Department

Physics and Astronomy

Program

Astronomy, PhD

Year

4

Supervisor Name

Robert Sica

Supervisor Email

sica@uwo.ca

Abstract Text

Water vapor plays a critically important role in many atmospheric processes. However, it is poorly characterized throughout much of the atmosphere, particularly in the UTLS (Upper Troposphere Lower Stratosphere) region, due to lack of accurate measurements. Raman lidar boasts the capacity for excellent spatial and temporal resolution, but requires an external calibration. Microwave radiometers can be calibrated in absolute terms, but have poor height resolution. In this study, we introduce an integrated water vapor retrieval using an optimal estimation method, where the measurements from the Raman Lidar for Meteorological Observation (RALMO) and a RPG-HATPRO radiometer, both located at the MeteoSwiss station in Payerne, Switzerland. We consider two radiometer forward models for characterizing the radiometer: ARTS2 (Eriksson et al. 2011) and a “lightweight” radiative model (Schroeder & Westwater 1991), comparing and analyzing their performance. The radiometer forward model is combined with a lidar forward model (Sica & Haefele 2016) to yield a forward model capable of retrieval of a calibrated lidar water vapor profile.

In progress (data not fully collected)

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

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Integrated Raman Lidar and Microwave Radiometer Retrieval of Atmospheric Water Vapor

Water vapor plays a critically important role in many atmospheric processes. However, it is poorly characterized throughout much of the atmosphere, particularly in the UTLS (Upper Troposphere Lower Stratosphere) region, due to lack of accurate measurements. Raman lidar boasts the capacity for excellent spatial and temporal resolution, but requires an external calibration. Microwave radiometers can be calibrated in absolute terms, but have poor height resolution. In this study, we introduce an integrated water vapor retrieval using an optimal estimation method, where the measurements from the Raman Lidar for Meteorological Observation (RALMO) and a RPG-HATPRO radiometer, both located at the MeteoSwiss station in Payerne, Switzerland. We consider two radiometer forward models for characterizing the radiometer: ARTS2 (Eriksson et al. 2011) and a “lightweight” radiative model (Schroeder & Westwater 1991), comparing and analyzing their performance. The radiometer forward model is combined with a lidar forward model (Sica & Haefele 2016) to yield a forward model capable of retrieval of a calibrated lidar water vapor profile.