Comparison Of Precipitable Water Vapor Research Articles

Comparison of precipitable water vapor derived from AIRS and SPM measurements and its correlation with surface temperature of 29 synoptic stations over Iran

Distribution of atmospheric water vapor is highly variable in time and space across the Earth; the knowledge of its distribution is essential in climate studies. In Iran, atmospheric water vapor measurements using conventional ground-based methods such as radiosondes are limited. In this paper, a comparison of precipitable water vapor (PWV) estimated by a ground-based sunphotometer (SPM) with atmospheric infrared sounder (AIRS) satellite sensor is presented over Zanjan, a city in northwest Iran for a period from December 2009 to December 2013. The PWV of SPM and AIRS have a good agreement (R2 = 93%). The seasonal bias between PWV estimated by SPM and AIRS (PWV SPM - PWV AIRS) over Zanjan city is −3.35, −5.15, −2.27, −0.95 mm during spring, summer, autumn, and winter, respectively. Average of AIRS PWV over Iran shows that the largest amount of PWV occurs along the seas (Caspian Sea, Oman Sea, and the Persian Gulf) and its lowest value happens in high altitude mountainous regions of the country. Also, correlation coefficients between AIRS PWV and surface temperature of 29 synoptic stations are calculated over Iran from September 2002 to December 2016. The average of correlation coefficients over the stations is 0.73.

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

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 ground-based 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 ground-based retrievals. The two closure activities – done for clear-sky scenes – use the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG- and MILO-corrected 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.

GPS-Meteorology network in Wuhan region

There are three aspects in the study of GPS meteorology network in the Wuhan region. The first is the comparison of the GPS precipitable water vapor between final ephemeris and ultra-rapid ephemeris for which the relative coefficient is 99.97 and the root mean squares is 0.048 mm. It can be concluded that ultra-rapid ephemeris can be used to get the GPS precipitable water vapor for the real-time prediction. The second is the comparison of precipitable water vapor of GPS stations and the distribution of water vapor in the Wuhan region is acquired. The change of GPS precipitable water vapor and rainfall in a rainfall process are compared and analyzed. The change of GPS precipitable water vapor can reflect and predict the process of rainfall.

Correction for Radiation Dry Bias Found in RS92 Radiosonde Data during the MISMO Field Experiment

Atmospheric soundings using the Vaisala RS92 radiosonde were intensively conducted during the field experiment MISMO (Mirai Indian Ocean cruise for the Study of the MJO-convection Onset) in the central and eastern equatorial Indian Ocean from October to December 2006. By comparing the RS92 relative humidity data with that from the Meteolabor Snow White (SW) chilled-mirror dew/frost-point hygrometers launched on the same ship around the local noon time, the dry bias was found to increase significantly with height. In addition, it was also revealed that the dry bias had a clear diurnal variation with its maximum at local noon and near-zero at night from the comparison of precipitable water vapor (PWV) with that derived from the shipboard Global Positioning System (GPS) data. Therefore, the dry bias of the RS92 data could be attributed to a solar radiation-induced error that was recently discussed by V omel et al. (2007). In this study, we developed a correction scheme for the MISMO RS92 humidity data as a function of pressure and local time using SW data, and then confirmed its validity with GPS-derived PWV.

Experiment on driving precipitable water vapor from ground-based GPS network in Chengdu Plain

The estimates of total zenith delay are derived using Bernese GPS Software V4. 2 based on GPS data every 30 s from the first measurement experiment of a ground-based GPS network in Chengdu Plain of Southwest China during the period from July to September 2004. Then the estimates of 0.5 hourly precipitable water vapor (PWV) derived from global positioning system (GPS) are obtained using meteorological data from automatic weather stations (AWS). The comparison of PWV derived from GPS and those from radiosonde observations is given for the Chengdu station, with RMS (root mean square) differences of 3.09m. The consistency of precipitable water vapor derived from GPS to those from radiosonde is good. It is concluded that Bevis’ empirical formula for estimating the weighted atmospheric mean temperature can be applicable in Chengdu area because the relationship of GPS PWV with Bevis’ formula and GPS PWV with radiosonde method shows a high correlation. The result of this GPS measurement experiment is helpful both for accumulating the study of precipitable water vapor derived from GPS in Chengdu areas located at the eastern side of the Tibetan Plateau and for studying spatial-temporal variations of regional atmospheric water vapor through many disciplines cooperatively.

Comparison of precipitable water vapor derived from radiosonde, GPS, and Moderate‐Resolution Imaging Spectroradiometer measurements

Atmospheric water vapor is highly variable in both space and time across the Earth, and knowledge of the distribution of water vapor is essential in understanding weather and global climate. In addition, knowledge of the amount of atmospheric water vapor is required for high‐precision interferometric synthetic aperture radar (InSAR) applications due to its significant impact on microwave signals, which is the principal motivation for this study. In order to assess the performance of different instruments, i.e., radiosondes (RS), Global Positioning System (GPS), and the Moderate‐Resolution Imaging Spectroradiometer (MODIS) and for measuring precipitable water vapor (PWV), coincident observations collected at the Atmospheric Radiation Measurement Southern Great Plains site and at the Herstmonceux site over a 8–11 month period are used for time series intercomparisons. In this study, the Terra MODIS near‐infrared water vapor products (Collection 3) were examined. In addition, a first spatial comparison of MODIS PWV and GPS PWV was performed using data covering all of Germany and kindly supplied by the GeoForschungsZentrum Potsdam. Time series comparisons of PWV between radiosondes and GPS show that the scale factors of PWV from radiosondes and GPS agreed to 4% with correlation coefficients higher than 0.98 and standard deviations about 1 mm. A significant day‐night difference was found for Vaisala RS90 radiosondes in comparison with GPS PWV, with nighttime launches having a scale factor 4% larger, but agreeing overall better. It is also shown that GPS PWV and RS PWV agreed better with each other than with MODIS PWV, and the differences of MODIS PWV relative to GPS or RS were larger than those between GPS PWV and RS PWV. MODIS PWV appeared to overestimate PWV against RS, with scale factors from 1.14 to 1.20 and standard deviations from 1.6 to 2.2 mm. MODIS PWV appeared to overestimate PWV against GPS, with scale factors from 1.07 to 1.14 and standard deviations varying from 0.8 to 1.4 mm in time series. The larger differences relative to MODIS PWV are likely to be caused by uncertainties in the spectroscopic database for the MODIS retrievals, calibration uncertainties in the radiances measured by MODIS, operational differences of the three systems, and different mapping functions adopted in GPS and MODIS PWV retrievals. We derived a linear fit model to calibrate MODIS PWV, and better agreements between calibrated MODIS PWV and GPS PWV in space have been achieved. This indicates that MODIS PWV products should be updated or calibrated using a linear fit model before being applied to correct InSAR measurements. Also, the potential accuracy of standard resolution (resampled) radiosonde data from the UK Met Office and the University of Wyoming has been assessed. It is demonstrated that some caution needs to be exercised when using standard resolution data.