992(Whiteman, , 2003Whiteman et al, 2012;Goldsmith et al, 1998). The overall systematic error of the calibration function is hence a sum of the systematic errors of the calibration constant and the correction functions, plus errors not accounted for by "overlap" and "temperature" corrections.…”

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

“…Following Whiteman et al (2012), the good agreement observed in the lower stratosphere (from 17 to 20 km) attests the absence of significant wet biases and validates the calibration procedure.…”

“…This monthly lower stratospheric water vapor profile might also be useful for the quality control of the data. In fact, as shown by the work of Whiteman et al (2012), the monthly average water vapor mixing ratios measured by the Microwave Limb Sounder (MLS) can be used to quality control Raman water vapor lidar data. This sounder installed on the AURA satellite observes thermal microwave -far infrared emissions from the Earth's atmosphere in five spectral regions.…”

“…In particular, several studies (Sherlock et al, 1999;Ferrare et al, 2004;Whiteman et al, 2006;Leblanc et al, 2012) have highlighted that many lidar systems experienced an excess amount of water vapor (wet bias) in the mid-upper troposphere lidar profile, significantly impacting their measurements. The recent work of Whiteman et al (2012) identified three general causes for this effect: (1) instrumental effects, (2) data processing, (3) atmospheric constituents.…”

“…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).…”