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A detailed investigation of calibration variation sources in the instrumental part of the detection-fibered, water vapor Raman lidar, Rameau, is presented. This lidar has been developed by the Institut National de l'Information Géographique et Forestière (IGN) together with the Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS) and a...
Contexts in source publication
Context 1
... Presentation of the IGN-LATMOS Raman lidar and inventory of the sources of instability Figure 1 draws the set-up of the IGN-LATMOS Raman lidar as it had been used during the Demevap campaign ( Bock et al., 2013). Below we discuss the likely signal and calibration variation sources at each stage of the system. ...
Context 2
... r N2 is the mass mixing ratio of nitrogen, M X the molecular weight of the species X, C X the instrumental transmission and detection efficiency of the optical and electronic elements in the re- ception, spectral detection and signal acquisition elements of the system (see figure 1), T (z, λ X ) the atmospheric transmittance from ground to distance z at wavelength λ X , and dσX (z,λX ) dΩ the Raman ...
Citations
... relative humidity (RH) is the most important atmospheric state parameter in cloud processes. RH also has a strong influence on visibility and the optical properties 20 of aerosol particles. However, due to the spatio-temporal variability of the water vapor, it is difficult to properly consider water vapor in weather prediction and climate models (Held and Soden, 2000;Tompkins, 2002). ...
... At present, the most frequently used calibration methods to determine the water vapor calibration constant are based on 20 simultaneous observations with a reference instrument, e.g., a microwave radiometer (Foth et al., 2015) or a radiosonde (Mattis et al., 2002;Madonna et al., 2011). These methods are also called sensor-dependent methods. ...
... The development of all these methods are well discussed by Whiteman et al. (2011) and David et al. (2017). Leblanc and McDermid (2008) proposed a new hybrid method combining dependent and independent methods to determine the calibration constant. ...
We present a practical method to continuously calibrate Raman lidar observations of the water vapor mixing ratio profile. 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 is obtained from the lidar observation. During CyCARE, 9 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. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with good accuracy.
