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Article summary:
1. A new technique for retrieving atmospheric temperature profiles from Rayleigh-scatter lidar measurements is presented, which uses a nonlinear inversion approach.
2. This technique allows temperatures to be reliably retrieved at all heights where measurements with adequate signal-to-noise ratio exist, and extends the altitude range of existing Rayleigh-scatter lidars by 10–15 km.
3. The method is based on the assumption that the atmosphere behaves as an ideal gas and can be considered static and in hydrostatic equilibrium at the spatial-temporal resolution at which the measurements are obtained.
Article analysis:
The article presents a new technique for retrieving atmospheric temperature profiles from Rayleigh-scatter lidar measurements using a nonlinear inversion approach. The article is well written and provides a detailed description of the method, including its advantages over existing methods and how it works. The authors provide evidence to support their claims, such as citing previous studies and providing examples of how their method works in practice.
However, there are some potential biases that should be noted when evaluating this article. First, the authors do not explore any counterarguments or alternative approaches to their proposed method. Additionally, they do not discuss any potential risks associated with their method or possible limitations that could arise from its implementation. Furthermore, they do not present both sides of the argument equally; instead they focus solely on promoting their own approach without considering other perspectives or solutions. Finally, while they cite previous studies to support their claims, they do not provide any evidence for the accuracy or reliability of their results beyond what is provided in those studies.
In conclusion, while this article provides a detailed description of a new technique for retrieving atmospheric temperature profiles from Rayleigh-scatter lidar measurements using a nonlinear inversion approach, there are some potential biases that should be taken into consideration when evaluating its trustworthiness and reliability.