A formulation of local GPS tropospheric tomography for determining 4-D distribution of refractivity in the troposphere is presented together with a preliminary analysis of local dense GPS campaign data. Dividing the modeling space up to a height of 10 km above GPS receivers into cells, the refractivity in each cell is estimated in a successive time window by a tomographic reconstruction method in a quite similar manner like the seismic velocity in each cell in Earth’s interior is estimated in seismic tomography. The basic data for tomography are GPS slant delays for respective pairs of station and satellite, which are the sum of postfit phase residual, hydrostatic and wet slant delay. On the other hand, the slant delay from a station to a satellite is expressed by the summation of the product of path length and refractivity in each cell along the ray path. In a given time window, we have numerous observed slant delays corresponding to different ray trajectories, and the refractivity in each cell can be estimated by discrete inversion and least squares methods. The observational equations are usually singular so that we use a damped least squares method popular in seismic tomography. An example of real data analysis is given for the 1995 Shigaraki GPS campaign data, which reveals the spatial and temporal change of refractivity corresponding to the passage of ‘cold front’.

Local GPS tropospheric tomography

This study was designed to compare the pattern of land surface temperature (LST) over four metro cities of India (Mumbai, Chennai, Delhi, and Kolkata) selected on a longitudinal basis in relation to the built-up and vegetation indices. Two different methods were employed for the retrieval of LST, i.e., mono-window algorithm (MWA) and split-window algorithm (SWA) on the Landsat 8 (OLI/TIRS) datasets, to analyze the spatial pattern of LST over selected cities in relation to normalized differential built-up index (NDBI) and normalized differential vegetation index (NDVI). The result shows that the LST was high over the densely built areas while low over the densely vegetated areas. The highest LST, NDBI, and NDVI were found in Mumbai, while Kolkata records the lowest LST and NDVI. Furthermore, the spatial analysis of LST shows that the LST was high in central parts of all cities except in the case of Delhi where some peripheral areas also record high LST. The comparison from in situ LST (field observations) reveals that the SWA has higher accuracy in the retrieval of LST in maritime areas like Mumbai and Chennai because it reduces the atmospheric effects, while the MWA has higher accuracy for inland areas like Delhi. The spatial relationships of LST with NDVI and NDBI show that vegetation cover has more impact on LST in Delhi while low in Chennai and Mumbai, and the built-up surfaces have a higher impact on LST in Chennai and Mumbai than Kolkata and Delhi.

Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India

After 30 years since the beginning of the Global Positioning System (GPS), or, more generally, Global Navigation Satellite System (GNSS) meteorology, this technique has proven to be a reliable method for retrieving atmospheric water vapor; it is low-cost, weather independent, with high temporal resolution and is highly accurate and precise. GNSS ground-based networks are becoming denser, and the first stations installed have now quite long time-series that allow the study of the temporal features of water vapor and its relevant role inside the climate system. In this review, the different GNSS methodologies to retrieve atmospheric water vapor content re-examined, such as tomography, conversion of GNSS tropospheric delay to water vapor estimates, analyses of errors, and combinations of GNSS with other sources to enhance water vapor information. Moreover, the use of these data in different kinds of studies is discussed. For instance, the GNSS technique is commonly used as a reference tool for validating other water vapor products (e.g., radiosounding, radiometers onboard satellite platforms or ground-based instruments). Additionally, GNSS retrievals are largely used in order to determine the high spatio-temporal variability and long-term trends of atmospheric water vapor or in models with the goal of determining its notable influence on the climate system (e.g., assimilation in numerical prediction, as input to radiative transfer models, study of circulation patterns, etc.).

Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics

Global Navigation Satellite System (GNSS) tomography has developed into an efficient tool for sensing the high spatiotemporal variability of atmospheric water vapor. The integration of GNSS top signals and side rays for tropospheric tomography systems using a novel height factor model (HFM) is proposed and discussed in this paper. Within the HFM, the sectional slant wet delay (SWD) of inside signals (the part of the side signal inside the tomography area), which is considered a key factor for modeling side rays, is separated into isotropic and anisotropic components. Correspondingly, two height factors are defined to calculate the isotropic and anisotropic part of tropospheric delays in the HFM. In addition, the dynamic tomography top boundary is first analyzed and determined based on 30-year radiosonde data to reasonably divide signals into top and side rays. Four special experimental schemes based on different tomography regions of Hong Kong are performed to assess the proposed HFM method, the results of which show increases of 33.42% in the mean utilization of rays, as well as decreases of 0.46 g/m3 in the average root mean square error (RMSE), compared to the traditional approach, revealing the improvement of tomography solutions when the side signals are included in the modeling. Furthermore, compared with the existing correction model for modeling side rays, the water vapor profiles retrieved from the proposed improved model are closer to the radiosonde data, which highlights the advantages of the proposed HFM for optimizing the GNSS tomography model.

