Analysis of the application of the optical method to the measurements of the water vapor content in the atmosphere – Part 1: Basic concepts of the measurement technique

Галкин, В. Д.; Immler, Franz; Alekseeva, G. A.; Berger, F. H.; Leiterer, U.; Naebert, T.; Nikanorova, I. N.; Новиков, В. В.; Pakhomov, V. P.; Sal'nikov, I.B.

doi:10.5194/amt-4-843-2011

Cited by 6 publications

(10 citation statements)

References 18 publications

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“…As "a" represents the intercept of the T w -PWV fitting line, this result points out the impact of the filter function shape on the PWV retrieval. These results are consistent with those presented in Galkin et al (2011), where a dependence of the "a" parameter on the half-width and shape of the filter function was found. Meanwhile, the parameter "b" can be considered less affected by the transmission curve.…”

Section: Resultssupporting

confidence: 82%

“…Furthermore, they showed that a background transmittance ∼ 10 −4 led to an error in aerosol optical depth up to 10 % and significant variations in "a" and "b" parameters in the T w -PWV exponential dependence found by Bruegge et al (1992). Finally, Galkin et al (2011) showed that "a" parameter depends on the half-width and shape of the filter transmission curve, pressure, temperature and water vapor content, while "b" appears only to depend on the latter effect.…”

Section: Methodsmentioning

confidence: 99%

“…Holben et al (2001) estimated a precision in PWV from sun photometry of ∼ 10 % as a result of the method for PWV retrieval and the relatively large uncertainty in the V 0 determined using the Langley method in ∼ 940 nm channel. Galkin et al (2011) also estimated an uncertainty in photometry in ∼ 10 %, mainly affected by errors in instrument calibration.…”

Section: A Barreto Et Al: Column Water Vapor Determination In Nightmentioning

confidence: 99%

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Column water vapor determination in night period with a lunar photometer prototype

Barreto

Cuevas

Damiri³

et al. 2013

Atmos. Meas. Tech.

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In this paper we present the preliminary results of atmospheric column-integrated precipitable water vapor (PWV) obtained with a new Lunar Cimel photometer (LC) at the high mountain Izaña Observatory in the period July–August 2011. We have compared quasi-simultaneous nocturnal PWV from LC with PWV from a Global Positioning System (GPS) receiver and nighttime radiosondes (RS92). LC data have been calibrated using the Lunar Langley method (LLM). We complemented this comparative study using quasi-simultaneous daytime PWV from Cimel AERONET (CA), GPS and RS92. Comparison of daytime PWV from CA shows differences between GPS and RS92 up to 0.18 cm. Two different filters, with and approximate bandwidth of 10 nm and central wavelengths at 938 nm (Filter#1) and 937 nm (Filter#2), were mounted onto the LC. Filter#1 is currently used in operational AERONET sun photometers. PWV obtained with LC-Filter#1 showed an overestimation above 0.18 and 0.25 cm compared to GPS and RS92, respectively, and root-mean-square errors (RMSEs) up to 0.27 cm and 0.24 cm, respectively. Filter#2, with a reduced out-of-band radiation, showed very low differences compared with the same references (≤ 0.05 cm) and RMSE values ≤ 0.08 cm in the case of GPS precise orbits.

These results demonstrate the ability of the new lunar photometer to obtain accurate and continuous PWV measurements at night, and the remarkable influence of the filter's transmissivity response to PWV determination at nighttime. The use of enhanced bandpass filters in lunar photometry, which is affected by more important inaccuracies than sun photometry, is necessary to infer PWV with similar precision to AERONET

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Section: Resultssupporting

confidence: 82%

Section: Methodsmentioning

confidence: 99%

Section: A Barreto Et Al: Column Water Vapor Determination In Nightmentioning

confidence: 99%

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Column water vapor determination in night period with a lunar photometer prototype

Barreto

Cuevas

Damiri³

et al. 2013

Atmos. Meas. Tech.

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“…Sun photometers are widely used for the measurement of the water vapor. The theory of determining content of the vertical atmospheric column PWV by means of a sun photometer is described in detail in Halthore et al (1997), Smirnov et al (2004), Galkin et al (2011) and Barreto et al (2013). Ferrare et al (2006) compare the daytime water vapor measurements of a Raman lidar, sun photometer and radiosonde.…”

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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“…We only focus on accuracy aspects, leaving precision and calibration errors to be addressed in subsequent studies. We also avoid the non-linear complications associated with measurements in the water vapour absorption bands (in the neighbourhood of 940 nm): this subject has already been extensively described in the studies of Galkin and Arkharov (1981), Halthore et al (1997), and Galkin et al (2011Galkin et al ( , 2010.…”

Section: Introductionmentioning

confidence: 99%

Accuracy in starphotometry

et al. 2021

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Abstract. Starphotometry, the night-time counterpart of sunphotometry, has not yet achieved the commonly sought observational error level of 1 %: a spectral optical depth (OD) error level of 0.01. In order to address this issue, we investigate a large variety of systematic (absolute) uncertainty sources. The bright-star catalogue of extraterrestrial references is noted as a major source of errors with an attendant recommendation that its accuracy, particularly its spectral photometric variability, be significantly improved. The small field of view (FOV) employed in starphotometry ensures that it, unlike sun- or moonphotometry, is only weakly dependent on the intrinsic and artificial OD reduction induced by scattering into the FOV by optically thin clouds. A FOV of 45 arcsec (arcseconds) was found to be the best trade-off for minimizing such forward-scattering errors concurrently with flux loss through vignetting. The importance of monitoring the sky background and using interpolation techniques to avoid spikes and to compensate for measurement delay was underscored. A set of 20 channels was identified to mitigate contamination errors associated with stellar and terrestrial atmospheric gas absorptions, as well as aurora and airglow emissions. We also note that observations made with starphotometers similar to our High Arctic instrument should be made at high angular elevations (i.e. at air masses less than 5). We noted the significant effects of snow crystal deposition on the starphotometer optics, how pseudo OD increases associated with this type of contamination could be detected, and how proactive techniques could be employed to avoid their occurrence in the first place. If all of these recommendations are followed, one may aspire to achieve component errors that are well below 0.01: in the process, one may attain a total 0.01 OD target error.

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Analysis of the application of the optical method to the measurements of the water vapor content in the atmosphere – Part 1: Basic concepts of the measurement technique

Cited by 6 publications

References 18 publications

Column water vapor determination in night period with a lunar photometer prototype

Column water vapor determination in night period with a lunar photometer prototype

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

Accuracy in starphotometry

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