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21 pages, 10795 KiB  
Article
COSMIC-2 RFI Prediction Model Based on CNN-BiLSTM-Attention for Interference Detection and Location
by Cheng-Long Song, Rui-Min Jin, Chao Han, Dan-Dan Wang, Ya-Ping Guo, Xiang Cui, Xiao-Ni Wang, Pei-Rui Bai and Wei-Min Zhen
Sensors 2024, 24(23), 7745; https://doi.org/10.3390/s24237745 - 4 Dec 2024
Viewed by 433
Abstract
As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the [...] Read more.
As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the paralysis of GNSS function in severe cases. In recent years, Low Earth Orbit (LEO) satellites have been highly emphasized for their unique advantages in GNSS interference detection, and related commercial and academic activities have increased rapidly. In this context, based on the signal-to-noise ratio (SNR) and radio-frequency interference (RFI) measurements data from COSMIC-2 satellites, this paper explores a method of predicting RFI measurements using SNR correlation variations in different GNSS signal channels for application to the detection and localization of civil terrestrial GNSS interference signals. Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. To this end, a CNN-BiLSTM-Attention model combining a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and attention mechanism is proposed in this paper, and the model takes the multi-channel SNR time series of the GNSS as the input and outputs the maximum measured value of RFI in the multi-channels. The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R2) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. Since the correlation changes in the SNR can be processed to decouple the signal strength, this model is also suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP, GRACE, and Spire) for which no RFI measurements have yet been made. Full article
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Figure 1
<p>The different signal transmission paths between the RFI source, the GNSS satellites, and the GNSS RO satellites (not to scale).</p>
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<p>SNR, S4 scintillation index, elevation angle, and RFI measurements for the POD 01 antenna of C2E1 satellite near 1:35 UTC on 1 January 2023: (<b>a</b>) SNR sequence of CA code L1 band for different channels. (<b>b</b>) S4 scintillation index. (<b>c</b>) Elevation angle of the LEO-GPS link. (<b>d</b>) Maximum value of RFI measurements in multiple channels.</p>
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<p>Changes in SNR, S4 scintillation index, and RFI measurements of the C2E1 satellite POD 01 antenna when scintillation occurs on 1 January 2023 near 1:10 UTC. (<b>a</b>) SNR sequence of CA code L1 band for different channels. (<b>b</b>) S4 scintillation index. (<b>c</b>) Maximum value of RFI measurements in multiple channels.</p>
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<p>Results of the interference detection algorithm: (<b>a</b>) The result of band-pass filtering and normalization of the multi-channel SNR sequence in <a href="#sensors-24-07745-f002" class="html-fig">Figure 2</a>a. (<b>b</b>) The calculated cross-correlation sequence after moving window normalization and filtering, as well as interference, can be detected by setting a threshold (set to 0.01 in this example).</p>
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<p>Spatial distribution of the orbits of GPS and COSMIC-2 satellites during the SNR duration in <a href="#sensors-24-07745-f002" class="html-fig">Figure 2</a>a.</p>
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<p>Calculation results of the RFI measurement sequence and cross-correlation sequence output by the C2E1 satellite 01 antenna on January 1, 2023 UTC. The two sequences also show a certain degree of correlation over the day: (<b>a</b>) RFI measurement sequence; (<b>b</b>) Calculated normalized cross-correlation sequence after band-pass filtering.</p>
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<p>Basic structure of a CNN model.</p>
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<p>LSTM model schematic.</p>
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<p>Schematic diagram of the BiLSTM model.</p>
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<p>Basic structure of the CNN-BiLSTM-Attention model used in this paper.</p>
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<p>Flowchart of the algorithm.</p>
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<p>Predicted RFI measurements and dRFI for the training set on the COSMIC-2 C2E1 satellite for the CNN-BiLSTM-Attention model. The dashed line in the figure indicates the interference threshold y = ±0.001, with the time resolution downsampled to 3 h: (<b>a</b>) the blue line is the true value of the RFI measurement, and the red line is the prediction result; (<b>b</b>) dRFI.</p>
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<p>Predicted RFI measurements and dRFI for the training set on the COSMIC-2 C2E1 satellite for the BiLSTM-Attention model. The dashed line in the figure indicates the interference threshold y = ±0.001, with the time resolution downsampled to 3 h: (<b>a</b>) the blue line is the true value of the RFI measurement, and the red line is the prediction result; (<b>b</b>) dRFI.</p>
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<p>Predicted RFI measurements and dRFI for the training set on the COSMIC-2 C2E1 satellite for the LSTM model. The dashed line in the figure indicates the interference threshold y = ±0.001, with the time resolution downsampled to 3 h: (<b>a</b>) the blue line is the true value of the RFI measurement, and the red line is the prediction result; (<b>b</b>) dRFI.</p>
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<p>Predicted RFI measurements and dRFI for the test set on the COSMIC-2 C2E1 satellite for the CNN-BiLSTM-Attention model. The dashed line in the figure indicates the interference threshold y = ±0.001, with the time resolution downsampled to 3 h: (<b>a</b>) the blue line is the true value of the RFI measurement, and the red line is the prediction result; (<b>b</b>) dRFI.</p>
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<p>Predicted RFI measurements and dRFI for the test set on the COSMIC-2 C2E1 satellite for the BiLSTM-Attention model. The dashed line in the figure indicates the interference threshold y = ±0.001, with the time resolution downsampled to 3 h: (<b>a</b>) the blue line is the true value of the RFI measurement, and the red line is the prediction result; (<b>b</b>) dRFI.</p>
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<p>Predicted RFI measurements and dRFI for the test set on the COSMIC-2 C2E1 satellite for the LSTM model. The dashed line in the figure indicates the interference threshold y = ±0.001, with the time resolution downsampled to 3 h: (<b>a</b>) the blue line is the true value of the RFI measurement, and the red line is the prediction result; (<b>b</b>) dRFI.</p>
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<p>Global RFI situation map of the six COSMIC-2 satellites’ 01 and 02 antenna superposition cases using the four methods mentioned in this paper and the actual measured values: (<b>a</b>) real measured values, and the three green dotted rectangles inside the marker are the primary sources of prediction error for various algorithms (<b>b</b>) CNN-BiLSTM-Attention, (<b>c</b>) BiLSTM-Attention, (<b>d</b>) LSTM, (<b>e</b>) normalized cross-correlation method with band-pass filtering.</p>
Full article ">Figure 18 Cont.
