A Comparison of Water Vapor Measurements Made by Raman Lidar and Radiosondes

Ferrare, R. A.; Melfi, S. H.; Whiteman, David N.; Evans, K. D.; Schmidlin, F. J.; Starr, David Oc.

doi:10.1175/1520-0426(1995)012<1177:acowvm>2.0.co;2

Cited by 109 publications

(63 citation statements)

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“…Ferrare et al (1995) showed that a RS80 radiosonde had a dry bias regardless of temperature or relative humidity. Fig.…”

Section: Comparison Results and Analysismentioning

confidence: 99%

Radiosonde Sensors Bias in Precipitable Water Vapor From Comparisons With Global Positioning System Measurements

Park¹,

Roh²,

Cho³

2012

Journal of Astronomy and Space Sciences

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In this study, we compared the precipitable water vapor (PWV) data derived from the radiosonde observation data at Sokcho Observatory and the PWV data at Sokcho Global Positioning System (GPS) Observatory provided by Korea Astronomy and Space Science Institute, for the years of 2006, 2008, 2010, and analyzed the radiosonde seasonal, diurnal bias according to radiosonde sensor types. In the scatter diagram of the daytime and nighttime radiosonde PWV data and the GPS PWV data, dry bias was found in the daytime radiosonde observation as known in the previous study. Overall, the tendency that the wet bias of the radiosonde PWV increased as the GPS PWV decreased and the dry bias of the radiosonde PWV increased as the GPS PWV increased. The quantitative analysis of the bias and error of the radiosonde PWV data showed that the mean bias decreased in the nighttime except for 2006 winter, and in comparison for summer, RS92-SGP sensor showed the highest quality.

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“…Ferrare et al (1995) showed that a RS80 radiosonde had a dry bias regardless of temperature or relative humidity. Fig.…”

Section: Comparison Results and Analysismentioning

confidence: 99%

Radiosonde Sensors Bias in Precipitable Water Vapor From Comparisons With Global Positioning System Measurements

Park¹,

Roh²,

Cho³

2012

Journal of Astronomy and Space Sciences

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“…In previous experiments (Ferrare et al, 1995;Herold et al, 2011), radiosondes showed a significant sonde-to-sonde variability (Nash et al, 2006) as well as a dry bias (Turner et al, 2003). For that reason, water vapour Raman lidars were often calibrated based on the IWV or integrated precipitable Using the IWV method, C H 2 O is defined as the ratio of the IWV measured with the MWR and the integrated signal ratio from the lidar.…”

Section: Iwv Methodsmentioning

confidence: 99%

Water vapour profiles from Raman lidar automatically calibrated by microwave radiometer data during HOPE

et al. 2015

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Abstract. In this paper, we present a method to derive water vapour profiles from Raman lidar measurements calibrated by the integrated water vapour (IWV) from a collocated microwave radiometer during the intense observation campaign HOPE in the frame of the HD(CP) 2 initiative. The simultaneous observation of a microwave radiometer and a Raman lidar allowed an operational and continuous measurement of water vapour profiles also during cloudy conditions. The calibration method provides results which are in a good agreement with conventional methods based on radiosondes. The calibration factor derived from the proposed IWV method is very stable with a relative uncertainty of 5 %. This stability allows for the calibration of the lidar even in the presence of clouds using the calibration factor determined during the most recent clear sky interval. Based on the application of this approach, it is possible to retrieve water vapour profiles during all non-precipitating conditions. A statistical analysis shows a good agreement between the lidar measurements and collocated radiosondes. The relative biases amount to less than 6.7 % below 2 km.

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“…Numerous studies have focused on modeling processes associated with water vapor and evaluating and improving water vapor observations (e.g., Ferrare et al, 1995Ferrare et al, , 2006Revercomb et al, 2003;Suortti et al, 2008;Krämer et al, 2009;Moradi et al, 2013a, b). Accurate measurements of water vapor are especially crucial in the upper troposphere; although very little water vapor is present in this part of the atmosphere (e.g., Ferrare et al, 2004), processes such as cirrus cloud formation and maintenance (Liou, 1986) and maintenance of stratospheric water vapor (e.g., Jensen et al, 1996a, b;Hartmann et al, 2001) require very accurate knowledge of the upper-tropospheric water vapor budget.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2016

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Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km m.s.l.), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS).The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from groundbased microwave radiometers at tropical, midlatitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the groundbased retrievals.The two closure activities -done for clear-sky scenesuse the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG-and MILOcorrected RHs are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover -a condition not accounted for in the radiance closure experiments.

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A Comparison of Water Vapor Measurements Made by Raman Lidar and Radiosondes

Cited by 109 publications

References 0 publications

Radiosonde Sensors Bias in Precipitable Water Vapor From Comparisons With Global Positioning System Measurements

Radiosonde Sensors Bias in Precipitable Water Vapor From Comparisons With Global Positioning System Measurements

Water vapour profiles from Raman lidar automatically calibrated by microwave radiometer data during HOPE

Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

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