Preconditions to ground based GPS water vapour tomography

Bender, Michael A.; Raabe, Armin

doi:10.5194/angeo-25-1727-2007

Cited by 61 publications

(62 citation statements)

References 19 publications

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“…Due to the nearly cone geometry of the GPS observations, the GPS signals cannot pass through all voxels (Bender and Raabe, 2007;Benevides et al, 2016). As a result, the tomographic system cannot be inverted due to too many zeros in the design matrix.…”

Section: Tomography Modelmentioning

confidence: 99%

“…These observation means have some drawbacks such as low temporal or spatial resolutions, high cost and weather dependence. In recent years, GPS tomography technique has been demonstrated as an effective means to acquire the three-dimensional (3-D) distribution of water vapor and can compensate for these disadvantages (Troller et al, 2006;Bender and Raabe, 2007;Bender et al, 2011;Champollion et al, 2009;Lutz et al, 2010;Perler et al, 2011;Rohm, 2013;Jiang et al, 2014;Benevides et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In order to reconstruct the 3-D distribution of the water vapor, the tomography area is discretized into voxels in both horizontal and vertical directions. Then, the SWV can be expressed by an integral of the water vapor density along the ray path from the receiver to the top boundary of the grid (Flores et al, 2000;Troller et al, 2006;Bender and Raabe, 2007;Bender et al, 2011;Perler et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Optimization of GPS water vapor tomography technique with radiosonde and COSMIC historical data

Xia

Cai

2016

Ann. Geophys.

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Abstract. The near-real-time high spatial resolution of atmospheric water vapor distribution is vital in numerical weather prediction. GPS tomography technique has been proved effectively for three-dimensional water vapor reconstruction. In this study, the tomography processing is optimized in a few aspects by the aid of radiosonde and COSMIC historical data. Firstly, regional tropospheric zenith hydrostatic delay (ZHD) models are improved and thus the zenith wet delay (ZWD) can be obtained at a higher accuracy. Secondly, the regional conversion factor of converting the ZWD to the precipitable water vapor (PWV) is refined. Next, we develop a new method for dividing the tomography grid with an uneven voxel height and a varied water vapor layer top. Finally, we propose a Gaussian exponential vertical interpolation method which can better reflect the vertical variation characteristic of water vapor. GPS datasets collected in Hong Kong in February 2014 are employed to evaluate the optimized tomographic method by contrast with the conventional method. The radiosonde-derived and COSMICderived water vapor densities are utilized as references to evaluate the tomographic results. Using radiosonde products as references, the test results obtained from our optimized method indicate that the water vapor density accuracy is improved by 15 and 12 % compared to those derived from the conventional method below the height of 3.75 km and above the height of 3.75 km, respectively. Using the COSMIC products as references, the results indicate that the water vapor density accuracy is improved by 15 and 19 % below 3.75 km and above 3.75 km, respectively.

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Section: Tomography Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of GPS water vapor tomography technique with radiosonde and COSMIC historical data

Xia

Cai

2016

Ann. Geophys.

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“…The analysis of the temperature-and humiditydependent delay of the GPS signal emitted by satellites and received by ground stations allows the retrieval of TCWV (Bender and Raabe, 2007) with an accuracy in the range of 1-2 mm (Gendt et al, 2004). The GPS-data was provided by GFZ Potdsam and was derived from 153 ground based GPS stations in Central Europe using the EPOS.P.V2 software (Gendt et al, 1999, see Fig.…”

Section: Validation Against Ground-based Gps Data (2003-2005)mentioning

confidence: 99%

1D-Var retrieval of daytime total columnar water vapour from MERIS measurements

et al. 2012

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Abstract.A new scheme for the retrieval of total columnar water vapour from measurements of MERIS (Medium Resolution Imaging Spectrometer) on ENVISAT (ENVIronmental SATellite) is presented. The algorithm is based on a fast forward model of the water vapour transmittance around 900nm, including a correction for atmospheric scattering and the influence of the temperature-and pressure-profile on the water vapour absorption lines. It provides the water vapour column amount for cloud-free scenes above land and ocean at a spatial resolution of 0.25 km × 0.3 km and 1 km × 1.2 km, depending on whether applied to the "full resolution" or the operational "reduced resolution" mode of MERIS. Uncertainties are provided on a pixel-by-pixel basis, taking into account all relevant sources of error. An extensive validation against various sources of ground-based reference data reveals a high accuracy of MERIS water vapour above land (root mean square deviations between 1.4 mm and 3.7 mm), apart from a wet bias of MERIS between 5 and 10 % that is found in all comparison studies. This wet bias might be caused by spectroscopic uncertainties, such as the description of the water vapour continuum. Above ocean the accuracy is reduced, due to the uncertainty introduced by the unknown atmospheric scattering. Consequently, an increased root mean square deviation of ≥5 mm was found by comparing MERIS total columnar water vapour above ocean against SSM/I and ENVISAT MWR data. An increased wet bias of 2-3 mm is found over ocean, potentially due to a not properly working atmospheric correction scheme.

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“…A small amount of water vapor variation may cause severe weather changes (Mohanakumar, 2008). Accurate information of water vapor spatiotemporal distributions is thus crucially important for weather forecasting services and meteorological research, such as precipitation and severe weather forecasting, and natural hazard mitigation (Bender and Raabe, 2007;Perler et al, 2011;Rocken et al, 1997). However, atmospheric water vapor remains one of the most poorly characterized parameters in meteorology due to its highly variable nature in space and time (Lee et al, 2013;Rocken et al, 1997).…”

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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Preconditions to ground based GPS water vapour tomography

Cited by 61 publications

References 19 publications

Optimization of GPS water vapor tomography technique with radiosonde and COSMIC historical data

Optimization of GPS water vapor tomography technique with radiosonde and COSMIC historical data

1D-Var retrieval of daytime total columnar water vapour from MERIS measurements

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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