GPS-derived Precipitable Water Vapor Research Articles

Near real-time water vapor tomography using ground-based GPS and meteorological data: long-term experiment in Hong Kong

Abstract. Water vapor tomography is a promising technique for reconstructing the 4-D moisture field, which is important to the weather forecasting and nowcasting as well as to the numerical weather prediction. A near real-time 4-D water vapor tomographic system is developed in this study. GPS slant water vapor (SWV) observations are derived by a sliding time window strategy using double-difference model and predicted orbits. Besides GPS SWV, surface water vapor measurements are also assimilated as real time observations into the tomographic system in order to improve the distribution of observations in the lowest layers of tomographic grid. A 1-year term experiment in Hong Kong was carried out. The feasibility of the GPS data processing strategy is demonstrated by the good agreement between the time series of GPS-derived Precipitable Water Vapor (PWV) and radio-sounding-derived PWV with a bias of 0.04 mm and a root-mean-square error (RMSE) of 1.75 mm. Using surface humidity observations in the tomographic system, the bias and RMSE between tomography and radiosonde data are decreased by half in the ground level, but such improved effects weaken gradually with the rise of altitude until becoming adverse above 4000 m. The overall bias is decreased from 0.17 to 0.13 g m−3 and RMSE is reduced from 1.43 to 1.28 g m−3. By taking the correlation coefficient and RMSE between tomography and radiosonde individual profile as the statistical measures, quality of individual profile is also improved as the success rate of tomographic solution is increased from 44.44 to 63.82% while the failure rate is reduced from 55.56 to 36.18%.

An observation campaign of precipitable water vapor with multiple GPS receivers in western Java, Indonesia

A campaign was conducted from 23 July to 5 August 2010 to measure atmospheric precipitable water vapor (PWV) using five Global Positioning System (GPS) receivers, stationed at four different locations in Jakarta and Bogor, western Java, Indonesia. Radiosondes were launched at an interval of 6 h to validate the GPS-derived PWV data. The validation resulted in a root mean square error of 2 to 3 mm in PWV. The influence of atmospheric pressure and temperature on GPS-derived PWV was evaluated. A regular semi-diurnal pressure oscillation was observed, showing an amplitude ranging from 3 to 5 hPa, which corresponds to 1.1 to 1.8 mm in PWV. A nocturnal temperature inversion layer was observed in the radiosonde profiles, which resulted in an error of about 0.5 mm in PWV. From 26 to 29 July, there was a passage of distributed rain clouds over western Java, moving southwestward from the equator toward the Indian Ocean. A second precipitation event, with scattered rain clouds forming locally near Bogor, occurred on 2 August. Both events were observed also by a C-band Doppler Radar operated near Jakarta. The highest peak of GPS-derived PWV (about 67 mm) registered during the campaign occurred on 27 July, coinciding with the distributed rainfall event. Spatial variations in the estimated PWV between the four sites were enhanced before both the analyzed rainfall events, on 27 July and 2 August. Peaks in the temporal variability of PWV were also observed in conjunction with the two events. The results indicated a relation between the space-time inhomogeneity of GPS-PWV and rainfall events in the tropics.

