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Climate Modelling and Monitoring Using GNSS

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 41558

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Special Issue Editors

Dr. Roeland Van Malderen

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Guest Editor

Royal Meteorological Institute of Belgium, Avenue Circulaire 3, 1180 Bruxelles, Belgium
Interests: GNSS meteorology and climate; atmospheric sounding and composition; data quality assessment; homogenization

Prof. Dr. Marcelo Santos

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Guest Editor

Dept. of Geodesy and Geomatics Engineering, University of New Brunswick, P.O.Box 4400, Fredericton, NB E3B 5A3, Canada
Interests: space and physical geodesy; GNSS and navigation; GNSS meteorology

Prof. Dr. Kefei Zhang

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Guest Editor

School of Science, RMIT University, Melbourne, Australia
Interests: GPS/GNSS; geodesy; atmospheric modelling; radio occultation; indoor positioning and tracking; space situational awareness; satellite orbit determination; space weather and severe weather
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Special Issue Information

Dear Colleagues,

Water vapor is a key component for the Earth’s climate, an essential climate variable (ECV), as it is the most important natural greenhouse gas and responsible for the largest known feedback mechanism for driving climate change. The global warming and the hydrological cycle are strongly linked, with a global scaling ratio of 5–7% of integrated water vapor (IWV) per 1 K increase. Global satellite navigation systems (GNSS) are a relatively new technology capable of retrieving IWV measurements from its zenith total delays globally, at a high temporal resolution and accuracy, and under all weather conditions. With the earliest measurements dating back to the mid-1990s, and an ever increased number and densification of continuously operating GNSS networks, interest has grown rapidly in recent years in assessing and maximizing the benefits of GNSS measurements for climate research, both as a basic observable of the water cycle (e.g., to evaluate IWV trends and variability), and as validation (and even benchmark) data for atmospheric models. In this context, several global and regional datasets have been homogeneously reprocessed and reanalyzed, and homogenization and mining activities of those datasets have been undertaken.

The main objective of this Special Issue is to strengthen the collaboration between the remote sensing GNSS and the climate (modelling) research communities. Particular areas that could be addressed include but are not limited to:

The assessment and data mining of long-term (reprocessed) GNSS datasets for use in climate studies (homogeneity, spatial representativeness, temporal relevance, etc.);
Use of GNSS long-term datasets to evaluate the performance of neutral-atmospheric models;
Study of the long-term variability of integrated water vapor based on GNSS datasets, possibly in comparison with other datasets (from observations or models);
Advocating data assimilation of GNSS neutral-atmosphere products in climate re-analysis of climate models;
Impacts of assimilating GNSS neutral-atmosphere into re-analysis models.

Dr. Roeland Van Malderen
Prof. Dr. Marcelo Santos
Prof. Dr. Kefei Zhang
Guest Editors

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Keywords

Integrated water vapor
Climate modeling
GNSS
Long-term monitoring
Reprocessing and homogeneity
Geodetic data analytics
Neutral atmosphere
Zenith delays
Gradients

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Editorial

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2 pages, 160 KiB

Open AccessEditorial

Editorial for the Special Issue ″Climate Modelling and Monitoring Using GNSS″

by Roeland Van Malderen, Marcelo Santos and Kefei Zhang

Remote Sens. 2022, 14(17), 4371; https://doi.org/10.3390/rs14174371 - 2 Sep 2022

Viewed by 1231

Abstract

Reliably modelling and monitoring the climate requires robust data that can be used to feed meteorological models, and, most importantly, to independently validate those models [...] Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

Research

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22 pages, 9297 KiB

Open AccessArticle

Global Spatiotemporal Variability of Integrated Water Vapor Derived from GPS, GOME/SCIAMACHY and ERA-Interim: Annual Cycle, Frequency Distribution and Linear Trends

by Roeland Van Malderen, Eric Pottiaux, Gintautas Stankunavicius, Steffen Beirle, Thomas Wagner, Hugues Brenot, Carine Bruyninx and Jonathan Jones

