Synergy of Raman lidar and modeled temperature for relative humidity profiling: assessment and uncertainty analysis
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...
| Autores: | , , , , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/335673 |
| Acesso em linha: | https://hdl.handle.net/2117/335673 https://dx.doi.org/10.1109/TGRS.2020.3039689 |
| Access Level: | acceso abierto |
| Palavra-chave: | Remote sensing Optical radar Humidity Temperature measurement Laser radar Atmospheric measurements Atmospheric modeling Uncertainty Instruments Nitrogen Raman lidar Relative humidity (RH) Water vapor Teledetecció Radar òptic Humitat atmosfèrica Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar |
| Resumo: | 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 in situ 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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