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 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 %.