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Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 24527

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Special Issue Editors

Dr. Xiaoming Wang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: precise positioning; atmospheric remote sensing; GNSS meteorology; climate monitoring
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kefei Zhang

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Guest Editor

School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Interests: high-accuracy positioning; atmospheric remote sensing; space science and technology; space resources exploration and utilization; people mobility and object tracking
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Suelynn Choy

E-Mail Website
Guest Editor

School of Science, RMIT University, Melbourne, VIC 3001, Australia
Interests: precise satellite positioning and navigation; geodesy; disaster management; atmospheric remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Karina Wilgan

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Guest Editor

GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Interests: GNSS meteorology; atmosphere; geodesy and surveying; climatology; data assimilation; numerical weather prediction

Dr. Haobo Li

E-Mail Website
Guest Editor

School of Science, RMIT University, Melbourne, VIC 3001, Australia
Interests: GNSS meteorology; GNSS atmospheric monitoring; data assimilation; numerical weather prediction; climate analysis and climate risks assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, severe weather events and climate change have seriously endangered the safety of human lives and assets. Effective monitoring and accurate forecasting of these phenomena are of great significance for disaster prevention and mitigation. With the rapid development of the cutting-edge multi-GNSS systems, GNSS remote sensing techniques offer an unprecedented opportunity for atmospheric modeling and provides high accuracy mega-geodetic data for meteorological and climatological studies. Precipitable water vapor, zenith total delay, slant total delay, slant water vapor, gradient, bending angle, refractivity and other atmospheric products obtained from space/ground-based GNSS remote sensing techniques have heralded a new era of atmospheric sounding, space weather monitoring, GNSS meteorology and climatology. To take advantage of the advanced GNSS remote sensing technique, this Special Issue mainly focuses on papers that address topics including but not limited to:

Advanced multi-GNSS atmospheric sounding and data processing;
Atmospheric augmentation modeling for PPP;
Data assimilation into operational earth system models;
Space weather modeling and monitoring;
Data mining of atmospheric products;
Machine learning-based approaches for data retrieval, weather forecasting and climate monitoring.
Furthermore, miscellaneous interdisciplinary researches and new applications in the atmosphere, meteorology and climatology fields are also welcomed.

Dr. Xiaoming Wang
Prof. Dr. Kefei Zhang
Prof. Dr. Suelynn Choy
Dr. Karina Wilgan
Dr. Haobo Li
Guest Editors

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Keywords

multi-GNSS data processing
radio occultation
atmospheric modeling for positioning
severe weather forecasting and climate monitoring
numerical weather prediction model
tropospheric tomography
miscellaneous applications

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Published Papers (11 papers)

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19 pages, 1899 KiB

Open AccessArticle

A Comprehensive Study on Factors Affecting the Calibration of Potential Evapotranspiration Derived from the Thornthwaite Model

by Haobo Li, Chenhui Jiang, Suelynn Choy, Xiaoming Wang, Kefei Zhang and Dejun Zhu

Remote Sens. 2022, 14(18), 4644; https://doi.org/10.3390/rs14184644 - 16 Sep 2022

Cited by 7 | Viewed by 2057

Abstract

Potential evapotranspiration (PET) is generally estimated using empirical models; thus, how to improve PET estimation accuracy has received widespread attention in recent years. Among all the models, although the temperature-driven Thornthwaite (TH) model is easy to operate, its estimation accuracy is rather limited. Although previous researchers proved that the accuracy of TH-PET can be greatly improved by using a limited number of variables to conduct calibration exercises, only preliminary experiments were conducted. In this study, to refine this innovation practice, we comprehensively investigated the factors that affect the calibration performances, including the selection of variables, seasonal effects, and spatial distribution of Global Navigation Satellite System (GNSS)/weather stations. By analyzing the factors and their effects, the following conclusions have been drawn: (1) an optimal variable selection scheme containing zenith total delay, temperature, pressure, and mean Julian Date was proposed; (2) the most salient improvements are in the winter and summer seasons, with improvement rates over 80%; (3) with the changes in horizontal (2.771–44.723 km) and height (1.239–344.665 m) differences among ten pairs of GNSS/weather stations, there are no obvious differences in the performances. These findings can offer an in-depth understanding of this practice and provide technical references to future applications. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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20 pages, 3654 KiB

