Application of the Optimal Estimation Method (OEM) to Retrieve Temperature and Relative Humidity from Rotational Raman Lidar Backscatter Measurements

The optimal estimation (OEM) method is one of the most robust and popular inverse method which determines the most probable state consistent with the measurements and a priori knowledge. The OEM requires a forward model which is capable of reproducing measurements using the relevant physics and mathematical description of the instrument. We present two forward models that were used to apply the OEM to retrieve atmospheric temperatures and relative humidity from the Rotational Raman (RR) backscatter lidar measurements. We successfully applied the OEM for temperature retrievals from the two pure RR channel measurements obtained by the Raman Lidar for Meteorological Observations (RALMO) located in Payerne, Switzerland. The OEM retrieved temperatures from day and night time measurements with clear and cloudy conditions agreed with the sonde and the traditional lidar temperature retrievals with in ±5 K up to 20 km in height. The OEM retrieved temperatures are then used as an input for the relative humidity forward model. Relative humidity retrievals use RALMO nitrogen and water vapor measurements in addition to the pure RR measurements. The lidar constants, overlap functions, deadtimes of the detectors, background noises and the aerosol extinction were also retrieved along with the relative humidity and temperature profiles. We also present the a full uncertainty budget with both random and systematic uncertainties on profile by profile basis and vertical resolution of the retrievals as a function of height provided by the OEM.