A detection of atmospheric relative humidity profile by UV Raman lidar

Hua

et al. 2012

Jpn. J. Appl. Phys.

Self Cite

A UV vibrational Raman lidar has been built and used to make quantitative measurements of water vapor and aerosol optical properties over Xi'an, China. Vertical profiles of the water vapor mixing ratio and aerosol extinction coefficient are retrieved. The water vapor mixing ratio is calibrated with radiosonde data. The diurnal variations of the water vapor mixing ratio, aerosol extinction coefficient, and aerosol optical depth are obtained. The results obtained in the form of a time-height indicator (THI) display clearly showed the relationship between water vapor and aerosols, in which the gradual enhancement of water vapor density results in aerosol accumulation in the early morning, in particular in the lower troposphere. The seasonal variations of the water vapor mixing ratio and aerosol optical depth over Xi'an were observed and analyzed using the average monthly distribution obtained by lidar for the first time, which will provide useful scientific data and real-time monitoring methods for studying local climate change. #

Section: System and Data Retrieval Methodsmentioning

confidence: 99%

“…Using the definition of the water vapor mixing ratio, the water vapor mixing ratio can be calculated by using the ratio of the Raman signals of H 2 O and N 2 and is given as 16) …”

Section: System and Data Retrieval Methodsmentioning

confidence: 99%

Observations and Analysis of Relationship between Water Vapor and Aerosols by Using Raman Lidar

Hua

et al. 2012

Jpn. J. Appl. Phys.

Self Cite

“…Radiosounding is the most widely used method for temperature profiling in the troposphere and lower stratosphere and has the advantage of operation in most weather conditions, but is typically limited to two flights per day only at selected sites worldwide. Pure rotational Raman (PRR) lidars have excellent vertical and temporal resolution and can be combined with vibrational Raman channels to determine relative humidity (Mattis et al, 2002;Wang et al, 2011;Behrendt et al, 2002;Reichardt et al, 2012). Lidar temperature measurements can be assimilated with atmospheric models to improve weather forecasts, as recently demonstrated by Adam et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Retrieval of temperature from a multiple channel pure rotational Raman backscatter lidar using an optimal estimation method

et al. 2019

Abstract. We present a new method for retrieving temperature from pure rotational Raman (PRR) lidar measurements. Our optimal estimation method (OEM) used in this study uses the full physics of PRR scattering and does not require any assumption of the form for a calibration function nor does it require fitting of calibration factors over a large range of temperatures. The only calibration required is the estimation of the ratio of the lidar constants of the two PRR channels (coupling constant) that can be evaluated at a single or multiple height bins using a simple analytic expression. The uncertainty budget of our OEM retrieval includes both statistical and systematic uncertainties, including the uncertainty in the determination of the coupling constant on the temperature. We show that the error due to calibration can be reduced significantly using our method, in particular in the upper troposphere when calibration is only possible over a limited temperature range. Some other advantages of our OEM over the traditional Raman lidar temperature retrieval algorithm include not requiring correction or gluing to the raw lidar measurements, providing a cutoff height for the temperature retrievals that specifies the height to which the retrieved profile is independent of the a priori temperature profile, and the retrieval's vertical resolution as a function of height. The new method is tested on PRR temperature measurements from the MeteoSwiss RAman Lidar for Meteorological Observations system in clear and cloudy sky conditions, compared to temperature calculated using the traditional PRR calibration formulas, and validated with coincident radiosonde temperature measurements in clear and cloudy conditions during both daytime and nighttime.

Journal of Atmospheric and Oceanic Technology

“…Our ERA5-reRH relative humidity can also be compared to a previous determination of relative humidity using a combination of 2 Raman lidars, one which measured rotational temperature and the other water vapour mixing ratio (Wang et al 2011). These measurements were then combined into a relative humidity profile which was compared to a radiosonde measurement.…”

mentioning

confidence: 99%

A 1D Var Retrieval of Relative Humidity Using the ERA5 Dataset for the Assimilation of Raman Lidar Measurements

Gamage

Sica

Martucci

et al. 2020

We present a 1-dimensional-variational (1D Var) retrieval of fifth generation European Centre for Medium-range Weather Forecast Re-Analysis (ERA5) temperature and relative humidity profiles above Payerne, Switzerland, assimilating raw backscatter measurements from the MeteoSwiss RAman lidar for Meteorological Observations (RALMO). Our reanalysis is called ERA5-reRH. We use an optimal estimation method to perform the 1D Var data retrieval. The forward model combines the Raman lidar equation with the Hyland and Wexler expression for water vapor saturation pressure. The error covariance matrix of ERA5 was derived from the differences between ERA5 and a set of 50 special radiosoundings which have not been assimilated into ERA5. We validate ERA5-reRH, ERA5 and RALMO temperature and relative humidity profiles against the same set of special radiosoundings and found the best agreement was with our reanalysis, with a bias of less than 2% relative humidity with respect to water (%RHw) and a spread of less than 8%RHw below 8 km in terms of relative humidity. Improvements for temperature in our reanalysis are only found in the boundary layer, as ERA5 assimilates a large number of upper air temperature observations. Our retrieval also provides a full uncertainty budget of the reanalyzed temperature and relative humidity including both random and systematic uncertainties.