Investigation and Analysis of All-Day Atmospheric Water Vapor Content over Xi’an Using Raman Lidar and Sunphotometer Measurements

Abstract

All-day atmospheric water vapor content measurements determined by Raman lidar and a sunphotometer were combined to investigate the all-day variation characteristics in the water vapor distribution in Xi'an, China (34.233 • N, 108.911 • E). To enhance the daytime lidar performance, the wavelet threshold de-noising method is used to filter out the strong solar background light, and effective denoised results are demonstrated with the following optimization: wavelet sym6, the improved threshold function, and the improved threshold selection. The denoised system signal-to-noise ratio (SNR) for the water vapor daytime measurement is validated, with an enhancement of~3.4 times up to a height of 3 km compared to that of the original signal. The time series of the atmospheric water vapor mixing ratio profiles and the obtained precipitable water vapor (PWV) measured by Raman lidar are used to reveal the temporal and spatial variations in water vapor, and the comparisons with the total column water vapor content (TCWV) measured by a sunphotometer validate the daytime variation trend of the water vapor. All-day continuous observations clearly present a consistent variation trend in the water vapor between the sunphotometer and Raman lidar measurements. The correlation analysis between TCWV and PWV at the layers below 850 hPa and below 700 hPa yields a good positive correlation coefficient (>0.75), indicating that PWV determination in the bottom layer by Raman lidar can directly reflect the variations in the total water vapor content. Moreover, different diurnal variation trends in water vapor are also observed, that is, a downward trend from the afternoon to the night, or a tendency of being high in the morning and afternoon and low at noon, demonstrating the high temporal-spatial variation characteristics of water vapor and close correlation with weather changes. The results reflected and validated that the diurnal variation in water vapor is complicated and can be an indicator of the weather to a certain extent.

Linear regression models to predict the tropospheric parameters at the Global Positioning systems’ sites over the East African region

“…The estimation of the PWV involves different observational standard methods which require observations from GPS, radiosondes, Light Detection and Ranging (LIDAR) systems, Water Vapour Radiometers (WVR) among others (Singh et al, 2014;Chen et al, 2018;Wang et al, 2018;Hicks-Jalali et al, 2020). Likewise calculations and procedures to estimate PWV using linear regression methods have been explained e.g.…”