Raman-scatter Lidar measurements of Water Vapor determined using an integrated Microwave Radiometer-Lidar retrieval

Water vapor plays a crucially important role in many atmospheric processes. However, it is poorly characterized in much of the atmosphere. Vibrational Raman-scattering Lidar has excellent spatial and temporal resolution, but requires an external calibration to correct for instrumental biases. Microwave Radiometers have poorer resolution, but can be calibrated absolutely and can be used to calibrate the Lidar system. I have implemented a new technique, incorporating both instruments to generate a calibrated water vapor mixing ratio profile. This integrated retrieval uses an inverse method which includes a combined forward model, integrating radiative transfer equations (Schroeder and Westwater 1991) and lidar equations (Sica and Haefele 2016) to account for both radiometer and lidar components. The retrieval uses lidar signal measurements from the RAman Lidar for Meteorological Observations (RALMO) and brightness temperatures from a RPG-HATPRO-G2 microwave radiometer, both located at the MeteoSwiss station in Payerne, Switzerland. The integrated retrieval is tested on synthetically-generated measurements, as well as real measurements from Payerne for clear day and nighttime observations. The performance of this retrieval is compared to the radiosonde-calibrated lidar retrieval technique of Sica and Haefele 2016 and Hicks-Jalali et al 2019, in which lidar constants are determined through a radiosonde-derived calibration factor. The integrated retrieval retrieves this factor directly, which is determined to be within 10\% of the radiosonde-derived value for most nighttime retrievals. Additionally, the uncertainties associated with the integrated method-retrieved factors are around 1.5\%, as opposed to approximately 5\% for the radiosonde-calibrated method. Integrated retrievals over 24-hour periods show diurnal shifts in the calibration factor, which are shown to vary seasonally in parallel with high background count rates in the daytime. For the retrieval of water vapor mixing ratio, the results from the two methods are similar, with retrieved humidity profiles determined with confidence extending into the upper troposphere for clear nights. The integrated retrieval also has the advantage of a lower total systematic uncertainty over the entire effective range of the retrieval, particularly in the lower troposphere. This method is thereby demonstrated to be a viable alternative to water vapor retrievals via radiosonde-calibrated lidar, with the potential to be incorporated into routine operation at the Payerne meteorological site

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

P07. Characterizing the Purple Crow Lidar to investigate potential sources of wet bias

The Purple Crow Lidar is a large aperture lidar, capable of retrieving water vapor profiles into the stratosphere. Water vapor in the upper Troposphere-Lower Stratosphere (UTLS) region is of particular importance in understanding Earth\u27s radiative budget and atmospheric dynamics, making accurate UTLS measurements crucial. A comparison campaign with the NASA/GSFC ALVICE mobile lidar in the spring of 2012 showed PCL water vapor measurements were consistently larger than those of ALVICE in the lower stratosphere, prompting an investigation to characterize the system. The investigation looks into how changes to the data processing approach, as well as applying additional instrumental corrections, would affect the water vapor mixing ratio. We also look into a retrieval of the mixing ratio using optimal estimation method (OEM), which should provide greater insight into the associated data processing parameters and uncertainties

Investigating potential wet bias in the Purple Crow Lidar water vapor measurements

The Purple Crow Lidar is a large aperture lidar, capable of retrieving water vapor into the strato-sphere. A comparison with the ALVICE lidar in 2012 showed water vapor measurements were consistently larger than those of ALVICE in the lower stratosphere, prompting an investigation of the system. Processing approaches and additional instrumental corrections are considered

Investigating potential wet bias in the Purple Crow Lidar water vapor measurements

The Purple Crow Lidar is a large aperture lidar, capable of retrieving water vapor into the strato-sphere. A comparison with the ALVICE lidar in 2012 showed water vapor measurements were consistently larger than those of ALVICE in the lower stratosphere, prompting an investigation of the system. Processing approaches and additional instrumental corrections are considered