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Author: Althausen, Dietrich × Start date: 2017 × Type: Text × Subject: data set × WGL Institute: TROPOS ×

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    Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data
    (München : European Geopyhsical Union, 2018) Dai, Guangyao; Althausen, Dietrich; Hofer, Julian; Engelmann, Ronny; Seifert, Patric; Bühl, Johannes; Mamouri, Rodanthi-Elisavet; Wu, Songhua; Ansmann, Albert
    We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar PollyXT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor from the lidar observations is used for the calibration of the lidar measurements with the sun photometer measurements. The retrieved calibrated water vapor mixing ratio from the lidar measurements has a relative uncertainty of 11 % in which the error is mainly caused by the error of the sun photometer measurements. During CyCARE, nine measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the PollyXT Raman lidar is 6.56 g kg−1 ± 0.72 g kg−1 (with a statistical uncertainty of 0.08 g kg−1 and an instrumental uncertainty of 0.72 g kg−1). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R2 of 0.99 is found. One example is presented to demonstrate the full potential of a well-calibrated Raman lidar. The relative humidity profiles from lidar, GDAS (simulation) and radiosonde are compared, too. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with the relative uncertainty of 10–20 %.
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    Regional Saharan dust modelling during the SAMUM 2006 campaign
    (Milton Park : Taylor & Francis, 2017) Heinold, Bernd; Tegen, Ina; Esselborn, Michael; Kandler, Konrad; Knippertz, Peter; Müller, Detlef; Schladitz, Alexander; Tesche, Matthias; Weinzierl, Bernadett; Ansmann, Albert; Althausen, Dietrich; Laurent, Benoit; Massling, Andreas; Müller, Thomas; Petzold, Andreas; Schepanski, Kerstin; Wiedensohler, Alfred
    The regional dust model system LM-MUSCAT-DES was developed in the framework of the SAMUM project. Using the unique comprehensive data set of near-source dust properties during the 2006SAMUMfield campaign, the performance of the model system is evaluated for two time periods in May and June 2006. Dust optical thicknesses, number size distributions and the position of the maximum dust extinction in the vertical profiles agree well with the observations. However, the spatio-temporal evolution of the dust plumes is not always reproduced due to inaccuracies in the dust source placement by the model. While simulated winds and dust distributions are well matched for dust events caused by dry synoptic-scale dynamics, they are often misrepresented when dust emissions are caused by moist convection or influenced by small-scale topography that is not resolved by the model. In contrast to long-range dust transport, in the vicinity of source regions the model performance strongly depends on the correct prediction of the exact location of sources. Insufficiently resolved vertical grid spacing causes the absence of inversions in the model vertical profiles and likely explains the absence of the observed sharply defined dust layers.