GPS 관측소 기선 처리에 따른 가강수량 특성 분석

본 연구에서는 겨울철 특별관측기간(2012년 1월 5일에서 2월 29일)동안 강원지방기상청에 설치된 GPS 자료를 이용하여 가강수량을 산정하고 이를 라디오존데 가강수량 자료와 비교 분석하였다. GPS 자료를 후처리하기 위하여 Bernese 5.0 소프트웨어를 사용하였다. GANG 단독측위와 GANG, DAEJ의 비교적 짧은 거리의 두 지점만을 이용한 상대측위 결과에 따른 가강수량은 시간에 따른 변동폭이 크고 실제 환산된 가강수량에 비해 5배 정도 크게 나타났다. 이러한 대류권 절대 오차에 의한 오류를 제거하기 위한 방법으로 Xian Dao (BJFS), Ibaraki-ken (TSKB) 국제 IGS 사이트의 장거리 기선설정으로 GPS 후처리를 실시한 결과 라디오존데 관측값과 상관이 0.93, 평균편의오차가 0.67, 평균제곱근오차가 6.40 수준으로 나타났다. 또한 GPS 수신기 고도 차이로 발생할 수 있는 대류권 상대 오차를 제거하기위해 강원지방기상청과 아주 가까운 지점인 강릉 원주대학교에 설치된 GPS 자료를 추가하여 후처리한 결과 상관이 0.93, 평균편의오차가 0.61, 평균제곱근오차가 5.79로 보다 개선된 결과를 보였다. In this study the GPS Precipitable Water Vapor (PWV) was derived and evaluated by a radiosode measure during the winter intensive observation in Gangneung site from January 5 till February 29 in 2012. Bernise 5.0 software was used to derive the GPS data. GPS-derived PWV from Zero difference (GANG) and Single difference (GANG and DAEJ) was high variance in time and about 5 times the PWV of radiosonde. GPS post-processing has been performed from two additional IGS site (Xian Dao, Ibaraki-ken) in order to correct the absolute troposphere errors. As a result, the mean bias error (MBE) and root mean square error (RMSE) and correlation compared with radiosonde measure were 0.67 mm, 6.40 mm, and 0.93, respectively. In order to correct the relative troposphere errors from the altitudinal difference between the two GPS receivers, we calculated the GPS-derived PWV by adding the data of GPS that was installed in Gangneung-Wonju University near the Gangwon Regional Meteorological Administration. In the end, the improved result showed that MBE, RMSE and correlation in comparison with radiosonde measures were 0.61 mm, 5.79 mm, and 0.93, respectively.

GPS-derived precipitable water vapour and its comparison with MODIS data for Almora, Central Himalaya, India

The present study is an attempt to analyse the precipitable water vapour (PWV) derived from Global Positioning System (GPS) and observed meteorological data over Almora, Central Himalayan Region. The PWV values derived using GPS study is compared with the corresponding moderate resolution imaging spectro-radiometer (MODIS) data. The statistical analysis reveals a positive correlation between both methods. Moderate resolution imaging spectroradiometer near-infrared (MODIS NIR) clear column water vapour product shows a higher correlation (R 2 = 90–93 %) with GPS-derived precipitable water vapour on annual scale as compared to the seasonal scale (R 2 = 62–87 %). MODIS is found to be overestimating in NIR clear column where the magnitude of bias and RMSE show systematic changes from season to season. Monsoon is an important phenomenon in the Indian weather context and holds significant importance in Central Himalayan ecosystem. The monthly and seasonal variation in precipitable water vapour is related with monsoon onset in the region. Diurnal variations in precipitable water vapour are studied with other meteorological data over Almora during dry and wet season. The precipitable water vapour had minimum value in the morning, increases in the afternoon to evening and again decreases to the midnight in both the dry and wet seasons. These results suggest that diurnal variation of water vapour is caused by the transport of water vapour by thermally induced local circulation.

Empirical model for mean temperature and assessment of precipitable water vapor derived from GPS

The estimation of Precipitable Water Vapor (PWV) derived from Global Positioning System (GPS) data at the IGS site WUHN is assessed by comparing with PWV obtained from radiosonde data (No.57494) in Wuhan. The applicability of Saastamoinen (SAAS) , Hopfield and Black models used for estimating Zenith Hydrostatic Delay (ZHD) and Zenith Wet Delay (ZWD) and different models is verified in the estimation of GPS-derived PWV for the applied area. The experimental results demonstrated that: 1) the precision of PWV estimated from Black model used for calculating ZHD (ZHD B ) is lower than that of SAAS (ZHD SAAS ) model and Hopfield model (ZHD H ) with the RMS of 4. 16 mm; 2) the RMS of PWV estimated from SAAS model used for calculating ZWD (SAAS) is 3.78 mm; 3) the well-known Bevis model gives similar accuracy compared with the site-specific models for T m in terms of surface temperature ( T s ) and surface pressure ( Ρ s ) , which can reach the accuracy inside 1 mm in the GPS-derived PWV estimates.