Open AccessArticle

An Improved Method for Rainfall Forecast Based on GNSS-PWV

by Longjiang Li, Kefei Zhang, Suqin Wu, Haobo Li, Xiaoming Wang, Andong Hu, Wang Li, Erjiang Fu, Minghao Zhang and Zhen Shen

Remote Sens. 2022, 14(17), 4280; https://doi.org/10.3390/rs14174280 - 30 Aug 2022

Cited by 19 | Viewed by 2565

Abstract

Global navigation satellite systems (GNSS) has been applied to the sounding of precipitable water vapor (PWV) due to its high accuracy and high spatiotemporal resolutions. PWV obtained from GNSS (GNSS-PWV) can be used to investigate extreme weather phenomena, such as the formation mechanism and prediction of rainfalls. In the study, a new, improved model for rainfall forecasting was developed based on GNSS data and rainfall data for the 9-year period from 2010 to 2018 at 66 stations located in the USA. The new model included three prediction factors—PWV value, PWV increase, maximum hourly PWV increase. The two key tasks involved for the development of the model were the determination of the thresholds for each prediction factor and the selection of the optimal strategy for using the three prediction factors together. For determining the thresholds, both critical success index (CSI) and true skill statistic (TSS) were tested, and results showed that TSS outperformed CSI for all rainfall events tested. Then, various strategies by combining the three prediction factors together were also tested, and results indicated that the best forecast result was from the case that any two of the prediction factors were over their own thresholds. Finally, the new model was evaluated using the GNSS data for the 2-year period from 2019 to 2020 at the above mentioned 66 stations, and the probability of detection (POD) and false-alarms rate (FAR) were adopted to measure the model performances. Over the 66 stations, the POD values ranged from 73% to 97% with the mean of 87%, and the FARs ranged from 26% to 77% with the mean of 53%. Moreover, it was also found that both POD and FAR values were related to the region of the station; e.g., the results at the stations that are located in humid regions were better than the ones located in dry regions. All these results suggest the feasibility and good performance of using GNSS-PWV for forecasting rainfall. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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22 pages, 10385 KiB

Open AccessArticle

Weighted Mean Temperature Hybrid Models in China Based on Artificial Neural Network Methods

by Meng Cai, Junyu Li, Lilong Liu, Liangke Huang, Lv Zhou, Ling Huang and Hongchang He

Remote Sens. 2022, 14(15), 3762; https://doi.org/10.3390/rs14153762 - 5 Aug 2022

Cited by 3 | Viewed by 1693

Abstract

The weighted mean temperature (T_m) is crucial for converting zenith wet delay to precipitable water vapor in global navigation satellite system meteorology. Mainstream T_m models have the shortcomings of poor universality and severe local accuracy loss, and they cannot reflect the nonlinear relationship between T_m and meteorological/spatiotemporal factors. Artificial neural network methods can effectively solve these problems. This study combines the advantages of the models that need in situ meteorological parameters and the empirical models to propose T_m hybrid models based on artificial neural network methods. The verification results showed that, compared with the Bevis, GPT3, and HGPT models, the root mean square errors (RMSEs) of the new three hybrid models were reduced by 35.3%/32.0%/31.6%, 40.8%/37.8%/37.4%, and 39.5%/36.4%/36.0%, respectively. The consistency of the new three hybrid models was more stable than the Bevis, GPT3, and HGPT models in terms of space and time. In addition, the three models occupy 99.6% less computer storage space than the GPT3 model, and the number of parameters was reduced by 99.2%. To better evaluate the improvement of hybrid models T_m in the precipitable water vapor (PWV) retrieval, the PWVs calculated using the radiosonde T_m and zenith wet delay (ZWD) were used as the reference. The RMSE of PWV derived from the best hybrid model’s T_m and the radiosonde ZWD meets the demand for meteorological research and is improved by 33.9%, 36.4%, and 37.0% compared with that of Bevis, GPT3, and HGPT models, respectively. The hypothesis testing results further verified that these improvements are significant. Therefore, these new models can be used for high-precision T_m estimation in China, especially in Global Navigation Satellite System (GNSS) receivers without ample storage space. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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15 pages, 22360 KiB

