A new tropospheric tomography model combining the pixel-based and function-based models

Abstract. As a new detection method of three-dimensional water vapor, the ground-based water vapor tomography technique using Global Navigation Satellite Systems (GNSS) observations can obtain the high spatial and temporal distribution information of tropospheric water vapor. Since the troposphere tomography was proposed, most previous studies belong to the pixel-based method, dividing the interest area into three-dimensional voxels of which the water vapor density of each voxel center is taken as the average water vapor density. However, the abovementioned method can only find the water vapor density value of the center of each voxel, which is unable to express the continuous change of water vapor in space and destroys the spatial continuity of water vapor variation. Moreover, when using the pixel-based method, too many voxels are needed to express the water vapor density, which leads to the problem of too many coefficients to be estimated. After analyzing the limitations of the traditional pixel-based troposphere tomography technique, this paper proposes a new GNSS tropospheric water vapor tomography model combining the pixel-based and function-based models for the first time. The tomographic experiences were validated using the data from 12 stations from the Hong Kong Satellite Positioning Reference Station Network (SatRef) collected between 25 March and 25 April 2014. The comparison between tomographic results and the European Centre for Medium-Range Weather Forecasts (ECMWF) data is mainly used to analyze the accuracy of the new model proposed in this paper under different conditions, for showing that this new model is superior to the traditional pixel-based model in terms of root-mean-square error (RMSE) and bias. The new model has more advantages than the traditional pixel-based model on the RMSE, especially when obtaining the water vapor in voxels without the penetration of GNSS rays, which is improved by 5.88 %. This model also solves the problem with more ease and convenience in expression.

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An improved pixel-based water vapor tomography model

Annales Geophysicae ◽

10.5194/angeo-37-89-2019 ◽

2019 ◽

Vol 37 (1) ◽

pp. 89-100

Author(s):

Yibin Yao ◽

Linyang Xin ◽

Qingzhi Zhao

Keyword(s):

Water Vapor ◽

Three Dimensional ◽

Satellite System ◽

Reference Station ◽

Weather Forecasts ◽

Satellite Positioning ◽

Optimal Polynomial ◽

Improved Model ◽

Global Navigation Satellite ◽

Gnss Data

Abstract. As an innovative use of Global Navigation Satellite System (GNSS), the GNSS water vapor tomography technique shows great potential in monitoring three-dimensional water vapor variation. Most of the previous studies employ the pixel-based method, i.e., dividing the troposphere space into finite voxels and considering water vapor in each voxel as constant. However, this method cannot reflect the variations in voxels and breaks the continuity of the troposphere. Moreover, in the pixel-based method, each voxel needs a parameter to represent the water vapor density, which means that huge numbers of parameters are needed to represent the water vapor field when the interested area is large and/or the expected resolution is high. In order to overcome the abovementioned problems, in this study, we propose an improved pixel-based water vapor tomography model, which uses layered optimal polynomial functions obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) by adaptive training for water vapor retrieval. Tomography experiments were carried out using the GNSS data collected from the Hong Kong Satellite Positioning Reference Station Network (SatRef) from 25 March to 25 April 2014 under different scenarios. The tomographic results are compared to the ECMWF data and validated by the radiosonde. Results show that the new model outperforms the traditional one by reducing the root-mean-square error (RMSE), and this improvement is more pronounced, at 5.88 % in voxels without the penetration of GNSS rays. The improved model also has advantages in more convenient expression.

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A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays

Applied Sciences ◽

10.3390/app9071446 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1446 ◽

Cited By ~ 3

Author(s):

Fei Yang ◽

Jiming Guo ◽

Junbo Shi ◽

Yinzhi Zhao ◽

Lv Zhou ◽

...

