Retrieval of Temperature From a Multiple Channel Rayleigh-Scatter Lidar Using an Optimal Estimation Method

Friday, 19 December 2014: 5:45 PM
Robert J Sica1,2 and Alexander Haefele2, (1)University of Western Ontario, London, ON, Canada, (2)Federal Office of Meteorology and Climatology MeteoSwiss, Remote Sensing Group, Payerne, Switzerland
The measurement of temperature in the middle atmosphere with Rayleigh-scatter lidars is an important technique for assessing atmospheric change. Current retrieval schemes for these temperature have several shortcoming which can be overcome using an optimal estimation method (OEM). OEMs are applied to the retrieval of temperature from Rayleigh-scatter lidar measurements using both single and multiple channel measurements. Forward models are presented that completely characterize the measurement and allow the simultaneous retrieval of temperature, dead time and background. The method allows a full uncertainty budget to be obtained on a per profile basis that includes, in addition to the statistical uncertainties, the smoothing error and uncertainties due to Rayleigh extinction, ozone absorption, the lidar constant, nonlinearity in the counting system, variation of the Rayleigh-scatter cross section with altitude, pressure, acceleration due to gravity and the variation of mean molecular mass with altitude. The vertical resolution of the temperature profile is found at each height, and a quantitative determination is made of the maximum height to which the retrieval is valid. A single temperature profile can be retrieved from measurements with multiple channels that cover different height ranges, vertical resolutions and even different detection methods. The OEM employed is shown to give robust estimates of temperature consistent with previous methods, while requiring minimal computational time. This demonstrated success of lidar temperature retrievals using an OEM opens new possibilities in atmospheric science for measurement integration between active and passive remote sensing instruments.

We are currently working on extending our method to simultaneously retrieve water vapour and temperature using Raman-scatter lidar measurements.