Open AccessArticle

GNSS Storm Nowcasting Demonstrator for Bulgaria

by Guergana Guerova, Jan Douša, Tsvetelina Dimitrova, Anastasiya Stoycheva, Pavel Václavovic and Nikolay Penov

Remote Sens. 2022, 14(15), 3746; https://doi.org/10.3390/rs14153746 - 4 Aug 2022

Cited by 8 | Viewed by 1859

Abstract

Global Navigation Satellite System (GNSS) is an established atmospheric monitoring technique delivering water vapour data in near-real time with a latency of 90 min for operational Numerical Weather Prediction in Europe within the GNSS water vapour service (E-GVAP). The advancement of GNSS processing made the quality of real-time GNSS tropospheric products comparable to near-real-time solutions. In addition, they can be provided with a temporal resolution of 5 min and latency of 10 min, suitable for severe weather nowcasting. This paper exploits the added value of sub-hourly real-time GNSS tropospheric products for the nowcasting of convective storms in Bulgaria. A convective Storm Demonstrator (Storm Demo) is build using real-time GNSS tropospheric products and Instability Indices to derive site-specific threshold values in support of public weather and hail suppression services. The Storm Demo targets the development of service featuring GNSS products for two regions with hail suppression operations in Bulgaria, where thunderstorms and hail events occur between May and September, with a peak in July. The Storm Demo real-time Precise Point Positioning processing is conducted with the G-Nut software with a temporal resolution of 15 min for 12 ground-based GNSS stations in Bulgaria. Real-time data evaluation is done using reprocessed products and the achieved precision is below 9 mm, which is within the nowcasting requirements of the World Meteorologic Organisation. For the period May–September 2021, the seasonal classification function for thunderstorm nowcasting is computed and evaluated. The probability of thunderstorm detection is 83%, with a false alarm ration of 38%. The added value of the high temporal resolution of the GNSS tropospheric gradients is investigated for a storm case on 24–30 August 2021. Real-time tropospheric products and classification functions are integrated and updated in real-time on a publicly accessible geoportal. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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17 pages, 4738 KiB

Open AccessArticle

An Improved Spatiotemporal Weighted Mean Temperature Model over Europe Based on the Nonlinear Least Squares Estimation Method

by Bingbing Zhang, Zhengtao Wang, Wang Li, Wei Jiang, Yi Shen, Yan Zhang, Shike Zhang and Kunjun Tian

Remote Sens. 2022, 14(15), 3609; https://doi.org/10.3390/rs14153609 - 28 Jul 2022

Cited by 2 | Viewed by 1719

Abstract

Weighted average temperature (Tm) plays a crucial role in global navigation satellite system (GNSS) precipitable water vapor (PWV) retrieval. Aiming at the poor applicability of the existing Tm models in Europe, in the article, we used observations from 48 radiosonde stations over Europe from 2014 to 2020 to establish a weighted average temperature model in Europe (ETm) by the nonlinear least squares estimation method. The ETm model takes into account factors such as ground temperature, water vapor pressure, latitude, and their annual variation, semiannual variation and diurnal variation. Taking the Tm obtained from the radiosonde data by the integration method in 2021 as the reference value, the accuracy of the ETm model was evaluated and compared with the commonly used Bevis model, ETmPoly model, and GPT2w model. The results of the 48 modeled stations showed that the mean bias and root mean square (RMS) values of the ETm model were 0.06 and 2.85 K, respectively, which were 21.7%, 11.5%, and 31.8% higher than the Bevis, ETmPoly, and GPT2w-1 (1° × 1° resolution) models, respectively. In addition, the radiosonde data of 12 non-modeling stations over Europe in 2021 were selected to participate in the model accuracy validation. The mean bias and RMS values of the ETm model were –0.07 and 2.87 K, respectively. Compared with the Bevis, ETmPoly, and GPT2w-1 models, the accuracy (in terms of RMS values) increased by 20.5%, 10.6%, and 35.2%, respectively. Finally, to further verify the superiority of the ETm model, the ETm model, and other Tm models were applied to the GNSS PWV calculation. The ETm model had mean RMS_PWV and RMS_PWV/PWV values of 0.17 mm and 1.03%, respectively, which were less than other Tm models. Therefore, the ETm model has essential applications in GNSS PWV over Europe. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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16 pages, 8135 KiB

