Calibration of a water vapour Raman lidar with a kite-based humidity  sensor

Totems, Julien; Chazette, Patrick

doi:10.5194/amt-9-1083-2016

Cited by 11 publications

(15 citation statements)

References 41 publications

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“…We developed our MRL system to meet these requirements as much as possible within the total material cost of ∼ USD 250 000. The Raman lidar technique is a well-established technique for measuring the water vapor distribution in the troposphere (e.g., Melfi et al, 1969;Whiteman et al, 1992), and the systems have been in operation for decades at stations around the world (Turner et al, 2016;Dinoev et al, 2013;Reichardt et al, 2012;Leblanc et al, 2012). Field-deployable systems have also been developed by several institutes (Whiteman et al, 2012;Chazette et al, 2014;Engelmann et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Automated compact mobile Raman lidar for water vapor measurement: instrument description and validation by comparison with radiosonde, GNSS, and high-resolution objective analysis

et al. 2019

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We developed an automated compact mobile Raman lidar (MRL) system for measuring the vertical distribution of the water vapor mixing ratio (w) in the lower troposphere, which has an affordable cost and is easy to operate. The MRL was installed in a small trailer for easy deployment and can start measurement in a few hours, and it is capable of unattended operation for several months. We describe the MRL system and present validation results obtained by comparing the MRL-measured data with collocated radiosonde, Global Navigation Satellite System (GNSS), and high-resolution objective analysis data. The comparison results showed that MRL-derived w agreed within 10 % (rootmean-square difference of 1.05 g kg −1 ) with values obtained by radiosonde at altitude ranges between 0.14 and 1.5 km in the daytime and between 0.14 and 5-6 km at night in the absence of low clouds; the vertical resolution of the MRL measurements was 75-150 m, their temporal resolution was less than 20 min, and the measurement uncertainty was less than 30 %. MRL-derived precipitable water vapor values were similar to or slightly lower than those obtained by GNSS at night, when the maximum height of MRL measurements exceeded 5 km. The MRL-derived w values were at most 1 g kg −1 (25 %) larger than local analysis data. A total of 4 months of continuous operation of the MRL system demonstrated its utility for monitoring water vapor distributions in the lower troposphere.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Section: Introductionmentioning

confidence: 99%

Automated compact mobile Raman lidar for water vapor measurement: instrument description and validation by comparison with radiosonde, GNSS, and high-resolution objective analysis

et al. 2019

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“…Water vapor has a large impact on the thermodynamic state of the atmosphere and is the most important greenhouse gas (Twomey, 1991). The relative humidity (RH) represents the water vapor content of an atmospheric volume and is also one of the most important atmospheric state parameters.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data

et al. 2018

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We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar Polly XT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor from the lidar observations is used for the calibration of the lidar measurements with the sun photometer measurements. The retrieved calibrated water vapor mixing ratio from the lidar measurements has a relative uncertainty of 11 % in which the error is mainly caused by the error of the sun photometer measurements. During CyCARE, nine measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the Polly XT Raman lidar is 6.56 g kg −1 ± 0.72 g kg −1 (with a statistical uncertainty of 0.08 g kg −1 and an instrumental uncertainty of 0.72 g kg −1 ). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R 2 of 0.99 is found. One example is presented to demonstrate the full potential of a well-calibrated Raman lidar. The relative humidity profiles from lidar, GDAS (simulation) and radiosonde are compared, too. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with the relative uncertainty of 10-20 %.

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“…The WVMR is obtained as the ratio between the H 2 O and N 2 Raman signals. In previous campaigns, during night‐time and after proper calibration, the uncertainty on the WVMR reached 6% below the 5 km range, after further averaging bringing the temporal resolution to 20 min and the vertical resolution to 15 m (Totems and Chazette, ). During daytime, measurements are more challenging due to sunlight, but have been performed with ∼10% precision for altitude ranges below ∼1 km using temporal averaging over 30 min.…”

Section: Methodsmentioning

confidence: 98%

“…As in Totems and Chazette (2016), from the H 2 O-Raman lidar, the profile of water vapour mixing ratio r H2O is obtained as:…”

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confidence: 99%

Accuracy of current Arctic springtime water vapour estimates, assessed by Raman lidar

Totems

Chazette

Raut

2019

Quart J Royal Meteoro Soc

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Current estimates of Arctic water vapour, obtained using reanalysis, numerical weather predictions and satellite products may be biased due to the concentration of moisture in the lowest levels, below 2–3 km. Here we investigate the water vapour content of the atmosphere during 2 weeks in May 2016, as estimated with (a) the ECMWF/Integrated Forecast System reanalyses; (b) Weather Research and Forecasting (WRF) simulations at high resolution; and (c) satellite retrievals from AIRS/Aqua and IASI/MetOp. We compare such estimates to a unique dataset of uninterrupted Raman lidar observations obtained at Hammerfest, Norway (70°N, 24°E), within the framework of the Pollution in the ARCtic System (PARCS) project. Ground‐based lidar profiles were acquired between 150 and 4,000 m altitude, with a vertical and temporal resolution of 15–200 m and 30 min, respectively. A strong variability of the water vapour field was observed in the lower troposphere in this period, ranging 0.43–6.35 g/kg. Dry air intrusions from the polar vortex, alternating with wet air intrusions associated with lows over the ocean have been revealed, creating strong gradients of water vapour, of the order of 2 g/kg per hour and per km. The satellite retrievals showed a good correlation with the measurements, despite a relatively large dispersion and a slight dry bias of the IASI level 2 product (standard deviation up to 1.5 g/kg). The ECMWF/IFS reanalysis and WRF both showed a wet bias below 1 km altitude (+0.4 g/kg), coherent with previous comparisons that made use of radiosondes.

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Calibration of a water vapour Raman lidar with a kite-based humidity sensor

Cited by 11 publications

References 41 publications

Automated compact mobile Raman lidar for water vapor measurement: instrument description and validation by comparison with radiosonde, GNSS, and high-resolution objective analysis

Automated compact mobile Raman lidar for water vapor measurement: instrument description and validation by comparison with radiosonde, GNSS, and high-resolution objective analysis

Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data

Accuracy of current Arctic springtime water vapour estimates, assessed by Raman lidar

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