<p>Global RFI situation map of the six COSMIC-2 satellites’ 01 and 02 antenna superposition cases using the four methods mentioned in this paper and the actual measured values: (<b>a</b>) real measured values, and the three green dotted rectangles inside the marker are the primary sources of prediction error for various algorithms (<b>b</b>) CNN-BiLSTM-Attention, (<b>c</b>) BiLSTM-Attention, (<b>d</b>) LSTM, (<b>e</b>) normalized cross-correlation method with band-pass filtering.</p>
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46 pages, 19002 KiB  
Article
3Cat-8 Mission: A 6-Unit CubeSat for Ionospheric Multisensing and Technology Demonstration Test-Bed
by Luis Contreras-Benito, Ksenia Osipova, Jeimmy Nataly Buitrago-Leiva, Guillem Gracia-Sola, Francesco Coppa, Pau Climent-Salazar, Paula Sopena-Coello, Diego Garcín, Juan Ramos-Castro and Adriano Camps
Remote Sens. 2024, 16(22), 4199; https://doi.org/10.3390/rs16224199 - 11 Nov 2024
Viewed by 988
Abstract
This paper presents the mission analysis of 3Cat-8, a 6-Unit CubeSat mission being developed by the UPC NanoSat Lab for ionospheric research. The primary objective of the mission is to monitor the ionospheric scintillation of the aurora, and to perform several technological [...] Read more.
This paper presents the mission analysis of 3Cat-8, a 6-Unit CubeSat mission being developed by the UPC NanoSat Lab for ionospheric research. The primary objective of the mission is to monitor the ionospheric scintillation of the aurora, and to perform several technological demonstrations. The satellite incorporates several novel systems, including a deployable Fresnel Zone Plate Antenna (FZPA), an integrated PocketQube deployer, a dual-receiver GNSS board for radio occultation and reflectometry experiments, and a polarimetric multi-spectral imager for auroral emission observations. The mission design, the suite of payloads, and the concept of operations are described in detail. This paper discusses the current development status of 3Cat-8, with several subsystems already developed and others in the final design phase. It is expected that the data gathered by 3Cat-8 will contribute to a better understanding of ionospheric effects on radio wave propagation and demonstrate the feasibility of compact remote sensors in a CubeSat platform. Full article
(This article belongs to the Special Issue Advances in CubeSats for Earth Observation)
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Figure 1
<p><sup>3</sup>Cat-8 overall mission timeline.</p>
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<p><sup>3</sup>Cat-8 Mission Launch and Early Orbit Phase.</p>
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<p>General view of the <sup>3</sup>Cat-8 satellite.</p>
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<p><sup>3</sup>Cat-8 in fully deployed configuration.</p>
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<p><sup>Po</sup>Cat deployment and early operations phase.</p>
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<p><sup>3</sup>Cat-8 Mission Operational Phase.</p>
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<p>Exploded view of the <sup>3</sup>Cat-8 satellite’s subsystems.</p>
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<p><sup>3</sup>Cat-8 data connection architecture.</p>
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<p>Modeled camera quantum efficiency compared to auroral emissions.</p>
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<p>Flight model of the SUSIE imager.</p>
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<p>OBDHand OBC PCBs of the C3SatP’s qualification and flight models.</p>
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<p>Schematic view of the GNSS-R/RO payload architecture.</p>
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<p>SiLeX down-looking antenna, which includes the L1 patch array, and the stacked S-band (<math display="inline"><semantics> <mrow> <mn>2</mn> <mo>×</mo> </mrow> </semantics></math> single patch) and X-band (<math display="inline"><semantics> <mrow> <mn>2</mn> <mo>×</mo> <mn>2</mn> </mrow> </semantics></math> array) antennas.</p>
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<p>FZPA pathfinders: (<b>left</b>) 3U model after a deployment test; (<b>center</b>) Single-crown FZPA in the anechoic chamber; (<b>right</b>) Scale model of the FZPA membrane for 2.45 GHz.</p>
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<p>Radiation pattern at L1, for a full-crown plus partial segments FZPA, from CST simulation (<b>left</b>), and anechoic chamber tests (<b>right</b>).</p>
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<p>Deployment sequence of the FZPA from the <sup>3</sup>Cat-8 spacecraft.</p>
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<p>CuPID models for two and three PocketQube units.</p>
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<p><sup>3</sup>Cat-8 power distribution block diagram.</p>
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<p><sup>3</sup>Cat-8 OBC application layer element overview.</p>
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<p><sup>3</sup> Cat-8 structure with a 3P version of CuPID (see <a href="#remotesensing-16-04199-f017" class="html-fig">Figure 17</a>a).</p>
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<p>Block diagram of the spacecraft ADCS.</p>
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<p>Attitude control actuators un <sup>3</sup>Cat-8.</p>
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<p>Link budget of the different communication subsystems.</p>
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<p>Pass duration distribution for four communication lines over GS in Montsec (over 1 year).</p>
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<p>Temperature distribution of <sup>3</sup>Cat-8 during Post-Standby in the coldest (<b>left</b>) and hottest (<b>right</b>) orbit areas.</p>
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<p><sup>3</sup>Cat-8 subsystem average temperature variations during Post-Standby.</p>
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<p>Temperature distribution of <sup>3</sup>Cat-8 in Nominal Mode in the coldest orbit area.</p>
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<p>Tmperature distribution of <sup>3</sup>Cat-8 in Nominal Mode in the hottest orbit area.</p>
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<p><sup>3</sup>Cat-8 subsystem average temperatures variation in Nominal Mode.</p>
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<p><sup>3</sup>Cat-8 simplified geometry.</p>
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<p>Modal shapes for modes with the greatest Effective Mass Ratio.</p>
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<p>Equivalent stress for the Random Vibrations simulation at GEVS levels.</p>
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<p>Angular body rates and actuator torques during complete detumbling operation.</p>
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<p>Total attitude error, disturbance torques, and reaction wheel angular rate during hybrid actuation nadir pointing with momentum unloading.</p>
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<p>Altitude change of <sup>3</sup>Cat-8 in Fully Deployed Configuration.</p>
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<p>Altitude change of <sup>3</sup>Cat-8 in Partially Deployed Configuration.</p>
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22 pages, 5856 KiB  
Article
Assessment of FY-3E GNOS II Radio Occultation Data Using an Improved Three-Cornered Hat Method
by Jiahui Liang, Congliang Liu, Xi Wang, Xiangguang Meng, Yueqiang Sun, Mi Liao, Xiuqing Hu, Wenqiang Lu, Jinsong Wang, Peng Zhang, Guanglin Yang, Na Xu, Weihua Bai, Qifei Du, Peng Hu, Guangyuan Tan, Xianyi Wang, Junming Xia, Feixiong Huang, Cong Yin, Yuerong Cai and Peixian Liadd Show full author list remove Hide full author list
Remote Sens. 2024, 16(20), 3808; https://doi.org/10.3390/rs16203808 - 13 Oct 2024
Viewed by 1068
Abstract
The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a [...] Read more.