Detection of a katabatic wind event with GPS meteorology measurements at Scott Base Antarctica

A katabatic wind event which dramatically affects the polar climate has been detected using GPS meteorology measurements. GPS-derived precipitable water vapor (GPS PWV) variability and its relation to a katabatic event at Scott Base station, Antarctica was investigated. The investigations using the data gathered from 21 to 30 November of 2002. They showed that the water vapor profile exhibited an irregular pattern with a maximum PWV of 7.38 mm (~6 mm on average). This event was strongly influenced by relative humidity than by wind speed activity. The dominant wind flow during this period was from the North to Northeast (blowing from the Ross Sea) with a mean speed of 3.79 ms−1. The PWV increased when the temperature was between −15 and −11°C. During the katabatic event identified between 21:30 UT of 28 November and 18:40 UT on 29 November, the wind blew from the Southeast to South direction (from the Ross Ice Shelf) with a maximum speed of 10.92 ms−1. The PWV increased ~1.0 mm (23%) from the mean value accompanied by severe wind had pronounced effect on GPS observations.

Nocturnal infrared clear sky temperatures correlated with screen temperatures and GPS-derived PWV in southern Australia

Infrared (IR) clear sky temperatures ( Tsky), screen level temperature ( T) and Precipitable Water Vapour (PWV) were collected for a period of 2 years from a coastal region of southern Australia. IR sky temperatures were derived from simple infrared detectors, which have been developed by the authors for inexpensive monitoring of cloud cover, while data for PWV were obtained from Global Positioning Satellite (GPS) transmissions. Meteorological data were obtained from the Australian Bureau of Meteorology. Based on these data, statistical regression analyses between nocturnal Tsky and the T and PWV (one variable fit) were conducted. Direct proportional relationships between Tsky and T and between Tsky and PWV were found. The one variable fit was further optimized by applying the multiple regression fit between Tsky and both T and PWV. The resulting multilinear model was essentially unbiased, with a correlation coefficient ( R 2) of 0.97, mean bias error (MBE) of −2.7 × 10 −5 °C, and root mean square error (RMSE) of about 1.6 °C. The performance of the multilinear model was tested against a 1-year independent data set. In this case, the predictability of the model was superior, with MBE and RMSE being 0.13 °C and 2.23 °C respectively. After correcting the daytime observations for the biases caused by solar heating of the detection system, the performance of this model in calculating the daytime measurements was tested and showed MBE and RMSE values of 1.3 °C and 2.5 °C, respectively. Additionally, twenty schemes from the literature were tested and assessed for predicting the measured sky temperature. The performances of these models were all different. Whereas most of them underestimated the measured sky temperatures, three of these models showed a reasonable prediction ability. Finally, the multilinear model was compared with the simulated sky temperatures obtained using MODTRAN software. Perfect agreements were found between the two temperatures.

Assimilation of Nationwide and Global GPS PWV Data for a Heavy Rain Event on 28 July 2008 in Hokuriku and Kinki, Japan

On 28 July, 2008, a local heavy rainfall occurred over the Hokuriku and Kinki districts, in central Japan. A stationary front existed near the heavy rainfall areas, and the atmospheric instability was increased by the inflow of the upper-level cold air. The operational mesoscale model (MSM) of the Japan Meteorological Agency (JMA) could not predict the heavy rainfall. The comparison of distributions of precipitable water vapor (PWV) between the initial condition of the MSM and the Global Positioning System (GPS) revealed that the MSM initial condition underestimated water vapor in the heavy rainfall areas because of a positional lag of a low-level convergence zone. Data assimilation experiments of GPS-derived PWV were conducted with JMA’s mesoscale 4 dimensional variational assimilation system (Meso 4D-Var). The PWV derived from the nationwide ground GPS network (GPS Earth Observation NETwork: GEONET) improved both the northward position error of the low-level convergence zone and the forecast of the observed rainfall. Moreover, further improvements were obtained when the PWV derived from the GPS stations of the International GNSS Service in East Asia were added.