Open AccessArticle

Comprehensive Analysis and Validation of the Atmospheric Weighted Mean Temperature Models in China

by Yongjie Ma, Qingzhi Zhao, Kan Wu, Wanqiang Yao, Yang Liu, Zufeng Li and Yun Shi

Remote Sens. 2022, 14(14), 3435; https://doi.org/10.3390/rs14143435 - 17 Jul 2022

Cited by 3 | Viewed by 1743

Abstract

Atmospheric weighted mean temperature (T_m) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical T_m models using meteorological or non-meteorological parameters have been proposed to calculate PWV, but their accuracy and reliability cannot be guaranteed in some regions. To validate and determine the optimal T_m model for PWV retrieval in China, this paper analyzes and evaluates some typical T_m models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the T_m model for China (CT_m), the Global Weighted Mean Temperature-H (GT_m-H) and the Global Tropospheric (GTrop) models. The T_m values of these models are first obtained at corresponding radiosonde (RS) stations in China over the period of 2011 to 2020. The corresponding T_m values of 87 RS stations in China are also calculated using the layered meteorological data and regarded as the reference. Comparison results show that the accuracy of these five T_m models in China has an obvious geographical distribution and decreases along with increasing altitude and latitude, respectively. The average root mean square (RMS) and Bias for the Linear, GPT3, CT_m, GT_m-H and GTrop models are 4.2/3.7/3.4/3.6/3.3 K and 0.7/−1.0/0.7/−0.1/0.3 K, respectively. Among these models, Linear and GPT3 models have lower accuracy in high-altitude regions, whereas CT_m, GT_m-H and GTrop models show better accuracy and stability throughout the whole China. These models generally have higher accuracy in regions with low latitude and lower accuracy in regions with middle and high latitudes. In addition, Linear and GPT3 models have poor accuracy in general, whereas GT_m-H and CT_m models are obviously less accurate and stable than GTrop model in regions with high latitude. These models show different accuracies across the four geographical regions of China, with GTrop model demonstrating the relatively better accuracy and stability. Therefore, the GTrop model is recommended to obtain T_m for calculating PWV in China. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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22 pages, 3327 KiB

Open AccessArticle

GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series

by Annarosa Quarello, Olivier Bock and Emilie Lebarbier

Remote Sens. 2022, 14(14), 3379; https://doi.org/10.3390/rs14143379 - 13 Jul 2022

Cited by 1 | Viewed by 1971

Abstract

Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data, which have not yet been used in this context. This paper proposes a novel segmentation method called segfunc that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm, which is the most efficient exact algorithm to estimate the change point positions. The performance of the method is assessed through numerical simulation experiments. It is implemented in the R package GNSSseg, which is available on the CRAN. This paper presents the application of the method to a real data set from a global network of 120 GNSS stations. A hit rate of 32% is achieved with respect to available metadata. The final segmentation is made in a semi-automatic way, where the change points detected by three different penalty criteria are manually selected. In this case, the hit rate reaches 60% with respect to the metadata. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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26 pages, 3674 KiB

Open AccessArticle

Verification and Validation of the COSMIC-2 Excess Phase and Bending Angle Algorithms for Data Quality Assurance at STAR