A Tropospheric Tomography Method with a Novel Height Factor Model Including Two Parts: Isotropic and Anisotropic Height Factors

The Global Navigation Satellite System (GNSS) tomographic technique can be used for remote sensing of the three-dimensional water vapor (WV) distribution in the troposphere, which has attracted considerable interest. However, a significant problem in this technique is the excessive reliance on constraints (particularly in large GNSS networks). In this paper, we propose an improved tomographic method based on optimized voxel, which only considers the voxels passed by GNSS rays. The proposed method can completely prevent the tomographic algorithm interference of constraints that originated from empirical functions. Experiments in Nanjing in the periods of day-of-year (DOY) 182–184, 2019, and 244–246, 2019, show that the mean absolute error (MAE) and root mean square error (RMSE) of the WV density profile obtained using the proposed method are 0.9 and 1.3 g/m3, while those obtained using the conventional method are 1.3 and 1.8 g/m3, respectively, with respect to the radiosonde (RS) method. The numerical results show that the proposed method is reliable and has a superior accuracy to that of the conventional method.

A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network

As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman–Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014–2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May–October) but not during the colder months (November–March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W·m−2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.

Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

. The determination of the distribution of water vapor in the atmosphere plays
an important role in the atmospheric monitoring. Global Navigation Satellite
Systems (GNSS) tomography can be used to construct 3-D distribution of water
vapor over the field covered by a GNSS network with high temporal and spatial
resolutions. In current tomographic approaches, a pre-set fixed rectangular
field that roughly covers the area of the distribution of the GNSS signals on
the top plane of the tomographic field is commonly used for all tomographic
epochs. Due to too many unknown parameters needing to be estimated, the
accuracy of the tomographic solution degrades. Another issue of these
approaches is their unsuitability for GNSS networks with a low number of stations, as
the shape of the field covered by the GNSS signals is, in fact, roughly that of an
upside-down cone rather than the rectangular cube as the pre-set. In this
study, a new approach for determination of tomographic fields fitting the
real distribution of GNSS signals on different tomographic planes at
different tomographic epochs and also for discretization of the tomographic
fields based on the perimeter of the tomographic boundary on the plane and
meshing techniques is proposed. The new approach was tested using three
stations from the Hong Kong GNSS network and validated by comparing the
tomographic results against radiosonde data from King's Park Meteorological
Station (HKKP) during the one month period of May 2015. Results indicated
that the new approach is feasible for a three-station GNSS network
tomography. This is significant due to the fact that the conventional
approaches cannot even solve a network tomography from a few stations.

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A new approach for GNSS tomography from a few GNSS stations

Global Navigation Satellite Systems (GNSS) tomography plays an important role in the monitoring and tracking of the tropospheric water vapor. In this study, a new approach for improving the node-based GNSS tomography is proposed, which makes a trade-off between the real observed region and the complexity of the discretization of the tomographic region. To obtain dynamically the approximate observed region, the convex hull algorithm and minimum bounding box algorithm are used at each tomographic epoch. This new approach can dynamically define the tomographic model for all types of study areas based on the GNSS data. The performance of the new approach is tested by comparing it against the common node-based GNSS tomographic approach. Test data in May 2015 are obtained from the Hong Kong GNSS network to build the tomographic models and the radiosonde data as a reference are used for validating the quality of the new approach. The experimental results show that the root-mean-square errors of the new approach, in most cases, have a 38 percent improvement and the values of standard deviation reduce to over 43 percent compared with the common approach. The results indicate that the new approach is applicable to the node-based GNSS tomography.

Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm

Global Navigation Satellite System (GNSS) tomography is a popular method for measuring and modelling water vapor in the troposphere. Presently, most studies use a cuboid-shaped tomographic region in their modelling, which represents the modelling region for all measurement epochs. This region is defined by the distribution of the GNSS signals skywards from a network of ground based GNSS stations for all epochs of measurements. However, in reality at each epoch the shape of the GNSS tomographic region is more likely to be an inverted cone. Unfortunately, this fixed conic tomographic region does not properly reflect the fact that the GNSS signal changes quickly over time. Therefore a dynamic or adaptive tomographic region is better suited. In this study, a new approach that adjusts the GNSS tomographic model to adapt the size of the GNSS network is proposed, which referred to as The High Flexibility GNSS Tomography (HFGT). Test data from different numbers of the GNSS stations are used and the results from HFGT are compared against that of radiosonde data (RS) to assess the accuracy of the HFGT approach. The results showed that the new approach is feasible for different numbers of the GNSS stations when a sufficient and uniformed distribution of GNSS signals is used. This is a novel approach for GNSS tomography.

Nan Ding

Papers

Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

A Tropospheric Tomography Method with a Novel Height Factor Model Including Two Parts: Isotropic and Anisotropic Height Factors

A new approach for GNSS tomography from a few GNSS stations

Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm

A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network