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)
Supervisor Consent
yes
Included in
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.