by Bin Zhang, Shu-peng Ho, Changyong Cao, Xi Shao, Jun Dong and Yong Chen

Remote Sens. 2022, 14(14), 3288; https://doi.org/10.3390/rs14143288 - 8 Jul 2022

Cited by 8 | Viewed by 2486

Abstract

In recent years, Global Navigation Satellite System (GNSS) radio occultation (RO) has become a critical observation system for global operational numerical weather prediction. Constellation Observing System for Meteorology, Ionosphere, Climate (COSMIC) 2 (COSMIC-2) has been a backbone RO mission for NOAA. NOAA also began to purchase RO data from commercial sources in 2020. To ensure the consistent quality of RO data from different sources, NOAA Center for Satellite Applications and Research (STAR) has developed capabilities to process all available RO data from different missions. This paper describes the STAR RO processing systems which convert the pseudo-range and carrier phase observations to excess phases and bending angles (BAs). We compared our COSMIC-2 data products with those processed by the University Corporation for Atmospheric Research (UCAR) COSMIC Data Analysis and Archive Center (CDAAC). We processed more than twelve thousand COSMIC-2 occultation profiles. Our results show that the excess phase difference between UCAR and STAR is within a few centimeters at high altitudes, although the difference increases towards the lower atmosphere. The BA profiles derived from the excess phase are consistent with UCAR. The mean relative BA differences at impact height from 10 to 30 km are less than 0.1% for GLObal NAvigation Satellite System (GLONASS) L2C signals and Global Positioning System (GPS) L2C and L2P signals. The standard deviations are 1.15%, 1.15%, and 1.32% for GLONASS L2C signal and for GPS L2C and L2P signals, respectively. The BA profiles agree with those derived from European Center for Medium-range Weather Forecast (ECMWF) reanalysis version 5 (ERA5). The Signal-to-Noise-Ratio (SNR) plays an essential role in the processing. The STAR BA profiles with higher L1 SNRs (L1 at 80 km) tend to yield more consistent results than those from UCAR, with a negligible difference and a smaller deviation than lower SNR profiles. Profiles with lower SNR values tend to show a more significant standard deviation towards the surface during the open-loop stage in the lower troposphere than those of higher SNR. We also found that the different COSMIC-2 clock solutions could contribute to the significant relative BA difference at high altitudes; however, it has little effect on the lower troposphere comparisons given larger BA values. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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19 pages, 3965 KiB

Open AccessArticle

Comprehensive Precipitable Water Vapor Retrieval and Application Platform Based on Various Water Vapor Detection Techniques

by Qingzhi Zhao, Xiaoya Zhang, Kan Wu, Yang Liu, Zufeng Li and Yun Shi

Remote Sens. 2022, 14(10), 2507; https://doi.org/10.3390/rs14102507 - 23 May 2022

Cited by 22 | Viewed by 3156

Abstract

Atmospheric water vapor is one of the important parameters for weather and climate studies. Generally, atmospheric water vapor can be monitored by some techniques, such as the Global Navigation Satellite System (GNSS), radiosonde (RS), remote sensing and numerical weather forecast (NWF). However, the comprehensive retrieval and application of precipitable water vapor (PWV) using multi techniques has been hardly performed before, which becomes the focus of this study. A comprehensive PWV retrieval and application platform (CPRAP) is first established by combing the ground-based (GNSS), space-based (Fengyun-3A, Sentinel-3A) and reanalysis-based (the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting, ERA5) techniques. Additionally, its applications are then extended to drought and rainfall monitoring using the CPRAP-derived PWV. The statistical result shows that PWV derived from ground-based GNSS has high accuracy in China, with the root mean square (RMS), Bias and mean absolute error (MAE) of 2.15, 0.05 and 1.65 mm, respectively, when the RS-derived PWV is regarded as the reference. In addition, the accuracy of PWV derived from the space-based (FY-3A and Sentinel-3A) techniques technique is also validated and the RMS, Bias and MAE of a Medium Resolution Spectral Imager (MERSI) onboard Fengyun-3A (FY-3A) and an Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A are 4.46/0.56/3.61 mm and 2.95/0.01/1.37 mm, respectively. Then, the performance of ERA5-derived PWV is evaluated based on GNSS-derived and RS-derived PWV. The result also shows good accuracy of ERA5-provided PWV with the averaged RMS, Bias and MAE of 1.86/0.11/1.48 mm and 0.90/−0.05/1.51 mm, respectively. Finally, the PWV data derived from the established CPRAP are further used for drought and rainfall monitoring. The applied results reveal that the calculated the standardized precipitation evapotranspiration index (SPEI) using the CPRAP-derived PWV can monitor the drought and the correlation coefficient ranges from 0.83 to 0.9 when compared with the SPEI. Furthermore, in this paper correlation analysis between PWV derived from the CPRAP and rainfall, and its potential for rainfall monitoring was also validated. Such results verify the significance of the established CPRAP for weather and climate studies. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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17 pages, 9014 KiB

