Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model

Chen, Biyan; Liu, Zhizhao

doi:10.1007/s00190-014-0715-y

Cited by 96 publications

(117 citation statements)

References 33 publications

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“…The problem becomes worse when the GNSS network is flat [21]. To solve this problem, additional constraints or a priori information are usually used [9,16,17,21,28]. Three kinds of constraints are usually added:…”

Section: General Methods and Existing Problemsmentioning

confidence: 99%

“…As the water vapor varies rapidly in the vertical direction, some scientists tried to use external profiles as a priori information to constrain the distribution of water vapor [17,29,30] while some scientists still used the weighted average method to establish horizontal constraints [9,21]. The former method is more determined by a priori information than by the SWDs when the SWDs are not sufficient to retrieve the water vapor distribution.…”

Section: General Methods and Existing Problemsmentioning

confidence: 99%

“…Flores et al proposed using horizontal smoothing, vertical smoothing and boundary conditions to solve the ill-posed problem and use the singular value decomposition to solve the inversion problem [9]. This method has been developed by many other scientists for troposphere tomography [12][13][14][15][16][17]. Other approaches to retrieve the 3D WV include algebraic reconstruction techniques [18,19] and estimating WV directly from raw GPS phase observations [20].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations

et al. 2017

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Using the Global Navigation Satellite System (GNSS) to sense three-dimensional water vapor (WV) has been intensively investigated. However, this technique still heavily relies on the a priori information. In this study, we propose an improved tomography approach based on adaptive Laplacian smoothing (ALS) and ground meteorological observations. By using the proposed approach, the troposphere tomography is less dependent on a priori information and the ALS constraints match better with the actual situation than the constant constraints. Tomography experiments in Hong Kong during a heavy rainy period and a rainless period show that the ALS method gets superior results compared with the constant Laplacian smoothing (CLS) method. By validation with radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) data, we found that the introduction of ground meteorological observations into tomography can solve the perennial problem of resolving the wet refractivity in the lower troposphere and thus significantly improve the tomography results. However, bad data quality and incompatibility of the ground meteorological observations may introduce errors into tomography results.

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Section: General Methods and Existing Problemsmentioning

confidence: 99%

Section: General Methods and Existing Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations

et al. 2017

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“…This software uses doubledifference to remove the satellite and receiver clock biases, and outputs many products including zenith tropospheric delay (ZTD), gradients and the double-differenced residuals (Dach et al, 2007). The slant wet delays can thus be retrieved according to Chen and Liu (2014): Figure 2. Schematic diagram of rays used in the tomography.…”

Section: Gps Observationsmentioning

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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“…In the actual calculation of the SWV, the humidity conversion factor is usually determined using empirical models or even simply regarded as a constant (Shi and Gao, 2009;Bosy et al, 2012;Shangguan et al, 2013;Adavi and Mashhadi-Hossainali, 2014;Chen and Liu, 2014). However, the conversion factor is varied in different areas and seasons (Jiang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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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Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model

Cited by 96 publications

References 33 publications

An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations

An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations

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

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

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