Remote Sens. 2022, 14(4), 1050; https://doi.org/10.3390/rs14041050 - 21 Feb 2022

Cited by 9 | Viewed by 2375

Abstract

Atmospheric water vapor plays a prominent role in climate change and atmospheric, meteorological, and hydrological processes. Because of its high spatiotemporal variability, precise quantification of water vapor is challenging. This study investigates Integrated Water Vapor (IWV) variability for the period 1995–2010 at 118 globally distributed Global Positioning System (GPS) sites, using additional UV/VIS satellite retrievals by GOME, SCIAMACHY, and GOME-2 (denoted as GOMESCIA below), plus ERA-Interim reanalysis output. Apart from spatial representativeness differences, particularly at coastal and island sites, all three IWV datasets correlate well with the lowest mean correlation coefficient of 0.878 (averaged over all the sites) between GPS and GOMESCIA. We confirm the dominance of standard lognormal distribution of the IWV time series, which can be explained by the combination of a lower mode (dry season characterized by a standard lognormal distribution with a low median value) and an upper mode (wet season characterized by a reverse lognormal distribution with high median value) in European, Western American, and subtropical sites. Despite the relatively short length of the time series, we found a good consistency in the sign of the continental IWV trends, not only between the different datasets, but also compared to temperature and precipitation trends. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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20 pages, 4965 KiB

Open AccessArticle

Understanding the Present-Day Spatiotemporal Variability of Precipitable Water Vapor over Ethiopia: A Comparative Study between ERA5 and GPS

by Abdisa Kawo Koji, Roeland Van Malderen, Eric Pottiaux and Bert Van Schaeybroeck

Remote Sens. 2022, 14(3), 686; https://doi.org/10.3390/rs14030686 - 31 Jan 2022

Cited by 6 | Viewed by 3408

Abstract

Atmospheric water vapor plays a crucial role in atmospheric, climate change, meteorological, and hydrological processes. In a country like Ethiopia, with its complex topography and synoptic-scale spatiotemporal circulation patterns, the analysis of the spatiotemporal variability of precipitable water vapor (PWV) is very challenging, and is hampered by the lack of long observational datasets. In this study, we process the PWV over eight Ethiopian global positioning system (GPS) sites and one close to the Ethiopian eastern border, for the available common period 2013–2020, and compare with the PWV retrieved from the state-of-the-art ERA5 reanalysis. Both PWV datasets agree very well at our sample, with correlation coefficients between 0.96 and 0.99, GPS-PWV show a moderate wet bias compared to ERA5-PWV for the majority of the sites, and an overall root mean square error of 3.4 mm. Seasonal and diurnal cycles are also well captured by these datasets. The seasonal variations of PWV and precipitation at the sites agree very well. Maximum diurnal PWV amplitudes are observed for stations near water bodies or dense vegetation, such as Arbaminch (ARMI) and Bahir Dar (BDMT). At those stations, the PWV behavior at heavy rainfall events has been investigated and an average 25% increase (resp. decrease) from 12 h before (resp. 12 h after) the start of the rainfall event, when the PWV peaks, has been observed. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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27 pages, 8244 KiB

Open AccessArticle

Water Vapour Assessment Using GNSS and Radiosondes over Polar Regions and Estimation of Climatological Trends from Long-Term Time Series Analysis

by Monia Negusini, Boyan H. Petkov, Vincenza Tornatore, Stefano Barindelli, Leonardo Martelli, Pierguido Sarti and Claudio Tomasi

Remote Sens. 2021, 13(23), 4871; https://doi.org/10.3390/rs13234871 - 30 Nov 2021