Open AccessArticle

Weighted Mean Temperature Modelling Using Regional Radiosonde Observations for the Yangtze River Delta Region in China

by Li Li, Yuan Li, Qimin He and Xiaoming Wang

Remote Sens. 2022, 14(8), 1909; https://doi.org/10.3390/rs14081909 - 15 Apr 2022

Cited by 5 | Viewed by 2183

Abstract

Precipitable water vapor can be estimated from the Global Navigation Satellite System (GNSS) signal’s zenith wet delay (ZWD) by multiplying a conversion factor, which is a function of weighted mean temperature (

T_{m}

) over the GNSS station. Obtaining T_m is [...] Read more.

T_{m}

) over the GNSS station. Obtaining T_m is an important step in GNSS precipitable water vapor (PWV) conversion. In this study, aiming at the problem that T_m is affected by space and time, observations from seven radiosonde stations in the Yangtze River Delta region of China during 2015−2016 were used to establish both linear and nonlinear multifactor regional T_m model (RTM). Compared with the Bevis model, the results showed that the bias of yearly one-factor RTM, two-factor RTM and three-factor RTM was reduced by 0.55 K, 0.68 K and 0.69 K, respectively. Meanwhile, the RMSE of yearly one-factor, two-factor and three-factor RTM was reduced by 0.56 K, 0.80 K and 0.83 K, respectively. Compared with the yearly three-factor linear RTM, the mean bias and RMSE of the linear seasonal three-factor RTMs decreased by 0.06 K and 0.10 K, respectively. The precision of nonlinear seasonal three-factor RTMs is comparable to linear seasonal three-factor RTMs, but the expressions of the linear RTMs are easier to use. Therefore, linear seasonal three-factor RTMs are more suitable for calculating T_m and are recommended to use for PWV conversion in the Yangtze River Delta region. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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12 pages, 3291 KiB

Open AccessTechnical Note

Adaptive Voxel-Based Model for the Dynamic Determination of Tomographic Region

by Nan Ding, Xinglong Tan, Xin Liu, Zhifen He, Yu Zhang, Yuchen Wang, Shubi Zhang, Lucas Holden and Kefei Zhang

Remote Sens. 2023, 15(2), 492; https://doi.org/10.3390/rs15020492 - 13 Jan 2023

Viewed by 1560

Abstract

Water vapor is a dominant greenhouse gas. It significantly impacts the atmosphere by trapping heat and infrared radiation. The greenhouse effect is essential for life on Earth but can also be harmful. Although the amount of water vapor in the atmosphere is not much during the water cycle, it is the most active element in rapid changes in both spatial and temporal domains. GNSS tomography’s ability to model the high-resolution 3D distribution of water vapor is a promising means of measuring and monitoring the spatial-temporal variation of water vapor. This study developed and tested a new GNSS tomographic model using adaptive voxel parameterization. It uses a 3D traversal algorithm to dynamically determine the position of voxels at each tomographic sampling epoch. It means that the new algorithm can exclude the voxels that no GNSS signals pass through, reducing the influence of such voxels in the construction of the tomographic model. This study provides a new approach to investigating the inversion of atmospheric water vapor. The experiment used one-month data from the Hong Kong network in September 2020, and the results were compared with the general system. The local radiosonde data is a reference for verification of the two approaches. The mean root-mean-square error (RMSE) and IQR of the water vapor profiles derived from AAR are decreased by 55% and 48% with respect to the GFR results, respectively. The results show that the accuracy of the new method outperforms the general approach in the result statistics. The successful implementation of the research has significant potential to drive the development of GNSS tomography in the study of weather and climate change. Full article

(This article belongs to the Special Issue Advanced GNSS Remote Sensing Techniques for Meteorology and Climate Sciences)

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