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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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Atmos. Meas. Tech., 11, 2735-2748, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
08 May 2018
Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data
Guangyao Dai1,2, Dietrich Althausen1, Julian Hofer1, Ronny Engelmann1, Patric Seifert1, Johannes Bühl1, Rodanthi-Elisavet Mamouri3, Songhua Wu2,4, and Albert Ansmann1 1Leibniz Institute for Tropospheric Research, Leipzig, Germany
2College of Information Science and Engineering, Ocean University of China, Qingdao, China
3Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus
4Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
Abstract. 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 %.
Citation: Dai, G., Althausen, D., Hofer, J., Engelmann, R., Seifert, P., Bühl, J., Mamouri, R.-E., Wu, S., and Ansmann, A.: Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data, Atmos. Meas. Tech., 11, 2735-2748,, 2018.
Publications Copernicus
Short summary
The presented calibration method grants access to quality approved automated atmospheric water vapor profiles from lidar measurements. This method uses the Raman lidar data from the water vapor and nitrogen channels and additional data from sun photometer and GDAS. The retrieved water vapor profiles agree well with respective profiles from radio soundings. The paper describes this method and shows results from the CyCARE (Cyprus Cloud Aerosol and Rain Experiment) campaign in 2015–2017.
The presented calibration method grants access to quality approved automated atmospheric water...