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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Synergy of Raman Lidar and Modeled Temperature for Relative Humidity Profiling: Assessment and Uncertainty Analysis
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Synergy of Raman Lidar and Modeled Temperature for Relative Humidity Profiling: Assessment and Uncertainty Analysis

机译：Synergy of Raman Lidar and Modeled Temperature for Relative Humidity Profiling: Assessment and Uncertainty Analysis

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摘要

Relative humidity (RH) profiling using Raman lidars requires simultaneous range-resolved temperature and pressure data that are not always available. We propose and assess a method based on the use of a locally retrieved atmospheric model to estimate the temperature and pressure profiles. This model relies on the data from daily radiosonde launches at Barcelona during a five-year-long period (2015–2019). We have computed the range-resolved error of the model compared with the radiosonde “true” temperature profiles. Then, we have calculated the induced uncertainty in the recalculated RH profiles, finding that the standard deviation at 5 km is 15 for more than 80 of the radiosoundings. We have applied the method to a set of lidar measurements. We have first compared the resulting profiles with lidar retrievals using radiosonde temperatures, and we found differences that are compatible with the previous statistical analysis. We also compared them with italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"in situ/i radiosonde humidity measurements, finding, in this case, bigger differences due to additional sources of error. Uncertainty analysis shows that the temperature model is the most significant error source below 4–5 km. For higher altitudes, the noise of the Raman signals may become the main contribution. We show that the resulting uncertainty (commonly 15 below 5 km) is compatible with the statistical analysis of the model and comparable with the ones obtained using other instruments for temperature profiling. We show that this method permits nocturnal lidar-based RH profiling with uncertainty estimation without the need for measured atmospheric profiles.

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