The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a novel spatial–temporal sampling correction method to mitigate the sampling errors associated with both RO–RS and RS–model pairs. We analyze the 3CH processing chain with this new correction method in comparison to traditional approaches, utilizing Fengyun-3E (FY-3E) GNSS Occultation Sounder II (GNOS II) RO data, atmospheric models, and RS datasets from the Hailar and Xisha stations. Overall, the results demonstrate that the improved 3CH method performs better in terms of spatial–temporal sampling errors and the variances of atmospheric parameters, including refractivity, temperature, and specific humidity. Subsequently, we assess the error variances of the FY-3E GNOS II RO, RS and model atmospheric parameters in China, in particular the northern China and southern China regions, based on large ensemble datasets using the improved 3CH data processing chain. The results indicate that the FY-3E GNOS II BeiDou navigation satellite system (BDS) RO and Global Positioning System (GPS) RO show good consistency, with the average error variances of refractivity, temperature, and specific humidity being less than 1.12%2, 0.13%2, and 700%2, respectively. A comparison of the datasets from northern and southern China reveals that the error variances for refractivity are smaller in northern China, while temperature and specific humidity exhibit smaller error variances in southern China, which is attributable to the differing climatic conditions. Full article
(This article belongs to the Special Issue International GNSS Service Validation, Application and Calibration)
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Figure 1

Figure 1
<p>The spatial distribution of radiosonde stations. Diamond symbols represent radiosonde stations, and the color of the symbols indicates the number of occultations co-located with each station. The two square symbols represent the northernmost Hailar station (49.25°N, 119.70°E) and the southernmost Xisha station (16.83°N, 112.33°E), respectively, and the solid blue line is the north–south dividing line.</p>
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<p>The number of FY-3E GNOS II BDS and GPS occultations co-located with each radiosonde station: blue bars indicate BDS data, and green bars represent GPS data. The dataset spans from 1 September 2022 to 31 August 2023.</p>
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<p>Statistical comparison of refractivity, temperature, and specific humidity profiles of (<b>a</b>–<b>c</b>) RO, (<b>d</b>–<b>f</b>) RS, and (<b>g</b>–<b>i</b>) ERA5 located at the Xisha and Hailar radiosonde stations. The ensemble of data span from 1 September 2022 to 31 August 2023 and the statistics include the mean (solid line) and the standard deviation (“std”; dashed line), respectively.</p>
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<p>Refractivity error variances (percentage square) of (<b>a</b>–<b>c</b>) RO, (<b>d</b>–<b>f</b>) RS, and (<b>g</b>–<b>i</b>) ERA5 datasets at the Hailar station for different sampling correction schemes.</p>
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<p>Refractivity error variances (percentage square) of (<b>a</b>–<b>c</b>) RO, (<b>d</b>–<b>f</b>) RS, and (<b>g</b>–<b>i</b>) ERA5 datasets at the Xisha station for different sampling correction schemes.</p>
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<p>Temperature error variances (percentage square) of (<b>a</b>–<b>c</b>) RO, (<b>d</b>–<b>f</b>) RS, and (<b>g</b>–<b>i</b>) ERA5 datasets at the Hailar station for different sampling correction schemes.</p>
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<p>Temperature error variances (percentage square) of (<b>a</b>–<b>c</b>) RO, (<b>d</b>–<b>f</b>) RS, and (<b>g</b>–<b>i</b>) ERA5 datasets at the Xisha station for different sampling correction schemes.</p>
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<p>Specific humidity error variances (percentage square) of (<b>a</b>–<b>c</b>) RO, (<b>d</b>–<b>f</b>) RS, and (<b>g</b>–<b>i</b>) ERA5 datasets at the Hailar station for different sampling correction schemes.</p>
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<p>Specific humidity error variances (percentage square) of (<b>a</b>–<b>c</b>) RO, (<b>d</b>–<b>f</b>) RS, and (<b>g</b>–<b>i</b>) ERA5 datasets at the Xisha station for different sampling correction schemes.</p>
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<p>Estimated error variances of refractivity, temperature, and specific humidity of (<b>a</b>–<b>c</b>) RO, (<b>d</b>–<b>f</b>) RS, and (<b>g</b>–<b>i</b>) ERA5 datasets in China (percentage squared).</p>
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<p>Estimated error variances of refractivity, temperature, and specific humidity of (<b>a</b>–<b>c</b>) RO, (<b>d</b>–<b>f</b>) RS, and (<b>g</b>–<b>i</b>) ERA5 datasets in southern China (percentage squared).</p>
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<p>Estimated error variances of refractivity, temperature, and specific humidity of (<b>a</b>–<b>c</b>) RO, (<b>d</b>–<b>f</b>) RS, and (<b>g</b>–<b>i</b>) ERA5 datasets in northern China (percentage squared).</p>
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21 pages, 19354 KiB  
Article
Assessment of Commercial GNSS Radio Occultation Performance from PlanetiQ Mission
by Mohamed Zhran, Ashraf Mousa, Yu Wang, Fahdah Falah Ben Hasher and Shuanggen Jin
Remote Sens. 2024, 16(17), 3339; https://doi.org/10.3390/rs16173339 - 8 Sep 2024
Viewed by 1094
Abstract
Global Navigation Satellite System (GNSS) radio occultation (RO) provides valuable 3-D atmospheric profiles with all-weather, all the time and high accuracy. However, GNSS RO mission data are still limited for global coverage. Currently, more commercial GNSS radio occultation missions are being launched, e.g., [...] Read more.
Global Navigation Satellite System (GNSS) radio occultation (RO) provides valuable 3-D atmospheric profiles with all-weather, all the time and high accuracy. However, GNSS RO mission data are still limited for global coverage. Currently, more commercial GNSS radio occultation missions are being launched, e.g., PlanetiQ. In this study, we examine the commercial GNSS RO PlanetiQ mission performance in comparison to KOMPSAT-5 and PAZ, including the coverage, SNR, and penetration depth. Additionally, the quality of PlanetiQ RO refractivity profiles is assessed by comparing with the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) data in October 2023. Our results ensure that the capability of PlanetiQ to track signals from any GNSS satellite is larger than the ability of KOMPSAT-5 and PAZ. The mean L1 SNR for PlanetiQ is significantly larger than that of KOMPSAT-5 and PAZ. Thus, PlanetiQ performs better in sounding the deeper troposphere. Furthermore, PlanetiQ’s average penetration height ranges from 0.16 to 0.49 km in all latitudinal bands over water. Generally, the refractivity profiles from all three missions exhibit a small bias when compared to ERA5-derived refractivity and typically remain below 1% above 800 hPa. Full article
(This article belongs to the Special Issue BDS/GNSS for Earth Observation: Part II)
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Figure 1
<p>Occultation events’ global distribution from (<b>a</b>) KOMPSAT-5, including 188 events, and (<b>b</b>) from PAZ including 193 events both from GPS satellites on 1 October 2023.</p>
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<p>PlanetiQ occultation events’ global distribution, including 321 events from GPS (blue), 293 events from GALILEO (red), 283 events from GLONASS (green), and 212 events from BDS (orange), on 1 October 2023.</p>
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<p>Monthly events number from KOMPSAT-5, PAZ, and PlanetiQ (<b>a</b>) and rising and setting profile percentages for PlanetiQ with occultation signals coming from GPS, Galileo, GLONASS, and BDS (<b>b</b>).</p>
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<p>Block diagram for the main processing used in this study.</p>
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<p>L1 SNR latitudinal distribution during October 2023 for (<b>a</b>) KOMPSAT-5 and (<b>b</b>) PAZ.</p>
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<p>L1 SNR latitudinal distribution for PlanetiQ during October 2023 for (<b>a</b>) GPS, (<b>b</b>) Galileo, (<b>c</b>) GLONASS, and (<b>d</b>) BDS.</p>
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<p>The normalized L1 SNR frequency distribution sample numbers (%) for GPS (in blue line) signals on (<b>a</b>) KOMPSAT-5 and (<b>b</b>) PAZ during October 2023. The total number of observations from each GPS satellite is mentioned in the figures.</p>
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<p>The normalized L1 SNR frequency distribution sample numbers (%) for GPS (blue line), Galileo (red line), GLONASS (green line), and BDS (orange line) signals during October 2023. The total number of observations from each GNSS satellite is mentioned in the figures.</p>
