Abstract
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.
Highlights
Water vapor is one of the most important components of the atmosphere when considering atmospheric chemistry, radiation, dynamics and clouds
Several appendices are provided that give more of the traditional calibration/validation results during the MOHAVE-2009 campaign. These appendices include information on the performance of the corrected Vaisala RS92 radiosonde during MOHAVE-2009; the development of a new radiosonde calibration technique that reduces the influence of atmospheric variability on the derived lidar calibration constant; the quantification of the full uncertainty budget of Raman lidar water vapor mixing ratio measurements; the development of specific data products for optimum comparison with radiosondes, satellites or other lidar systems; and a comparison of Integrated Precipitable Water (IPW) from lidar and GPS
These appendices included information on the performance of the corrected Vaisala RS92 radiosonde during MOHAVE-2009; the development of a new radiosonde calibration technique that reduces the influence of atmospheric variability on the derived lidar calibration constant; the quantification of the full uncertainty budget of Raman lidar water vapor mixing ratio measurements; the development of specific data products for optimum comparison with radiosondes, satellites or other lidar systems; and a comparison of Integrated Precipitable Water (IPW) from lidar and GPS
Summary
Water vapor is one of the most important components of the atmosphere when considering atmospheric chemistry, radiation, dynamics and clouds. The largest percentage changes are expected in the upper troposphere where model predictions show annual increases of up to 1 % and more during the current century (Soden et al, 2005; Boers and Meijgaard, 2009; Whiteman et al, 2011b) For these reasons and others, significant effort has been put into both measurements and modeling of upper tropospheric water vapor to assess long-term trends in water vapor concentrations and address the consequences of future changes in atmospheric water vapor amounts. These appendices include information on the performance of the corrected Vaisala RS92 radiosonde during MOHAVE-2009; the development of a new radiosonde calibration technique that reduces the influence of atmospheric variability on the derived lidar calibration constant; the quantification of the full uncertainty budget of Raman lidar water vapor mixing ratio measurements; the development of specific data products for optimum comparison with radiosondes, satellites or other lidar systems; and a comparison of Integrated Precipitable Water (IPW) from lidar and GPS
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