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

The determination of atmospheric temperature in the stratosphere, mesosphere and thermosphere using Rayleigh-scatter lidar measurements is one of the most important techniques available for understanding the dynamics of the middle atmosphere. While lidar instrumentation has significantly improved since the first applications of lasers in atmospheric sciences, the analysis techniques for middle atmosphere temperature have not significantly evolved in over 30 years. During this time, improvements in computers and computational techniques have made possible the use of retrieval techniques that would have in the past been impractical. Optimal Estimation Methods (OEMs) are a class of techniques that use prior knowledge of a system, combined with the confidence to which system parameters are known, to retrieve the most likely value of unknown system quantities. While OEMs are already widely used in passive remote sensing, here an OEM is 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 from multiple channels. 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. When measurements from multiple channels are used that cover different height ranges, vertical resolutions and even different detection methods, a single temperature profile can be retrieved without requiring the combination of the photocount profiles from different channels before the retrieval or merging multiple temperature profiles afterward. 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 both active and passive remote sensing instruments.