Retrieval of water vapor mixing ratio from a multiple channel Raman-scatter lidar using an optimal estimation method

Validation of the Water Vapor Profiles of the Raman Lidar at the Maïdo Observatory (Reunion Island) Calibrated with Global Navigation Satellite System Integrated Water Vapor

The Maïdo high-altitude observatory located in Reunion Island (21 ∘ S, 55.5 ∘ E) is equipped with the Lidar1200, an innovative Raman lidar designed to measure the water vapor mixing ratio in the troposphere and the lower stratosphere, to perform long-term survey and processes studies in the vicinity of the tropopause. The calibration methodology is based on a GNSS (Global Navigation Satellite System) IWV (Integrated Water Vapor) dataset. The lidar water vapor measurements from November 2013 to October 2015 have been calibrated according to this methodology and used to evaluate the performance of the lidar. The 2-year operation shows that the calibration uncertainty using the GNSS technique is in good agreement with the calibration derived using radiosondes. During the MORGANE (Maïdo ObservatoRy Gaz and Aerosols NDACC Experiment) campaign (Reunion Island, May 2015), CFH (Cryogenic Frost point Hygrometer) radiosonde and Raman lidar profiles are compared and show good agreement up to 22 km asl; no significant biases are detected and mean differences are smaller than 9% up to 22 km asl.

Water Vapor Calibration: Using a Raman Lidar and Radiosoundings to Obtain Highly Resolved Water Vapor Profiles

We revised the calibration of a water vapor Raman lidar by co-located radiosoundings for a site in the high European Arctic. For this purpose, we defined robust criteria for a valid calibration. One of these criteria is the logarithm of the water vapor mixing ratio between the sonde and the lidar. With an error analysis, we showed that for our site correlations smaller than 0.95 could be explained neither by noise in the lidar nor by wrong assumptions concerning the aerosol or Rayleigh extinction. However, highly variable correlation coefficients between sonde and consecutive lidar profiles were found, suggesting that small scale variability of the humidity was our largest source of error. Therefore, not all co-located radiosoundings are useful for lidar calibration. As we assumed these changes to be non-systematic, averaging over several independent measurements increased the calibration’s quality. The calibration of the water vapor measurements from the lidar for individual profiles varied by less than ±5%. The seasonal median, used for calibration in this study, was stable and reliable (confidence ±1% for the season with most calibration profiles). Thus, the water vapor mixing ratio profiles from the Koldewey Aerosol Raman Lidar (KARL) are very accurate. They show high temporal variability up to 4 km altitude and, therefore, provide additional, independent information to the radiosonde.

Improved ozone DIAL retrievals in the upper troposphere and lower stratosphere using an optimal estimation method

We have implemented a first-principle optimal estimation method to retrieve ozone density profiles using simultaneously tropospheric and stratospheric differential absorption lidar (DIAL) measurements. Our retrieval extends from 2.5 km to about 42 km in altitude, and in the upper troposphere and the lower stratosphere (UTLS) it shows a significant improvement in the overlapping region, where the optimal estimation method (OEM) can retrieve a single ozone profile consistent with the measurements from both lidars. Here stratospheric and tropospheric measurements from the Observatoire de Haute Provence are used, and the OEM retrievals in the UTLS region compared with coincident ozonesonde measurements. The retrieved ozone profiles have a small statistical uncertainty in the UTLS region relative to individual determinations of ozone from each lidar, and the maximum statistical uncertainty does not exceed a maximum of 7%.

Twin scanning lidars for accurate measurement of lower tropospheric aerosols by numerical approximation

In order to improve accuracy of aerosol measurements, a novel method using twin scanning lidars is presented; this method is able to overcome the incomplete overlap range of vertical lidar as well as provide 2D spatial distributions. The scanning lidar setups in the opposite directions are employed as remote sensing tools. Aerosol measurements are performed with cross scanning from the ground to the height of interest. Aerosol optical properties are retrieved using numerical approximation, in which differences between the measured values and the constructed values of the logarithmic range-square-corrected lidar data in the cross-scanning region are minimized. In the data retrieval, we utilize a matrix formulation, in which a Cartesian 2D range-height-indicator diagram is constructed. To verify this method, scanning measurements by ultraviolet Mie scanning lidar performed at different time intervals were taken as the cross-scanning measurements from the twin scanning lidars. With the retrieved spatial distributions of aerosol optical properties, such as aerosol backscatter, aerosol extinction, and lidar ratio, the regional aerosol studies showed that aerosol loading was relatively small and in the presence of multiple layers, which may be influenced by airflow from long-range transportation and cause a large impact on the local environment. To conclude, the presented method using twin scanning lidars is feasible for aerosol measurement in the application of horizontally atmospheric inhomogeneity.

How to apply the optimal estimation method to your lidar measurements for improved retrievals of temperature and composition

The optimal estimation method (OEM) has a long history of use in passive remote sensing, but has only recently been applied to active instruments like lidar. The OEM’s advantage over traditional techniques includes obtaining a full systematic and random uncertainty budget plus the ability to work with the raw measurements without first applying instrument corrections. In our meeting presentation we will show you how to use the OEM for temperature and composition retrievals for Rayleigh-scatter, Ramanscatter and DIAL lidars.

A Calibration of the MeteoSwiss RAman Lidar for Meteorological Observations (RALMO)Water Vapour Mixing Ratio Measurements using a Radiosonde Trajectory Method

With only 50% downtime from 2007-2016, the RALMO lidar in Payerne, Switzerland, has one of the largest continuous lidar data sets available. These measurements will be used to produce an extensive lidar water vapour climatology using the Optimal Estimation Method introduced by Sica and Haefele (2016). We will compare our improved technique for external calibration using radiosonde trajectories with the standard external methods, and present the evolution of the lidar constant from 2007 to 2016.

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.