A Novel Calibration Method for Pure Rotational Raman Lidar Temperature Profiling

Abstract

We propose a new calibration method for pure rotational Raman (PRR) lidar temperature profiling based on the different temperature sensitivities of Stocks and anti-Stocks PRR lines. This method reconstructs the expression of the differential backscatter cross section according to the temperature dependencies of each component and forms a temperature factor and a calibration factor in the intensity ratio. With these factors, the temperature is retrievable from the lidar return. The effectiveness and accuracy of the proposed method have been verified through simulations and experiments. The inversion error can be reduced by~50% compared with the commonly used calibration methods in weak signal-to-noise situations.Plain Language Summary A new calibration method for temperature detecting by a pure rotational Raman lidar is proposed. Based on the properties of the pure rotational Raman spectrum, a calibration factor is formed in the return signals and is used to retrieve the temperature in combination with local radiosonde. Both simulations and experiments validate the effectiveness of our proposed method. The inversion errors are reduced by~50% compared with the traditional fitting methods in weak signal-to-noise environments. HE ET AL.10,925

Retrieval of temperature from a multiple channel pure rotational Raman backscatter lidar using an optimal estimation method

“…The results showed the 1 T term expressed in a form of a quadratic function of log ratio of the PRR measurements as best for practical use. He et al (2018) proposed a new calibration method for PRR lidar temperature profiling based on the different temperature sensitivities of Stokes and anti-Stokes PRR lines. They reconstruct the expression of the differential backscatter cross section according to the temperature dependencies of each component and form both a temperature factor and a calibration factor in the intensity ratio.…”