Keyword(s):

Water Vapor ◽

Density Profile ◽

Temporal Distribution ◽

Precipitable Water ◽

Absolute Error ◽

New Method ◽

Reference Station ◽

Full Potential ◽

Vapor Density ◽

Unknown Parameters

The spatio-temporal distribution of atmospheric water vapor information can be obtained by global positioning system (GPS) water vapor tomography. GPS signal rays pass through the tomographic area from different boundaries because the scope of the research region (latitude, longitude, and altitude) is designated in the process of tomographic modeling, the influence of the geographic distribution of receivers, and the geometric location of satellite constellations. Traditionally, only signal rays penetrating the entire tomographic area are considered in the computation of water vapor information, whereas those passing through the sides are neglected. Therefore, the accuracy of the tomographic result, especially at the bottom of the area, does not reach its full potential. To solve this problem, this paper proposes a new method that simultaneously considers the discretized tomographic voxels and the troposphere outside the research area as unknown parameters. This method can effectively improve the utilization of existing GPS observations and increase the number of voxels crossed by satellite signals, especially by increasing the proportion of voxels penetrated. A tomographic experiment is implemented using GPS data from the Hong Kong Satellite Positioning Reference Station Network. Compared to the traditional method, the proposed method increases the number of voxels crossed by signal rays and the utilization of the observed data by 15.14% and 19.68% on average, respectively. Numerical results, including comparisons of slant water vapor (SWV), precipitable water vapor (PWV), and water vapor density profile, show that the proposed method is better than traditional methods. In comparison to the water vapor density profile, the root-mean-square error (RMS), mean absolute error (MAE), standard deviation (SD), and bias of the proposed method are 1.39, 1.07, 1.30, and −0.21 gm−3, respectively. For the SWV and PWV comparison, the RMS/MAE of the proposed method are 10.46/8.17 mm and 4.00/3.39 mm, respectively.

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A New Method for Determining an Optimal Diurnal Threshold of GNSS Precipitable Water Vapor for Precipitation Forecasting

Remote Sensing ◽

10.3390/rs13071390 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1390

Author(s):

Haobo Li ◽

Xiaoming Wang ◽

Suqin Wu ◽

Kefei Zhang ◽

Erjiang Fu ◽

...

Keyword(s):

Water Vapor ◽

Weather Prediction ◽

Precipitable Water ◽

Heavy Precipitation ◽

Precipitable Water Vapor ◽

Global Navigation Satellite Systems ◽

New Method ◽

New Model ◽

Precipitation Records ◽

Optimal Set

Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the “threshold-based” models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010–2017 at the co-located HKSC–KP (King’s Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model.

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New parameterized model for GPS water vapor tomography

Annales Geophysicae ◽

10.5194/angeo-35-311-2017 ◽

2017 ◽

Vol 35 (2) ◽

pp. 311-323 ◽

Cited By ~ 7

Author(s):

Nan Ding ◽

Shubi Zhang ◽

Qiuzhao Zhang

Keyword(s):

Water Vapor ◽

Water Cycle ◽

Weather Conditions ◽

Reference Station ◽

Atmospheric Conditions ◽

Vapor Density ◽

Spatiotemporal Resolution ◽

Parameterized Model ◽

Time Changes ◽

Rainy Days

Abstract. Water vapor is the basic parameter used to describe atmospheric conditions. It is rarely contained in the atmosphere during the water cycle, but it is the most active element in rapid space–time changes. Measuring and monitoring the distribution and quantity of water vapor is a necessary task. GPS tomography is a powerful means of providing high spatiotemporal resolution of water vapor density. In this paper, a spatial structure model of a humidity field is constructed using voxel nodes, and new parameterizations for acquiring data about water vapor in the troposphere via GPS are proposed based on inverse distance weighted (IDW) interpolation. Unlike the density of water vapor that is constant within a voxel, the density at a certain point is determined by IDW interpolation. This algorithm avoids the use of horizontal constraints to smooth voxels that are not crossed by satellite rays. A prime number decomposition (PND) access order scheme is introduced to minimize correlation between slant wet delay (SWD) observations. Four experimental schemes for GPS tomography are carried out in dry weather from 2 to 8 August 2015 and rainy days from 9 to 15 August 2015. Using 14 days of data from the Hong Kong Satellite Positioning Reference Station Network (SatRef), the results indicate that water vapor density derived from 4-node methods is more robust than that derived from that of 8 nodes or 12 nodes, or that derived from constant refractivity schemes and the new method has better performance under stable weather conditions than unstable weather (e.g., rainy days). The results also indicate that an excessive number of interpolations in each layer reduce accuracy. However, the accuracy of the tomography results is gradually reduced with increases in altitude below 7000 m. Moreover, in the case of altitudes between 7000 m and the upper boundary layer, the accuracy can be improved by a boundary constraint.