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<p>The mean daily SNR (<math display="inline"><semantics> <mrow> <mi>v</mi> <mo>/</mo> <mi>v</mi> </mrow> </semantics></math>) of PlanetiQ in October 2023.</p>
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<p>Penetration depth percentage of PlanetiQ, KOMPSAT-5, and PAZ in October 2023.</p>
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<p>(<b>a</b>) The cumulative RO percentage with a penetration depth of PlanetiQ (red), KOMPSAT-5 (blue), and PAZ (black), over water in October 2023 and (<b>b</b>) the corresponding numbers of observations from the surface to 13 km altitude for PlanetiQ, KOMPSAT-5, and PAZ over water in October 2023.</p>
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<p>The percentage of data below 1 km for PlanetiQ (red), KOMPSAT-5 (blue), and PAZ (black) at different latitude zones over water in October 2023.</p>
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<p>(<b>a</b>) Mean fractional bias of refractivity and(<b>b</b>) Mean fractional RMSE of refractivity for KOMPSAT-5, PAZ, and PlanetiQ. (<b>c</b>) Mean fractional bias of refractivity and (<b>d</b>) Mean fractional RMSE of refractivity for PlanetiQ from different GNSS satellites.</p>
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<p>Cross-section of the mean fractional bias in refractivity for (<b>a</b>) KOMPSAT-5, (<b>b</b>) PAZ, and (<b>c</b>) PlanetiQ. Cross-sections of mean fractional RMSE of refractivity for (<b>d</b>) KOMPSAT-5, (<b>e</b>) PAZ, and (<b>f</b>) PlanetiQ.</p>
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<p>Cross-section of the mean fractional (<b>a</b>–<b>d</b>) bias and (<b>e</b>–<b>h</b>) RMSE in refractivity for PlanetiQ from different GNSS satellites. Columns from left to right are profiles from GPS, Galileo, BDS, and GLONASS, respectively.</p>
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<p>(<b>a</b>) Mean bias of dry temperature and (<b>b</b>) Mean RMSE of dry temperature for KOMPSAT-5, PAZ, and PlanetiQ. (<b>c</b>) Mean bias of dry temperature and (<b>d</b>) Mean RMSE of dry temperature for PlanetiQ from different GNSS satellites.</p>
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<p>Cross-section of the mean bias of dry temperature for (<b>a</b>) KOMPSAT-5, (<b>b</b>) PAZ, and (<b>c</b>) PlanetiQ. Cross-sections of mean RMSE of dry temperature for (<b>d</b>) KOMPSAT-5, (<b>e</b>) PAZ, and (<b>f</b>) PlanetiQ.</p>
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<p>Cross-section of the mean (<b>a</b>–<b>d</b>) bias and (<b>e</b>–<b>h</b>) RMSE of dry temperature for PlanetiQ from different GNSS satellites. Columns from left to right are profiles from GPS, Galileo, BDS, and GLONASS, respectively.</p>
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18 pages, 6083 KiB  
Article
First Detections of Ionospheric Plasma Density Irregularities from GOES Geostationary GPS Observations during Geomagnetic Storms
by Iurii Cherniak, Irina Zakharenkova, Scott Gleason and Douglas Hunt
Atmosphere 2024, 15(9), 1065; https://doi.org/10.3390/atmos15091065 - 3 Sep 2024
Viewed by 851
Abstract
In this study, we present the first results of detecting ionospheric irregularities using non-typical GPS observations recorded onboard the Geostationary Operational Environmental Satellites (GOES) mission operating at ~35,800 km altitude. Sitting above the GPS constellation, GOES can track GPS signals only from GPS [...] Read more.
In this study, we present the first results of detecting ionospheric irregularities using non-typical GPS observations recorded onboard the Geostationary Operational Environmental Satellites (GOES) mission operating at ~35,800 km altitude. Sitting above the GPS constellation, GOES can track GPS signals only from GPS transmitters on the opposite side of the Earth in a rather unique geometry. Although GPS receivers onboard GOES are primarily designed for navigation and were not configured for ionospheric soundings, these GPS measurements along links that traverse the Earth’s ionosphere can be used to retrieve information about ionospheric electron density. Using the radio occultation (RO) technique applied to GPS measurements from the GOES–16, we analyzed variations in the ionospheric total electron content (TEC) on the links between the GPS transmitter and geostationary GOES GPS receiver. For case-studies of major geomagnetic storms that occurred in September 2017 and August 2018, we detected and analyzed the signatures of storm-induced ionospheric irregularities in novel and promising geostationary GOES GPS observations. We demonstrated that the presence of ionospheric irregularities near the GOES GPS RO sounding field of view during geomagnetic disturbances was confirmed by ground-based GNSS observations. The use of RO observations from geostationary orbit provides new opportunities for monitoring ionospheric irregularities and ionospheric density. Full article
(This article belongs to the Special Issue Ionospheric Irregularity)
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<p>The sketch view of the GOES–GPS and LEO–GPS radio occultation experiment configurations.</p>
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<p>Variations in IMF Bz and auroral electrojet (AE) and SYM-H indices during 6–8 September 2017.</p>
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<p>The daily MLT-MLAT ROTI maps for the Northern Hemisphere for (<b>a</b>–<b>c</b>) 6–8 September 2017. The maps cover 50–90° N MLAT with 10° latitude circles; the magnetic local noon/midnight is at the top/bottom and dusk/dawn. The blue color indicates no or very weak ionospheric irregularities, whereas the red color shows severe ionospheric irregularity occurrence.</p>
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<p>The rate of the TEC from the GOES GPS RO observations as a function of altitude (right) and ground-based ROTI maps corresponding to these occultation events (left) for (<b>a</b>–<b>d</b>) several successful occultation events for pre-storm conditions on 7 September 2017. The gray shading on the maps shows the nighttime, and the thick black line shows the magnetic equator. The maps also show the position of GOES-16 at 72.5° W (large black dot), GOES RO tangent point projections (magenta dots), and line-of-sight link in the direction between the GEOS GPS receiver and GPS transmitter (black line).</p>
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<p>The same as <a href="#atmosphere-15-01065-f004" class="html-fig">Figure 4</a> but for the main phase of the storm on 8 September 2017 (<b>a</b>–<b>c</b>).</p>
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<p>Variations in IMF Bz and auroral electrojet (AE) and SYM-H indices during 25–27 August 2018.</p>
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<p>The daily MLT-MLAT ROTI maps for the Northern Hemisphere for (<b>a</b>–<b>c</b>) 25–27 August 2018. The maps cover 50–90° N MLAT with 10° latitude circles; the magnetic local noon/midnight is at the top/bottom and dusk/dawn.</p>
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<p>The rate of the TEC from the GOES GPS RO observations as a function of altitude (right) and the ground-based ROTI maps corresponding to these occultation events (left) for (<b>a</b>,<b>b</b>) several successful occultation events for the pre-storm conditions on 25 August 2018. The gray shading on the maps shows the nighttime, and the thick black line shows the magnetic equator. The maps also show the position of GOES-16 (large black dot), the GOES RO tangent point projections (magenta dots), and the line-of-sight link in the direction between the GEOS GPS receiver and GPS transmitter (black line).</p>
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<p>The same as <a href="#atmosphere-15-01065-f008" class="html-fig">Figure 8</a> but for (<b>a</b>–<b>c</b>) several occultation events for the conditions after the storm onset on 25 August 2018.</p>
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<p>(<b>a</b>–<b>c</b>) The same as <a href="#atmosphere-15-01065-f008" class="html-fig">Figure 8</a> but for 26 August 2018 (part 1).</p>
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<p>(<b>a</b>–<b>c</b>) The same as <a href="#atmosphere-15-01065-f008" class="html-fig">Figure 8</a> but for 26 August 2018 (part 2).</p>
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18 pages, 5101 KiB  
Article
Atmospheric Water Vapor Variability over Houston: Continuous GNSS Tomography in the Year of Hurricane Harvey (2017)
by Pedro Mateus, João Catalão, Rui Fernandes and Pedro M. A. Miranda
Remote Sens. 2024, 16(17), 3205; https://doi.org/10.3390/rs16173205 - 30 Aug 2024
Viewed by 672
Abstract
This study evaluates the capability of an unconstrained tomographic algorithm to capture 3D water vapor density variability throughout 2017 in Houston, U.S. The algorithm relies solely on Global Navigation Satellite System (GNSS) observations and does not require an initial guess or other specific [...] Read more.
