ALBERT

Description: The MOHAVE-2009 campaign brought together diverse instrumentation for measuring atmospheric water vapor. We report on the participation of the ALVICE mobile laboratory in the MOHAVE-2009 campaign. In an appendix we also report on the performance of the corrected Vaisala RS92 radiosonde during the campaign. A new radiosonde based calibration algorithm is presented that reduces the influence of atmospheric variability on the derived calibration constant. The MOHAVE-2009 campaign permitted all 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. A correction technique is derived and applied to the ALVICE lidar water vapor profiles. Other sources of wet biases are discussed and data from other Raman lidar systems are investigated revealing that wet biases in upper tropospheric and lower stratospheric 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 data. 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 resistant to additional noise sources. The correction shown here holds promise for permitting useful upper tropospheric water vapor profiles to be consistently measured by Raman lidar within NDACC and elsewhere despite the prevalence of instrumental and atmospheric effects that can contaminate the very low signal to noise measurements in the UT.

Description: 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.

Description: Water vapor plays an important role in the energetics of the boundary layer processes which in turn play a key role in regulating regional and global climate. It plays a primary role in Earth's hydrological cycle, in radiation balance as a direct absorber of infrared radiation, and in atmospheric circulation as a latent heat energy source, as well as in determining cloud development and atmospheric stability. Water vapor concentration, expressed as a mass mixing ratio (g kg(exp -l)), is conserved in all meteorological processes except condensation and evaporation. This property makes it an ideal choice for studying many of the atmosphere's dynamic features. Raman scattering measurements from lidar also allow retrieval of water vapor mixing ratio profiles at high temporal and vertical resolution. Raman lidars sense water vapor to altitudes not achievable with towers and surface systems, sample the atmosphere at much higher temporal resolution than radiosondes or satellites, and do not require strong vertical gradients or turbulent fluctuations in temperature that is required by acoustic sounders and radars. Analysis of highly-resolved water vapor profiles are used here to characterize two important mesoscale flows: thunderstorm outflows and a cold front passage. The data were obtained at the Atmospheric Radiation Measurement Site (CART) by the groundbased Department of Energy/Sandia National Laboratories lidar (CART Raman lidar or CARL) and Goddard Space Flight Center Scanning Raman Lidar (SRL). A detailed discussion of the SRL and CARL performance during the IOPs is given by others in this meeting.

Description: The amount of water vapor in the atmosphere helps to determine the likelihood that severe storms may develop. The concentration of water vapor, though, is highly variable in space and time. And yet small changes in water vapor concentration over a short period of time or over a short spatial distance can determine whether a storm may or may not develop. Therefore, in order to improve the ability to forecast severe weather such as thunderstorms it is important to measure water vapor in the atmosphere with high spatial and temporal resolution. One of the most attractive research tools for measuring water vapor in the atmosphere with high spatial and temporal resolution is a Raman lidar. A Raman lidar consists of a laser transmitter, a telescope receiver and optics and electronics for processing opticand electronic signals. A laser pulse is emitted into the atmosphere and it interacts with molecules in the atmosphere causing them to become excited and to emit, through the Raman process, photons of different wavelength than emitted by the laser. The molecule that emitted these emitted. This is the way that a Raman lidar identifies water vapor molecules in the atmosphere. can be identified based on the wavelength of the photons One of the great challenges in Raman lidar measurements has been to make useful daytime measurements of the water vapor profile under bright daytime conditions. In this first of two papers, we describe the instrumentation and analysis of the first documented Raman lidar that is able to measure water vapor in the daytime with sufficient quality to permit the study of developing storm systems.

Description: 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.

Description: The NASA/GSFC Scanning Raman Lidar (SRL) was stationed on Andros Island in the Bahamas during August - September, 1998 as a part of the third Convection and Moisture Experiment (CAMEX-3) which focussed on hurricane development and tracking. During the period August 21 - 24, hurricane Bonnie passed near Andros Island and influenced the water vapor and cirrus cloud measurements acquired by the SRL. Two drying signatures related to the hurricane were recorded by the SRL and other sensors. Cirrus cloud optical depths (at 351 nm) were also measured during this period. Optical depth values ranged from approximately 0.01 to 1.4. The influence of multiple scattering on these optical depth measurements was studied with the conclusion that the measured values of optical depth are less than the actual value by up to 20%. The UV/lR cirrus cloud optical depth ratio was estimated based on a comparison of lidar and GOES measurements. Simple radiative transfer model calculations compared with GOES satellite brightness temperatures indicate that satellite radiances are significantly affected by the presence of cirrus clouds if IR optical depths are approximately 0.02 or greater. This has implications for satellite cirrus detection requirements.