Cited by 8 | Viewed by 2871

Abstract

The atmospheric humidity in the Polar Regions is an important factor for the global budget of water vapour, which is a significant indicator of Earth’s climate state and evolution. The Global Navigation Satellite System (GNSS) can make a valuable contribution in the calculation of the amount of Precipitable Water Vapour (PW). The PW values retrieved from Global Positioning System (GPS), hereafter PW_GPS, refer to 20-year observations acquired by more than 40 GNSS geodetic stations located in the polar regions. For GNSS stations co-located with radio-sounding stations (RS), which operate Vaisala radiosondes, we estimated the PW from RS observations (PW_RS). The PW values from the ERA-Interim global atmospheric reanalysis were used for validation and comparison of the results for all the selected GPS and RS stations. The correlation coefficients between times series are very high: 0.96 for RS and GPS, 0.98 for RS and ERA in the Arctic; 0.89 for RS and GPS, 0.97 for RS and ERA in Antarctica. The Root-Mean-Square of the Error (RMSE) is 0.9 mm on average for both RS vs. GPS and RS vs. ERA in the Arctic, and 0.6 mm for RS vs. GPS and 0.4 mm for RS vs. ERA in Antarctica. After validation, long-term trends, both for Arctic and Antarctic regions, were estimated using Hector scientific software. Positive PW_GPS trends dominate at Arctic sites near the borders of the Atlantic Ocean. Sites located at higher latitudes show no significant values (at 1σ level). Negative PW_GPS trends were observed in the Arctic region of Greenland and North America. A similar behaviour was found in the Arctic for PW_RS trends. The stations in the West Antarctic sector show a general positive PW_GPS trend, while the sites on the coastal area of East Antarctica exhibit some significant negative PW_GPS trends, but in most cases, no significant PW_RS trends were found. The present work confirms that GPS is able to provide reliable estimates of water vapour content in Arctic and Antarctic regions too, where data are sparse and not easy to collect. These preliminary results can give a valid contribution to climate change studies. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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20 pages, 12338 KiB

Open AccessArticle

A Comprehensive Evaluation of Key Tropospheric Parameters from ERA5 and MERRA-2 Reanalysis Products Using Radiosonde Data and GNSS Measurements

by Lijie Guo, Liangke Huang, Junyu Li, Lilong Liu, Ling Huang, Bolin Fu, Shaofeng Xie, Hongchang He and Chao Ren

Remote Sens. 2021, 13(15), 3008; https://doi.org/10.3390/rs13153008 - 30 Jul 2021

Cited by 21 | Viewed by 2946

Abstract

Tropospheric delay is a major error source in the Global Navigation Satellite System (GNSS), and the weighted mean temperature (T_m) is a key parameter in precipitable water vapor (PWV) retrieval. Although reanalysis products like the National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-Interim) data have been used to calculate and model the tropospheric delay, T_m, and PWV, the limitations of the temporal and spatial resolutions of the reanalysis data have affected their performance. The release of the fifth-generation accurate global atmospheric reanalysis (ERA5) and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) provide the opportunity to overcome these limitations. The performances of the zenith tropospheric delay (ZTD), zenith wet delay (ZWD), T_m, and zenith hydrostatic delay (ZHD) of ERA5 and MERRA-2 data from 2016 to 2017 were evaluated in this work using GNSS ZTD and radiosonde data over the globe. Taking GNSS ZTD as a reference, the ZTD calculated from MERRA-2 and ERA5 pressure-level data were evaluated in temporal and spatial scales, with an annual mean bias and root mean square (RMS) of 2.3 and 10.9 mm for ERA5 and 4.5 and 13.1 mm for MERRA-2, respectively. Compared to radiosonde data, the ZHD, ZWD, and T_m derived from ERA5 and MERRA-2 data were also evaluated on temporal and spatial scales, with annual mean bias values of 1.1, 1.7 mm, and 0.14 K for ERA5 and 0.5, 4.8 mm, and –0.08 K for MERRA-2, respectively. Meanwhile, the annual mean RMS was 4.5, 10.5 mm, and 1.03 K for ERA5 and 4.4, 13.6 mm, and 1.17 K for MERRA-2, respectively. Tropospheric parameters derived from MERRA-2 and ERA5, with improved temporal and spatial resolutions, can provide a reference for GNSS positioning and PWV retrieval. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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20 pages, 86762 KiB

Open AccessArticle

A Regional Model for Predicting Tropospheric Delay and Weighted Mean Temperature in China Based on GRAPES_MESO Forecasting Products

by Liying Cao, Bao Zhang, Junyu Li, Yibin Yao, Lilong Liu, Qishun Ran and Zhaohui Xiong