This study evaluates the capability of an unconstrained tomographic algorithm to capture 3D water vapor density variability throughout 2017 in Houston, U.S. The algorithm relies solely on Global Navigation Satellite System (GNSS) observations and does not require an initial guess or other specific constraints regarding water vapor density variability within the tomographic domain. The test domain, featuring 9 km horizontal, 500 m vertical, and 30 min temporal resolutions, yielded remarkable results when compared to data retrieved from the ECMWF Reanalysis v5 (ERA5), regional Weather Research and Forecasting Model (WRF) data, and GNSS-Radio Occultation (RO). For the first time, a time series of Precipitable Water Vapor maps derived from the Interferometric Synthetic Aperture Radar (InSAR) technique was used to validate the spatially integrated water vapor computed by GNSS tomography. Tomographic results clearly indicate the passage of Hurricane Harvey, with integrated water vapor peaking at 60 kg/m2 and increased humidity at altitudes up to 7.5 km. Our findings suggest that GNSS tomography holds promise as a reliable source of atmospheric water vapor data for various applications. Future enhancements may arise from denser and multi-constellation networks. Full article
(This article belongs to the Section Environmental Remote Sensing)
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<p>GNSS tomographic 2D grid over Houston city (TX, USA). Blue triangles display the location of GPS stations used in the tomographic process, and red triangles are the stations used to obtain the absolute and calibrated InSAR PWV maps used to evaluate the tomographic solution spatial variability. The rectangles display the two footprints of Sentinel-1 used (ascending and descending orbits). The background colormap represents the elevation. The red dashed line indicates the vertical cross-section location used further on. The black line is the best track (determined by the National Hurricane Center) of Hurricane Harvey from 26 August to 29 August 2017 (hours in UTC).</p>
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<p>TOMO-, WRF-, and ERA5-PWV derived values at the GNSS station’s location versus the PWV estimated from GNSS observations. The black line corresponds to the perfect fit. Legend indicates the correlation and the RMSE in kg/m<sup>2</sup> (within []).</p>
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<p>Temporal series of the average PWV over the topography domain. (<b>a</b>) Averaged WRF-, ERA5-, and TOMO-PWV; the green stars represent the PWV derived from RO. The vertical dashed lines delimit the month of August; (<b>b</b>) zoom over August. Date in month/day format.</p>
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<p>Vertical profiles of TOMO, ERA5, and WRF assessed against 30 available GNSS-RO observations: (<b>a</b>) average water vapor density; (<b>b</b>) RMSE; (<b>c</b>) BIAS; and (<b>d</b>) temporal correlation coefficient.</p>
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<p>PWV maps for 29 August around 00:30 UTC (at the end of Hurricane Harvey’s passage over the Houston area): (<b>a</b>) estimated using the interferometric phase (ascending orbit); (<b>b</b>) simulated by WRF model; (<b>c</b>) derived from ERA5 reanalysis; and (<b>d</b>) obtained via GNSS tomography (area corresponding to the TOMO footprint, black rectangle in (<b>a</b>–<b>c</b>)). The dotted rectangle corresponds to the InSAR footprint in ascending orbit.</p>
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<p>TOMO and models temporal evaluation using InSAR PWV maps as the “ground truth”. (<b>a</b>) mean RMSE; (<b>b</b>) mean BIAS; and (<b>c</b>) mean correlation coefficient. Before applying the statistical metrics, a spatial bilinear resampling method (that uses the distance-weighted average of the four nearest pixel values to estimate a new pixel value) was performed to attain the TOMO spatial resolution (9 km).</p>
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<p>Spatial evaluation using InSAR PWV maps. (<b>a</b>) Spatial RMSE for WRF; (<b>b</b>) for ERA5; and (<b>c</b>) for TOMO. (<b>d</b>) Skill score (SS) taking InSAR as “ground true”, WRF as first model, and TOMO as second; and (<b>e</b>) the same as before, but with ERA5 as the first model. The mean correlation and RMSE in kg/m<sup>2</sup> are within [].</p>
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<p>Hovmöller diagram of water vapor density profiles every 30 min. (<b>a</b>) WRF; (<b>b</b>) ERA5; and (<b>c</b>) TOMO solution. Averaged over the tomographic domain.</p>
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<p>An example of a vertical cross-section of the tomographic inversion for 22 August at 00:00 UTC (<b>a</b>–<b>c</b>), corresponding to the beginning of the hurricane’s passage over Houston, and for 28th at 00:00 UTC (<b>d</b>–<b>f</b>) during Harvey near peak intensity over Houston. The cross-section corresponds to the southeast–northwest red dashed line in <a href="#remotesensing-16-03205-f001" class="html-fig">Figure 1</a>. (<b>a</b>,<b>d</b>) WRF; (<b>b</b>,<b>e</b>) ERA5; and (<b>c</b>,<b>f</b>) TOMO solution.</p>
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19 pages, 3836 KiB  
Article
Seasonal–Longitudinal Variability of Equatorial Plasma Bubbles Observed by FormoSat-7/Constellation Observing System for Meteorology Ionosphere and Climate II and Relevant to the Rayleigh–Taylor Instability
by Lung-Chih Tsai, Shin-Yi Su, Harald Schuh, Mohamad Mahdi Alizadeh and Jens Wickert
Remote Sens. 2024, 16(13), 2310; https://doi.org/10.3390/rs16132310 - 25 Jun 2024
Viewed by 823
Abstract
The FormoSat-7/Constellation Observing System for Meteorology, Ionosphere, and Climate II (FS7/COSMIC2) program has acquired over three hundred thousand equatorial plasma bubble (EPB) observations from 2019 to 2023 in the equatorial and near low-latitude regions. The huge FS7/COSMIC2 database offers an opportunity to perform [...] Read more.