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A Case Study of the 3D Water Vapor Tomography Model Based on a Fast Voxel Traversal Algorithm for Ray Tracing

Remote Sensing ◽

10.3390/rs13122422 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2422

Author(s):

Heng Hu ◽

Min Liu ◽

Jiqin Zhong ◽

Xin Deng ◽

Yunchang Cao ◽

...

Keyword(s):

Water Vapor ◽

Ray Tracing ◽

Three Dimensional ◽

Satellite System ◽

Horizontal Distribution ◽

Observation Data ◽

Vapor Density ◽

Average Deviation ◽

Global Navigation Satellite ◽

Traversal Algorithm

A fast voxel traversal algorithm for ray tracing was applied to build a 4 × 4 × 20 tomography model using the observation data of 11 ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in Hebei Province, China. The precipitation water vapor (PWV) observed at 05 a.m. (Universal Time Coordinated: UTC) on 10 December 2019, was used to reconstruct three-dimensional (3D) water vapor density fields over the test area. The tomographic results (GNSS_T) show that the water vapor density above this area is mainly below 25 g/m3 and is concentrated between the first to the fourth layers. The vertical distribution conforms to the exponential characteristics, while the horizontal distribution shows a decreasing trend from southwest to northeast. In addition, the results of the 0.25° grid dataset generated by the Global Forecast System (GFS) of the National Center for Environmental Forecasting (NCEP) (GFS_L) were interpolated to the height of the tomographic grid, which is in good agreement with the tomographic results. GFS_L is larger than GNSS_T on the first floor at the surface, with an average deviation of 0.19 g/m3. In contrast, GFS_L from the second floor to the top of the model is smaller than GNSS_T, with the average deviations distributed between −0.08 and −0.15 g/m3.

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Retrieval of Atmospheric Water Vapor Density With Fine Spatial Resolution Using Three-Dimensional Tomographic Inversion of Microwave Brightness Temperatures Measured by a Network of Scanning Compact Radiometers

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2009.2031107 ◽

2009 ◽

Vol 47 (11) ◽

pp. 3708-3721 ◽

Cited By ~ 22

Author(s):

S. Padmanabhan ◽

S.C. Reising ◽

J. Vivekanandan ◽

F. Iturbide-Sanchez

Keyword(s):

Water Vapor ◽

Spatial Resolution ◽

Three Dimensional ◽

Atmospheric Water Vapor ◽

Vapor Density ◽

Atmospheric Water ◽

Tomographic Inversion ◽

Brightness Temperatures ◽

Fine Spatial Resolution

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

Annales Geophysicae ◽

10.5194/angeo-34-789-2016 ◽

2016 ◽

Vol 34 (9) ◽

pp. 789-799 ◽

Cited By ~ 17

Author(s):

Shirong Ye ◽

Pengfei Xia ◽

Changsheng Cai

Keyword(s):

Water Vapor ◽

Conventional Method ◽

Historical Data ◽

Interpolation Method ◽

Weather Prediction ◽

Three Dimensional ◽

Precipitable Water ◽

Vapor Density ◽

Tomographic Method ◽

Zenith Hydrostatic Delay

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 COSMIC-derived 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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LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography

Remote Sensing ◽

10.3390/rs13163056 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3056

Author(s):

Si Xiong ◽

Fujian Ma ◽

Xiaodong Ren ◽

Jun Chen ◽

Xiaohong Zhang

Keyword(s):