Remote Sens. 2021, 13(13), 2644; https://doi.org/10.3390/rs13132644 - 5 Jul 2021

Cited by 10 | Viewed by 3065

Abstract

Accurate tropospheric delay (TD) and weighted mean temperature (Tm) are important for Global Navigation Satellite System (GNSS) positioning and GNSS meteorology. For this purpose, plenty of empirical models have been built to provide estimates of TD and Tm. However, these models cannot resolve TD and Tm variations at synoptic timescales since they only model the average annual, semi-annual, and/or daily variations. As a result, the existed empirical models cannot perform well under extreme weather conditions. To address this limitation, we propose to estimate Zenith Hydrostatic Delay (ZHD), Zenith Wet Delay (ZWD), and Tm directly from the stratified numerical weather forecasting products of the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_MESO) of China. The GRAPES_MESO forecasting data has a temporal resolution of 3 h, which provides the opportunity to resolve the synoptic variation. However, it is found that the estimated ZWD and Tm exhibit apparent systematic deviation from in situ observation-based estimates, which is due to the inherent biases in the GRAPES_MESO data. To solve this problem, we propose to correct these biases using a linear model and a spherical cap harmonic model. The estimates after correction are termed as the “CTropGrid” products. When validated by the radiosonde data, the CTropGrid product has biases of 1.5 mm, −0.7 mm, and −0.1 K, and Root Mean Square (RMS) error of 8.9 mm, 20.2 mm, and 1.5 K for ZHD, ZWD, and Tm. Compared to the widely used GPT2w model, the CTropGrid products have improved the accuracies of ZHD, ZWD, and Tm by 11.9%, 55.6%, and 60.5% in terms of RMS. When validating the Zenith Tropospheric Delay (ZTD) products (the sum of ZHD and ZWD) using the IGS ZTD data, the CTropGrid ZTD has a bias of −0.7 mm and an RMS of 35.8 mm, which is 22.7% better than the GPT2w model in terms of RMS. Besides the accuracy improvements, the CTropGrid products well model the synoptic-scale variations of ZHD, ZWD, and Tm. Compared to the existing empirical models that only capture the tidal (seasonal and/or diurnal) variations, the CTropGrid products capture well the non-tidal variations of ZHD, ZWD, and Tm, which enhances the tropospheric delay corrections and GNSS water vapor monitoring at synoptic timescales. Therefore, the CTropGrid product is an important progress in GNSS positioning and GNSS meteorology. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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15 pages, 2953 KiB

Open AccessArticle

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

by Haobo Li, Xiaoming Wang, Suqin Wu, Kefei Zhang, Erjiang Fu, Ying Xu, Cong Qiu, Jinglei Zhang and Li Li

Remote Sens. 2021, 13(7), 1390; https://doi.org/10.3390/rs13071390 - 4 Apr 2021

Cited by 13 | Viewed by 3080

Abstract

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. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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18 pages, 6304 KiB

Open AccessArticle

Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS and ERA5 Data

by Song Li, Tianhe Xu, Nan Jiang, Honglei Yang, Shuaimin Wang and Zhen Zhang

Remote Sens. 2021, 13(5), 1004; https://doi.org/10.3390/rs13051004 - 6 Mar 2021

Cited by 22 | Viewed by 2974

Abstract

The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predicting ZTD. In this paper, we develop three new regional ZTD models based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTD products and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, the ERA5 data is extended to ERA5S-ZTD and ERA5P-ZTD as the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to construct ZTD models based on the LSSVM algorithm, including the without background data, with the ERA5S-ZTD, and with the ERA5P-ZTD. To investigate the advantage and feasibility of the proposed ZTD models, we evaluate the accuracy of two background data and three schemes by segmental comparison with the IGS-ZTD of 85 IGS stations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for the ERA5S-ZTD, and 10.7 mm for the ERA5P-ZTD. The overall average RMSE is 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for the ZTD model with ERA5S-ZTD and ERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for the ZTD model with ERA5P-ZTD, the loop verification is performed by estimating the ZTD values of each available IGS station. In actuality, the monthly improvement rate of estimated ZTD is positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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18 pages, 3313 KiB