The FormoSat-7/Constellation Observing System for Meteorology, Ionosphere, and Climate II (FS7/COSMIC2) program has acquired over three hundred thousand equatorial plasma bubble (EPB) observations from 2019 to 2023 in the equatorial and near low-latitude regions. The huge FS7/COSMIC2 database offers an opportunity to perform statistical inspections of the proposed hypothesis on seasonal versus longitudinal variability of EPB occurrence rates relevant to the Rayleigh–Taylor (R-T) instability. The detected EPBs are distributed along the magnetic equator with a half width of ~20° in geomagnetic latitude. The obtained EPB occurrence rates in local time (LT) rose rapidly after sunsets, and could be deconstructed into two overlapped Gaussian distributions resembling a major peak around 23:00 LT and a minor peak around 20:20 LT. The two groups of Gaussian-distributed EPBs in LT were classified as first- and second-type EPBs, which could be caused by different mechanisms such as sporadic E (Es) instabilities and pre-reversal enhancement (PRE) fields. The obtained seasonal–longitudinal distributions of both types of EPBs presented two diffused traces of high occurrence rates, which happened near the days and longitudes when and where the angle between the two lines of magnetic declination and solar terminator at the magnetic equator was equal to zero. Finally, we analyzed the climatological and seasonal–longitudinal variability of EPB occurrences and compared the results with the physical R-T instability model controlled by Es instabilities and/or PRE fields. Full article
(This article belongs to the Special Issue BDS/GNSS for Earth Observation: Part II)
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<p>An example of an FS7/COSMIC2 RO observation (obtained on 27 November 2023) identified as an EPB event. The limb-viewing and fluctuating SNR amplitude profiles at the occulting side are shown in black and blue for L1 and L2 bands, respectively, and the resulting complete S4 profiles are in cyan and brown. The retrieved Ne profile is shown in red. We note that an Es layer is also detected in this observed ionosphere.</p>
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<p>Six bimonthly geographical occurrence distributions of EPB and/or F-layer scintillation events from FS7/COSMIC2 observations in 2023. The red and white curves are drawn to represent the magnetic equator and the ± 20° geomagnetic latitudes, respectively. The coded colors represent the scintillation occurrence rates from zero to twenty-five percent within every 5° by 5° in the geographic bin. The white bins are not for reference and indicate that less than twenty RO observations are located within each bin area. The dates of EPB observations for each occurrence map are labeled above each subfigure.</p>
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<p>From top to bottom, the six panels show the temporal profiles of daily averaged Kp in blue, ISS in red, and five zonal (Zones A, B, C, D, and E) EPB occurrence rates in red from mid-2019 to the end of 2023. The dark blue dots refer to the local time (right y-axes) of every identified EPB event in different low-latitude zones. The five right subpanels show zonal EPB occurrence rates as a function of local time in blue lines. For each panel, the yellow background denotes the months from April to September annually, and the white background denotes the months from October to March.</p>
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<p>Results of the statistical analyses applying two overlapped Gaussian distributions and the least-squares fitting technique to the five zonal EPB occurrence rates as a function of local time based on the FS7/COSMIC2 data from 2020 to 2023. The five black profiles represent the obtained zonal EPB occurrence rates as a function of local time. The green and red curves show the two deconstructed Gaussian distributions at means of −3.7 and −1 LT (i.e., 20:20 and 23:00 LT, respectively). The five gray profiles represent the residual zonal EPB occurrence rates after deconstruction.</p>
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<p>Yearly distribution maps of the first-type EPB occurrence rates as a function of DOY and geographic longitude based on the FS7/COSMIC2 RO observations from 2020 to 2023. The coded colors represent the EPB occurrence rates from zero to twenty percent within every bin of 5 days by 5° geographic longitudes. For reference, the five identified low-latitude zones are separated by black lines. The two red curves represent the days and longitudes when and where the angle <span class="html-italic">α</span> between the two lines of magnetic declination and solar terminator at the magnetic equator is equal to zero.</p>
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<p>Yearly distribution maps of the second-type EPB occurrence rates as a function of DOY and geographic longitude from 2020 to 2023. We note that the coded color scales are different to those in <a href="#remotesensing-16-02310-f005" class="html-fig">Figure 5</a>.</p>
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<p>The seasonal–longitudinal map of the angle <span class="html-italic">α</span> between the lines of magnetic declinations and solar terminators at the magnetic equator in 2023. The coded colors represent <span class="html-italic">α</span> values from −30 to 30 degrees. The two red curves represent the days and longitudes when and where <span class="html-italic">α</span> = 0°, and the pattern zoned by the four white curves represents the days and longitudes when and where <span class="html-italic">|α|</span> <math display="inline"><semantics> <mo>≤</mo> </semantics></math> 15° for reference.</p>
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<p>The seasonal–longitudinal map of the F-layer scale height <span class="html-italic">H</span> at the magnetic equator and at the local time of 20:20 LT in 2023. The coded colors represent <span class="html-italic">H</span> values from 40 to 80 km within every bin of one day by around 7.5° geographic longitudes.</p>
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<p>The seasonal–longitudinal map of relative R-T instability growth rates due to PRE electric fields in 2023. The coded colors represent relative <span class="html-italic">γ</span> ratios from 1 to 2.5 referring to the minimum <span class="html-italic">γ</span> within every bin of one day by around 7.5° geographic longitudes. For reference, the two red curves represent the days and longitudes when and where <span class="html-italic">α</span> = 0°, and the five identified low-latitude zones are separated by black lines.</p>
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<p>Two example monthly occurrence rate distributions of Es events observed from FS7/COSMIC2 in January (upper panel) and July (lower panel) of 2023. The coded color represents the Es occurrence rates from zero to eighty percent within every 5° by 5° in the geographic bin. The three typical regions enclosed by black lines were identified based on the occurrence statistics, and the curves of geomagnetic latitudes at 55°, 5°, −5°, and −55° are also labeled.</p>
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<p>Daily Es occurrence rates (red lines referring to the left y-axes) and local times of Es occurrences (blue dots referring to the right y-axes) in the three identified regions (i.e., Regions X, Y, and Z), observed from FS7/COSMIC2 during the days from mid-2019 to the end of 2023. In the three left panels, the yellow background denotes the “yellow” season from April to September annually, and the white background denotes the “white” season from October to March. The three right subpanels show the Es occurrence rates of the corresponding region as a function of local time in red and blue lines for “yellow” and “white” seasons, respectively.</p>
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<p>Daily Es occurrence rates (shown as a red line), averaged <span class="html-italic">S4L1max</span> values (shown as a black line), and averaged relative <span class="html-italic">N<sub>e</sub></span> values at the Es-layer peaks (<span class="html-italic">NmEs</span>, shown as a green line) during nighttime (19:00~5:00 LT) in Region X observed from FS7/COSMIC2 for the period from mid-2019 to the end of 2023.</p>
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<p>Four yearly nighttime seasonal–longitudinal distribution maps of the averaged <span class="html-italic">N<sub>e</sub></span> values at the E-layer peaks in Region X from 2020 to 2023. The coded colors represent the relative <span class="html-italic">N<sub>e</sub></span> values from 0 to 4 × 10<sup>5</sup> el/cm<sup>3</sup> within every bin of 5 days by 5° geographic longitudes. As in <a href="#remotesensing-16-02310-f005" class="html-fig">Figure 5</a> and <a href="#remotesensing-16-02310-f006" class="html-fig">Figure 6</a>, the two red curves represent the days and longitudes when and where <span class="html-italic">α</span> = 0°, and the five identified low-latitude zones are separated by black lines.</p>
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17 pages, 10217 KiB  
Article
Analysis of Ionospheric VTEC Retrieved from Multi-Instrument Observations
by Gurkan Oztan, Huseyin Duman, Salih Alcay, Sermet Ogutcu and Behlul Numan Ozdemir
Atmosphere 2024, 15(6), 697; https://doi.org/10.3390/atmos15060697 - 9 Jun 2024
Viewed by 1071
Abstract
This study examines the Vertical Total Electron Content (VTEC) estimation performance of multi-instruments on a global scale during different ionospheric conditions. For this purpose, GNSS-based VTEC data from Global Ionosphere Maps (GIMs), COSMIC (F7/C2)—Feng–Yun 3C (FY3C) radio occultation (RO) VTEC, SWARM–VTEC, and JASON–VTEC [...] Read more.
