Sensing atmospheric water vapor with the global positioning system

Rocken, Christian; Ware, Randolph; Hove, Teresa Van; Solheim, Fredrick; Alber, Chris; Johnson, James M.; Bevis, M. G.; Businger, Steven

doi:10.1029/93gl02935

Cited by 313 publications

(198 citation statements)

References 10 publications

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“…This estimation agrees with previous studies where uncertainties are obtained by comparison with water vapour radiometers and radiosondes (Kuo et al, 1993;Rocken et al, 1993Rocken et al, , 1995Businger et al, 1996;Duan et al, 1996;Tregoning et al, 1998).…”

Section: Uncertainty Of the Gps-based Iwvsupporting

confidence: 92%

A multi-site intercomparison of integrated water vapour observations for climate change analysis

et al. 2014

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Abstract. Water vapour plays a dominant role in the climate change debate. However, observing water vapour over a climatological time period in a consistent and homogeneous manner is challenging. On one hand, networks of groundbased instruments able to retrieve homogeneous integrated water vapour (IWV) data sets are being set up. Typical examples are Global Navigation Satellite System (GNSS) observation networks such as the International GNSS Service (IGS), with continuous GPS (Global Positioning System) observations spanning over the last 15+ years, and the AErosol RObotic NETwork (AERONET), providing long-term observations performed with standardized and well-calibrated sun photometers. On the other hand, satellite-based measurements of IWV already have a time span of over 10 years (e.g. AIRS) or are being merged to create long-term time series (e.g. GOME, SCIAMACHY, and GOME-2).This study performs an intercomparison of IWV measurements from satellite devices (in the visible, GOME/SCIAMACHY/GOME-2, and in the thermal infrared, AIRS), in situ measurements (radiosondes) and ground-based instruments (GPS, sun photometer), to assess their use in water vapour trends analysis. To this end, we selected 28 sites world-wide for which GPS observations can directly be compared with coincident satellite IWV observations, together with sun photometer and/or radiosonde measurements. The mean biases of the different techniques compared to the GPS estimates vary only between −0.3 to 0.5 mm of IWV. Nevertheless these small biases are accompanied by large standard deviations (SD), especially for the satellite instruments. In particular, we analysed the impact of clouds on the IWV agreement. The influence of specific issues for each instrument on the intercomparison is also investigated (e.g. the distance between the satellite ground pixel centre and the co-located ground-based station, the satellite scan angle, daytime/nighttime differences). Furthermore, we checked if the properties of the IWV scatter plots between these different instruments are dependent on the geography and/or altitude of the station. For all considered instruments, the only dependency clearly detected is with latitude: the SD of the IWV observations with respect to the GPS IWV retrievals decreases with increasing latitude and decreasing mean IWV.

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Section: Uncertainty Of the Gps-based Iwvsupporting

confidence: 92%

A multi-site intercomparison of integrated water vapour observations for climate change analysis

et al. 2014

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“…Rocken et al, 1993Rocken et al, , 1995Emardson et al, 1998 GPS network. The data were obtained from the Onsala Space Observatory, Onsala, Sweden, and the Finnish Geodetic Institute.…”

Section: Validation Of the Hrm With Groundbased Measurementsmentioning

confidence: 99%

Validation of a regional weather forecast model with GPS data

Johnsen

Rockel

2001

Physics and Chemistry of the Earth, Part B: Hydrology, Oceans a

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“…Thereafter numerous field campaigns demonstrated the GPS/GNSS ability to accurately measure atmospheric water vapor, and the derived precipitable water vapor (PWV) data can reach an accuracy of 1-2 mm or even better (Duan et al, 1996;Elgered et al, 1997;Lee et al, 2013;Liu and Li, 2013;Rocken et al, 1993;Tregoning et al, 1998). GNSS-inferred PWV data have enriched meteorological research by providing detailed information of horizontal distribution of atmospheric water vapor.…”

Section: Introductionmentioning

confidence: 99%

Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013

Chen

Liu

2016

Atmos. Meas. Tech.

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Abstract. Acquiring accurate atmospheric water vapor spatial information remains one of the most challenging tasks in meteorology. The tomographic technique is a powerful tool for modeling atmospheric water vapor and monitoring the water vapor spatial and temporal distribution/variation information. This paper presents a study on the monitoring of water vapor variations using tomographic techniques based on multi-source water vapor data, including GPS (Global Positioning System), radiosonde, WVR (water vapor radiometer), NWP (numerical weather prediction), AERONET (AErosol RObotic NETwork) sun photometer and synoptic station measurements. An extensive investigation has been carried out using multi-source data collected from May to October 2013 in Hong Kong. With the use of radiosonde observed profiles, five different vertical a priori information schemes were designed and examined. Analysis results revealed that the best vertical constraint is to employ the average radiosonde profiles over the 3 days prior to the tomographic time and that the assimilation of multi-source data can increase the tomography modeling accuracy. Based on the best vertical a priori information scheme, comparisons of slant wet delay (SWD) measurements between GPS data and multi-observational tomography showed that the root mean square error (RMSE) of their differences is 10.85 mm. Multi-observational tomography achieved an accuracy of 7.13 mm km −1 when compared with radiosonde wet refractivity observations. The vertical layer tomographic modeling accuracy was also assessed using radiosonde water vapor profiles. An accuracy of 11.44 mm km −1 at the lowest layer (0-0.4 km) and an RMSE of 3.30 mm km −1 at the uppermost layer (7.5-8.5 km) were yielded. At last, a test of the tomographic modeling in a torrential storm occurring on 21-22 May 2013 in Hong Kong demonstrated that the tomographic modeling is very robust, even during severe precipitation conditions.

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Sensing atmospheric water vapor with the global positioning system

Cited by 313 publications

References 10 publications

A multi-site intercomparison of integrated water vapour observations for climate change analysis

A multi-site intercomparison of integrated water vapour observations for climate change analysis

Validation of a regional weather forecast model with GPS data

Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013

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