Open AccessArticle

A Regional Zenith Tropospheric Delay (ZTD) Model Based on GPT3 and ANN

by Fei Yang, Jiming Guo, Chaoyang Zhang, Yitao Li and Jun Li

Remote Sens. 2021, 13(5), 838; https://doi.org/10.3390/rs13050838 - 24 Feb 2021

Cited by 24 | Viewed by 3773

Abstract

The delays of radio signals transmitted by global navigation satellite system (GNSS) satellites and induced by neutral atmosphere, which are usually represented by zenith tropospheric delay (ZTD), are required as critical information both for GNSS positioning and navigation and GNSS meteorology. Establishing a stable and reliable ZTD model is one of the interests in GNSS research. In this study, we proposed a regional ZTD model that makes full use of the ZTD calculated from regional GNSS data and the corresponding ZTD estimated by global pressure and temperature 3 (GPT3) model, adopting the artificial neutral network (ANN) to construct the correlation between ZTD derived from GPT3 and GNSS observations. The experiments in Hong Kong using Satellite Positioning Reference Station Network (SatRet) were conducted and three statistical values, i.e., bias, root mean square error (RMSE), and compound relative error (CRE) were adopted for our comparisons. Numerical results showed that the proposed model outperformed the parameter ZTD model (Saastamoinen model) and the empirical ZTD model (GPT3 model), with an approximately 56%/52% and 52%/37% RMSE improvement in the internal and external accuracy verification, respectively. Moreover, the proposed method effectively improved the systematic deviation of GPT3 model and achieved better ZTD estimation in both rainy and rainless conditions. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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16 pages, 3039 KiB

Open AccessArticle

Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

by Jinyun Guo, Rui Hou, Maosheng Zhou, Xin Jin, Chengming Li, Xin Liu and Hao Gao

Remote Sens. 2021, 13(3), 386; https://doi.org/10.3390/rs13030386 - 22 Jan 2021

Cited by 12 | Viewed by 3920

Abstract

From late 2019 to early 2020, forest fires in southeastern Australia caused huge economic losses and huge environmental pollution. Monitoring forest fires has become increasingly important. A new method of fire detection using the difference between global navigation satellite system (GNSS)-derived precipitable water vapor and radiosonde-derived precipitable water vapor (ΔPWV) is proposed. To study the feasibility of the new method, the relationship is studied between particulate matter 10 (PM10) (2.5 to 10 microns particulate matter) and ΔPWV based on Global Positioning System (GPS) data, radiosonde data, and PM10 data from 1 June 2019 to 1 June 2020 in southeastern Australia. The results show that before the forest fire, ΔPWV and PM10 were smaller and less fluctuating. When the forest fire happened, ΔPWV and PM10 were increasing. Then after the forest fire, PM10 became small with relatively smooth fluctuations, but ΔPWV was larger and more fluctuating. Correlation between the 15-day moving standard deviation (STD) time series of ΔPWV and PM10 after the fire was significantly higher than that before the fire. This study shows that ΔPWV is effective in monitoring forest fires based on GNSS technique before and during forest fires in climates with more uniform precipitation, and using ΔPWV to detect forest fires based on GNSS needs to be further investigated in climates with more precipitation and severe climate change. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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22 pages, 3016 KiB

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Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV

by Haobo Li, Xiaoming Wang, Suqin Wu, Kefei Zhang, Xialan Chen, Cong Qiu, Shaotian Zhang, Jinglei Zhang, Mingqiang Xie and Li Li

Remote Sens. 2020, 12(24), 4101; https://doi.org/10.3390/rs12244101 - 15 Dec 2020

Cited by 48 | Viewed by 3202

Abstract

Nowadays, the Global Navigation Satellite Systems (GNSS) have become an effective atmospheric observing technique to remotely sense precipitable water vapor (PWV) mainly due to their high spatiotemporal resolutions. In this study, from an investigation for the relationship between GNSS-derived PWV (GNSS-PWV) and heavy precipitation, it was found that from several hours before heavy precipitation, PWV was probably to start with a noticeable increase followed by a steep drop. Based on this finding, a new model including five predictors for heavy precipitation prediction is proposed. Compared with the existing 3-factor model that uses three predictors derived from the ascending trend of PWV time series (i.e., PWV value, PWV increment and rate of the PWV increment), the new model also includes two new predictors derived from the descending trend: PWV decrement and rate of PWV decrement. The use of the two new predictors for reducing the number of misdiagnosis predictions is proposed for the first time. The optimal set of monthly thresholds for the new five-predictor model in each summer month were determined based on hourly GNSS-PWV time series and precipitation records at three co-located GNSS/weather stations during the 8-year period 2010–2017 in the Hong Kong region. The new model was tested using hourly GNSS-PWV and precipitation records obtained at the above three co-located stations during the summer months in 2018 and 2019. Results showed that 189 of the 198 heavy precipitation events were correctly predicted with a lead time of 5.15 h, and the probability of detection reached 95.5%. Compared with the 3-factor method, the new model reduced the FAR score by 32.9%. The improvements made by the new model have great significance for early detection and predictions of heavy precipitation in near real-time. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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20 pages, 5623 KiB