This study examines the Vertical Total Electron Content (VTEC) estimation performance of multi-instruments on a global scale during different ionospheric conditions. For this purpose, GNSS-based VTEC data from Global Ionosphere Maps (GIMs), COSMIC (F7/C2)—Feng–Yun 3C (FY3C) radio occultation (RO) VTEC, SWARM–VTEC, and JASON–VTEC were utilized. VTEC assessments were conducted on three distinct days: geomagnetic active (17 March 2015), solar active (22 December 2021), and quiet (11 December 2021). The VTEC values of COSMIC/FY3C RO, SWARM, and JASON were compared with data retrieved from GIMs. According to the results, COSMIC RO–VTEC is more consistent with GIM–VTEC on a quiet day (the mean of the differences is 4.38 TECU), while the mean of FY3C RO–GIM differences is 7.33 TECU on a geomagnetic active day. The range of VTEC differences between JASON and GIM is relatively smaller on a quiet day, and the mean of differences on active/quiet days is less than 6 TECU. Besides the daily comparison, long-term results (1 January–31 December 2015) were also analyzed by considering active and quiet periods. Results show that Root Mean Square Error (RMSE) values of COSMIC RO, FY3C RO, SWARM, and JASON are 5.02 TECU, 6.81 TECU, 16.25 TECU, and 5.53 TECU for the quiet period, and 5.21 TECU, 7.07 TECU, 17.48 TECU, and 5.90 TECU for the active period, respectively. The accuracy of each data source was affected by solar/geomagnetic activities. The deviation of SWARM–VTEC is relatively greater. The main reason for the significant differences in SWARM–GIM results is the atmospheric measurement range of SWARM satellites (460 km–20,200 km (SWARM A, C) and 520 km–20,200 km (SWARM B), which do not contain a significant part of the ionosphere in terms of VTEC estimation. Full article
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<p>kp, dst, and F10.7 index values on geomagnetic active, solar active, and quiet days.</p>
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<p>The used radio occultation VTEC points for active and quiet days.</p>
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<p>GIM–VTEC and Radio Occultation VTEC along with G24 Mission ID COO1/C006.</p>
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<p>GIM–VTEC and Radio Occultation VTEC along with G25 Mission ID COO1/C006.</p>
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<p>GIM–VTEC and Radio Occultation VTEC along with G28 Mission ID COO1/C006.</p>
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<p>GIM–VTEC and Radio Occultation VTEC along with G30 Mission ID COO1/C006.</p>
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<p>GIM–VTEC and Radio Occultation VTEC along with G31 Mission ID COO1/C006.</p>
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<p>GIM–VTEC and Radio Occultation VTEC along with G32 and G05 for quiet (11 December 2021) and solar active day (22 December 2021) days.</p>
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<p>The used FY3C radio occultation VTEC points for active day.</p>
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<p>GIM–VTEC and FY3C RO–VTEC along with G16, G24, G28, G30, G31.</p>
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<p>The used SWARM–VTEC points for active and quiet days.</p>
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<p>GIM–VTEC and SWARM–VTEC for geomagnetic active day along the GPS satellites.</p>
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<p>GIM–VTEC and SWARM–VTEC for quiet day along the GPS satellites.</p>
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<p>GIM–VTEC and SWARM–VTEC for solar active day along the GPS satellites.</p>
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<p>Daily ground track of JASON–2 on 17 March 2015 and JASON–3 on 11 December 2021 and 22 December 2021.</p>
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<p>JASON–VTEC and GIM–VTEC values for active and quiet days.</p>
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<p>Scatter plots of VTEC values retrieved from COSMIC–RO (<b>a</b>), FY3C–RO (<b>b</b>), SWARM (<b>c</b>), and JASON (<b>d</b>) with GIM–VTEC for the quiet period.</p>
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<p>Scatter plots of VTEC values retrieved from COSMIC–RO (<b>a</b>), FY3C–RO (<b>b</b>), SWARM (<b>c</b>), and JASON (<b>d</b>) with GIM–VTEC for the active period.</p>
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23 pages, 2990 KiB  
Article
A Novel Approach to Evaluate GNSS-RO Signal Receiver Performance in Terms of Ground-Based Atmospheric Occultation Simulation System
by Wei Li, Yueqiang Sun, Weihua Bai, Qifei Du, Xianyi Wang, Dongwei Wang, Congliang Liu, Fu Li, Shengyu Kang and Hongqing Song
Remote Sens. 2024, 16(1), 87; https://doi.org/10.3390/rs16010087 - 25 Dec 2023
Cited by 1 | Viewed by 1143
Abstract
The global navigation satellite system radio occultation (GNSS-RO) is an important means of space-based meteorological observation. It is necessary to test the Global Navigation Satellite System Occultation signal receiver on the ground before the deployment of space-based occultation detection systems. The current approach [...] Read more.
