Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data

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 Polly XT 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 Polly XT 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 R 2 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 %.

Ice-nucleating particle versus ice crystal number concentrationin altocumulus and cirrus layers embedded in Saharan dust:a closure study

“…gov/gdas1.php, last access: 8 October 2019) NCEP model GDAS1 output archives contain these data (GDAS, 2019). During CyCARE we found remarkably good agreement between the GDAS1 and radiosonde temperature profiles (Dai et al, 2018). The standard deviation (root-mean-square deviation based on all 43 radiosonde profiles and respective GDAS1 profiles) was 0.87 K.…”

“…An excellent data set was collected for in-depth studies on the impact of dust and aerosol pollution on cloud and rain evolution in the eastern Mediterranean. During the intensive Cy-CARE field phase in April 2017, 43 Vaisala radiosondes were launched at Limassol (Dai et al, 2018). In the first step, the INPC is retrieved from polarization lidar observations outside the cloud layer (e.g., before the cloud layer crosses the lidar field site).…”

Automated compact mobile Raman lidar for water vapor measurement: instrument description and validation by comparison with radiosonde, GNSS, and high-resolution objective analysis

“…To obtain the value of K, several methods have been proposed and tested; they used external reference water vapor sensor measurements (e.g., radiosonde, GNSS, microwave radiometer, or sun photometer) or external reference light sources (e.g., diffuse sunlight or a standard lamp) and the effective Raman cross sections. A comprehensive review of these methods can be found elsewhere (Dai et al, 2018;David et al, 2017). In this study, we used the most conventional and reliable method by using radiosonde as described in Sect.…”

Long-term profiling of aerosol light extinction, particle mass, cloud condensation nuclei, and ice-nucleating particle concentration over Dushanbe, Tajikistan, in Central Asia

“…Volume concentration profiles are converted to mass concentration profiles by using generic densities of dust (2.6 g cm −3 ) and non-dust (1.5 g cm −3 ) (Ansmann et al, 2012). To finally estimate INP concentrations for the most relevant ice nucleation modes (immersion freezing, deposition nucleation) , parameterizations are applied with dust particle number concentration n 250,d (considering particles with radii larger than 250 nm only) as input to obtain immersion-freezing INP concentrations (DeMott et al, 2015) and dust particle surfacearea concentration s d as input to obtain deposition-nucleation INP concentrations (Ullrich et al, 2017). In the latter retrieval, the ice supersaturation level during the ice crystal nucleation process is required, and a typical value of 1.15 (115 % relative humidity over ice) is assumed.…”

“…11e. We distinguish profiles relevant for immersion freezing (DeMott et al, 2015) and deposition nucleation of ice crystals (Ullrich et al, 2017). Immersion freezing dominates in mixed-phase clouds at temperatures > −30 • C, whereas deposition nucleation is the relevant heterogeneous ice nucleation process at temperatures < −30 • C, e.g., in cirrus layers.…”