Open AccessArticle

Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis Approach

by Wang Li, Dongsheng Zhao, Yi Shen and Kefei Zhang

Remote Sens. 2020, 12(23), 3851; https://doi.org/10.3390/rs12233851 - 24 Nov 2020

Cited by 15 | Viewed by 3100

Abstract

The global ionosphere map (GIM) is not capable of serving precise positioning and navigation for single frequency receivers in Australia due to sparse International GNSS Service (IGS) stations located in the vast land. This study proposes an approach to represent Australian total electron content (TEC) using the spherical cap harmonic analysis (SCHA) and artificial neural network (ANN). The new Australian TEC maps are released with an interval of 15 min for longitude and latitude in 0.5° × 0.5°. The validation results show that the Australian Ionospheric Maps (AIMs) well represent the hourly and seasonally ionospheric electrodynamic features over the Australian continent; the accuracy of the AIMs improves remarkably compared to the GIM and the model built only by the SCHA. The residual of the AIM is inversely proportional to the level of solar radiation. During the equinoxes and solstices in a solar minimum year, the residuals are 2.16, 1.57, 1.68, and 1.98 total electron content units (TECUs, 1 TECU = 10¹⁶ electron/m²), respectively. Furthermore, the AIM has a strong capability in capturing the adequate electrodynamic evolutions of the traveling ionospheric disturbances under severe geomagnetic storms. The results demonstrate that the ANN-aided SCHA method is an effective approach for mapping and investigating the TEC maps over Australia. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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21 pages, 7599 KiB

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Assessments of the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Tropospheric Conditions Using Radiosonde Data

by Ying Li, Yunbin Yuan and Xiaoming Wang

Remote Sens. 2020, 12(17), 2717; https://doi.org/10.3390/rs12172717 - 22 Aug 2020

Cited by 4 | Viewed by 2969

Abstract

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) retrieved temperature and specific humidity profiles can be widely used for weather and climate studies in troposphere. However, some aspects, such as the influences of background data on these retrieved moist profiles have not been discussed yet. This research evaluates RO retrieved temperature and specific humidity profiles from Wegener Center for Climate and Global Change (WEGC), Radio Occultation Meteorology Satellite Application Facility (ROM SAF) and University Corporation for Atmospheric Research (UCAR) Boulder RO processing centers by comparing with measurements from 10 selected Integrated Global Radiosonde Archive (IGRA) radiosonde stations in different latitudinal bands over 2007 to 2010. The background profiles used for producing their moist profiles are also compared with radiosonde. We found that RO retrieved temperature profiles from all centers agree well with radiosonde. Mean differences at polar, mid-latitudinal and tropical stations are varying within ±0.2 K, ±0.5 K and from −1 to 0.2 K, respectively, with standard deviations varying from 1 to 2 K for most pressure levels. The differences between RO retrieved and their background temperature profiles for WEGC are varying within ±0.5 K at altitudes above 300 hPa, and the differences for ROM SAF are within ±0.2 K, and that for UCAR are within 0.5 K at altitudes below 300 hPa. Both RO retrieved and background specific humidity above 600 hPa are found to have large positive differences (up to 40%) against most radiosonde measurements. Discrepancies of moist profiles among the three centers are overall minor at altitudes above 300 hPa for temperature and at altitudes above 700 hPa for specific humidity. Specific humidity standard deviations are largest at tropical stations in June July August months. It is expected that the outcome of this research can help readers to understand the characteristics of moist products among centers. Full article

(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)

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