Water Vapor ◽

Rapid Development ◽

Three Dimensional ◽

Horizontal Resolution ◽

Observation Time ◽

Low Earth Orbit ◽

Global Navigation Satellite Systems ◽

Satellite Systems ◽

Dimensional Distribution ◽

Global Navigation Satellite

Global navigation satellite systems (GNSS) water vapor tomography is an important technique to obtain the three-dimensional distribution of atmospheric water vapor. The rapid development of low Earth orbit (LEO) constellations has led to a richer set of observations, which brings new expectations for water vapor tomography. This paper analyzes the influence of LEO constellation-augmented multi-GNSS（LCAMG）on the tomography, in terms of ray distribution, tomography accuracy, and horizontal resolution, by simulating LEO constellation data. The results show that after adding 288 LEO satellites to GNSS, the 30-min ray distribution effect of GNSS can be achieved in 10 min, which can effectively shorten the observation time by 66.7%. In the 10-min observation time, the non-repetitive effective observation value of LCAMG is 2.38 times that of GNSS, and the accuracy is 1.27% higher than that of GNSS. Compared with GNSS and the global positioning system (GPS), at a horizontal resolution of 13 × 14, the proportion of empty voxels in LCAMG reduces by 5.22% and 22.53%, respectively.

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A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography

Remote Sensing ◽

10.3390/rs12182999 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2999

Author(s):

Yibin Yao ◽

Chen Liu ◽

Chaoqian Xu ◽

Yu Tan ◽

Mingshan Fang

Keyword(s):

Water Vapor ◽

Conventional Method ◽

Three Dimensional ◽

Coefficient Matrix ◽

Satellite System ◽

Reference Station ◽

Dimensional Structure ◽

Rank Score ◽

Characteristic Signal ◽

Gnss Observations

Global navigation satellite system (GNSS) tomography can effectively sense the three-dimensional structure of tropospheric water vapor (WV) using the GNSS observations. Numerous studies have utilized a tomographic window to include more epochs of observations, which significantly increases the number of valid signals. However, considering the tomography grid limits, a massive number of valid signals inevitably exhibits linear dependence. This dependence makes it impossible to improve the rank score of the tomography coefficient matrix by blindly introducing a large number of valid rays. Furthermore, excessive valid signals may lead to a high condition number in the coefficient matrix (ill-condition problem), which causes unstable results using the GNSS-WV tomography. Considering these problems, we proposed an improved tomographic approach, which applies a refined tomographic window. It differs from the general tomographic window in that the window is refined to traverse the valid signals available 15 min before and after the target epoch while retaining only the linearly independent parts (characteristic signal). Compared to the conventional method, the proposed method can filter the characteristic signal, which increases the rank score of the coefficient matrix and improves the stability of the tomography model. In this paper, we used GNSS observations from the Hong Kong Satellite Positioning Reference Station Network (SatRef) to validate the performance of the proposed method over the day-of-year (DOY) periods of 130–132, 2019 and 146–148, 2019. The numerical results showed that, by using a refined tomographic window, the proposed method obtained superior WV products in comparison with that of the conventional method.

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Conformation of Ribosomes from Escherichia Coli

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051062 ◽

1974 ◽

Vol 32 ◽

pp. 210-211

Author(s):

M. Boublik ◽

W. Hellmann ◽

F. Jenkins

Keyword(s):

Binding Sites ◽

Ribosomal Proteins ◽

Fluorescent Dyes ◽

Protein Biosynthesis ◽

Three Dimensional ◽

Spatial Arrangement ◽

Dimensional Structure ◽

Complete Understanding ◽

And Function

The present knowledge of the three-dimensional structure of ribosomes is far too limited to enable a complete understanding of the various roles which ribosomes play in protein biosynthesis. The spatial arrangement of proteins and ribonuclec acids in ribosomes can be analysed in many ways. Determination of binding sites for individual proteins on ribonuclec acid and locations of the mutual positions of proteins on the ribosome using labeling with fluorescent dyes, cross-linking reagents, neutron-diffraction or antibodies against ribosomal proteins seem to be most successful approaches. Structure and function of ribosomes can be correlated be depleting the complete ribosomes of some proteins to the functionally inactive core and by subsequent partial reconstitution in order to regain active ribosomal particles.

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