Prediction of daily heavy precipitation in a lowland area using GPS and surface weather data

Recent progress in GPS technology has enabled us to estimate the total amount of water vapor in an atmospheric column with infinite height as ’GPS-derived precipitable water vapor’ (GPS-PWV). In the present study, we analyzed the GPS and meteorological data obtained in Saga Plain, in northern Kyushu, Japan. An attempt was made to predict heavy precipitation (≥50 mm d−1) on a daily basis using the analyzed relations between surface temperature and GPS-PWV. Several features were revealed that were associated with the daily heavy precipitation at the study area. An index was made to predict heavy precipitation on a daily basis. Applying the index to observations, we obtained acceptable results of the prediction. It is suggested that GPS is useful for predicting heavy precipitation events.

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.

Relationship between GPS-derived Precipitable Water Vapor and Surface Vapor Pressure under Low-humidity Conditions

Relationship between GPS-derived Precipitable Water Vapor and Surface Vapor Pressure under Low-humidity Conditions

Examination of an Index for Predicting Heavy Precipitation using GPS-derived Precipitable Water Vapor

Examination of an Index for Predicting Heavy Precipitation using GPS-derived Precipitable Water Vapor

Estimating Daily Potential Evapotranspiration using the Relation between GPS-derived Precipitable Water Vapor and Surface Temperature

Estimating Daily Potential Evapotranspiration using the Relation between GPS-derived Precipitable Water Vapor and Surface Temperature

Estimation of Vapor Pressure near the Ground from GPS-derived Precipitable Water Vapor

Estimation of Vapor Pressure near the Ground from GPS-derived Precipitable Water Vapor

GPS measurements of ocean loading and its impact on zenith tropospheric delay estimates: a case study in Brittany, France

The results from a global positioning system (GPS) experiment carried out in Brittany, France, in October 1999, aimed at measuring crustal displacements caused by ocean loading and quantifying their effects on GPS-derived tropospheric delay estimates, are presented. The loading effect in the vertical and horizontal position time series is identified, however with significant disagreement in amplitude compared to ocean loading model predictions. It is shown that these amplitude misfits result from spatial tropospheric heterogeneities not accounted for in the data processing. The effect of ocean loading on GPS-derived zenith total delay (ZTD) estimates is investigated and a scaling factor of 4.4 between ZTD and station height for a 10° elevation cutoff angle is found (i.e. a 4.4-cm station height error would map into a 1-cm ZTD error). Consequently, unmodeled ocean loading effects map into significant errors in ZTD estimates and ocean loading modeling must be properly implemented when estimating ZTD parameters from GPS data for meteorological applications. Ocean loading effects must be known with an accuracy of better than 3 cm in order to meet the accuracy requirements of meteorological and climatological applications of GPS-derived precipitable water vapor.

Derivation of precipitable water from GPS data: an application to meteorology

It has been demonstrated in a number of previous publications that it is possible to derive very accurate and almost time continuous precipitable water vapour (PWV) values from the zenith tropospheric delays after processing the measurements of a Global Positioning System (GPS) network. Since February 1996 the Penc GPS station is a permanent site in Hungary, integrated into the network data processing of two GPS data centres. Our purpose was to investigate the quality of PWV values derived from the estimates provided by these two networks. In this paper comparisons and collocation statistics between the two types of GPS-derived PWV data and both meteorological measurements, and numerical prediction model analyses are presented.