Solar background radiation temperature calibration of a pure rotational Raman lidar

Raman lidars are an important tool for measuring important atmospheric parameters including water vapour content and temperature in the troposphere and stratosphere. These measurements enable climatology studies and trend analyses to be performed. To detect long-term trends it is critical that the calibration of the system and the monitoring of its associated uncertainties are as reliable and continuous as possible. Here we demonstrate a new methodology to derive calibration coefficients for a rotational Raman temperature lidar. We use solar background measurements taken by the rotational Raman channels of the Raman Lidar for Meteorological Observations (RALMO) located at the Federal Office of Meteorology and Climatology (MeteoSwiss) in Payerne, Switzerland, to calculate a relative calibration as a function of time, which is made an absolute calibration by requiring only a single external calibration, in our case with an ensemble of radiosonde flights. This approach was verified using an external time series of coincident radiosonde measurements. We employed the calibration technique on historical measurements that used a Licel data acquisition system and established a calibration time series spanning 2011 to 2015 using both the radiosonde-based external and solar-background-based internal methods. Our results show that using the background calibration technique reduces the mean bias of the calibration by an average of 0.5 K across the troposphere compared to using the local radiosoundings. Furthermore, it demonstrates the background calibration's ability to adjust and maintain continuous calibration values even amidst sudden changes in the system, which sporadic external calibration could miss. This approach ensures that climatological averages and trends remain unaffected by the drift effects commonly associated with using daily operational radiosondes. It also allows a lidar not co-located with a routine external source to be continuously calibrated once an initial external calibration is done. Furthermore, the technique works for temperature retrievals using both the optimal estimation method and the traditional temperature algorithms.