The global navigation satellite system radio occultation (GNSS-RO) is an important means of space-based meteorological observation. It is necessary to test the Global Navigation Satellite System Occultation signal receiver on the ground before the deployment of space-based occultation detection systems. The current approach of testing the GNSS signal receiver on the ground is mainly the mountaintop-based testing approach, which has problems such as high cost and large simulation error. In order to overcome the limitations of the mountaintop-based test approach, this paper proposes an accurate, repeatable, and controllable GNSS atmospheric occultation simulation system and builds a load performance evaluation approach based on the ground-based GNSS atmospheric occultation simulation system on the basis of it. The GNSS atmospheric occultation simulation system consists of the visualization and interaction module, the GNSS-RO simulation signal generation module, the GNSS-RO simulator module, the GNSS-RO signal receiver module, and the GNSS-RO inversion and evaluation module, combined with the preset atmospheric model to generate GNSS-RO simulation signals with a high degree of simulation, and comparing the atmospheric parameters of the inversion performance of the GNSS-RO signal receiver with the parameters of the preset atmospheric model to obtain the error data. The overall performance of the GNSS-RO signal receiver can be evaluated based on the error information. The novel approach to evaluate the GNSS-RO signal receiver performance proposed in this paper is validated by using the FY-3E (FengYun-3E) receiver qualification parts that have been verified in orbit, and the results confirm that the approach can meet the requirements of the GNSS-RO receiver performance test. This study shows that the novel approach to evaluate the GNSS-RO signal receiver performance in terms of the ground-based atmospheric occultation simulation system can efficiently and accurately be used to carry out the receiver test and provides an effective solution for the ground-based test of GNSS-RO signal receivers. Full article
(This article belongs to the Special Issue BDS/GNSS for Earth Observation: Part II)
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<p>Overall workflow.</p>
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<p>Visualization interface.</p>
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<p>GNSS-RO simulation signal generation module.</p>
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<p>The basic flow between each module controlled by system scheduler.</p>
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<p>Comparison of refractive indices derived from CIRA model and inversion algorithm.</p>
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<p>Comparison of refractive indices from CIRA model and inversion algorithm by absolute error.</p>
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<p>Comparison of refractive indices from CIRA model and inversion algorithm by relative error.</p>
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<p>Temperature contrasts derived from CIRA modeling and inversion algorithms.</p>
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<p>Comparison of temperatures from CIRA model and inversion algorithm by absolute error.</p>
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<p>Comparison of temperatures from CIRA model and inversion algorithm by relative error.</p>
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<p>Pressure comparisons from CIRA modeling and inversion algorithms.</p>
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<p>Comparison of pressure derived from CIRA model and inversion algorithm by absolute error.</p>
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<p>Comparison of pressure derived from CIRA model and inversion algorithm by relative error.</p>
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<p>Comparison of refractive indices derived from ERA5 model and inversion algorithm.</p>
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<p>Comparison of refractive indices from ERA5 model and inversion algorithm by absolute error.</p>
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<p>Comparison of refractive indices from ERA5 model and inversion algorithm by relative error.</p>
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<p>Temperature contrasts derived from ERA5 modeling and inversion algorithms.</p>
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<p>Comparison of temperatures from ERA5 model and inversion algorithm by absolute error.</p>
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<p>Comparison of temperatures from ERA5 model and inversion algorithm by relative error.</p>
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14 pages, 2715 KiB  
Article
Automatic GNSS Ionospheric Scintillation Detection with Radio Occultation Data Using Machine Learning Algorithm
by Guangwang Ji, Ruimin Jin, Weimin Zhen and Huiyun Yang
Appl. Sci. 2024, 14(1), 97; https://doi.org/10.3390/app14010097 - 21 Dec 2023
Cited by 1 | Viewed by 1460
Abstract
Ionospheric scintillation often occurs in the polar and equator regions, and it can affect the signals of the Global Navigation Satellite System (GNSS). Therefore, the ionospheric scintillation detection applied to the polar and equator regions is of vital importance for improving the performance [...] Read more.
Ionospheric scintillation often occurs in the polar and equator regions, and it can affect the signals of the Global Navigation Satellite System (GNSS). Therefore, the ionospheric scintillation detection applied to the polar and equator regions is of vital importance for improving the performance of satellite navigation. GNSS radio occultation is a remote sensing technique that primarily utilizes GNSS signals to study the Earth’s atmosphere, but its measurement results are susceptible to the effects of ionospheric scintillation. In this study, we propose an ionospheric scintillation detection algorithm based on the Sparrow-Search-Algorithm-optimized Extreme Gradient Boosting model (SSA-XGBoost), which uses power spectral densities of the raw signal intensities from GNSS occultation data as input features to train the algorithm model. To assess the performance of the proposed algorithm, we compare it with other machine learning algorithms such as XGBoost and a Support Vector Machine (SVM) using historical ionospheric scintillation data. The results show that the SSA-XGBoost method performs much better compared to the SVM and XGBoost models, with an overall accuracy of 97.8% in classifying scintillation events and a miss detection rate of only 12.9% for scintillation events with an unbalanced GNSS RO dataset. This paper can provide valuable insights for designing more robust GNSS receivers. Full article
(This article belongs to the Special Issue Intelligent Computing and Remote Sensing)
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<p>GNSS RO geometry.</p>
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<p>Schematic diagram of the XGBoost algorithm.</p>
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<p>Structure of the SSA-XGBoost model.</p>
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<p>Examples of GPS signal strength fluctuations recorded by the MetOp-A occultation. The left panel shows more dramatic signal strength fluctuations.</p>
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<p>Experimental flow.</p>
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<p>The confusion matrix.</p>
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<p>Confusion matrix on the test set: (<b>a</b>–<b>c</b>) correspond to the SVM, XGBoost, and SSA-XGBoost models.</p>
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26 pages, 37961 KiB  
Review
GNOS-II on Fengyun-3 Satellite Series: Exploration of Multi-GNSS Reflection Signals for Operational Applications
by Yueqiang Sun, Feixiong Huang, Junming Xia, Cong Yin, Weihua Bai, Qifei Du, Xianyi Wang, Yuerong Cai, Wei Li, Guanglin Yang, Xiaochun Zhai, Na Xu, Xiuqing Hu, Yan Liu, Cheng Liu, Dongwei Wang, Tongsheng Qiu, Yusen Tian, Lichang Duan, Fu Li, Xiangguang Meng, Congliang Liu, Guangyuan Tan, Peng Hu, Ruhan Wu and Dongmei Songadd Show full author list remove Hide full author list
Remote Sens. 2023, 15(24), 5756; https://doi.org/10.3390/rs15245756 - 16 Dec 2023
Cited by 6 | Viewed by 2138
Abstract
The Global Navigation Satellite System Occultation Sounder II (GNOS-II) payload onboard the Chinese Fengyun-3E (FY-3E) satellite is the world’s first operational spaceborne mission that can utilize reflected signals from multiple navigation systems for Earth remote sensing. The satellite was launched into an 836-km [...] Read more.
The Global Navigation Satellite System Occultation Sounder II (GNOS-II) payload onboard the Chinese Fengyun-3E (FY-3E) satellite is the world’s first operational spaceborne mission that can utilize reflected signals from multiple navigation systems for Earth remote sensing. The satellite was launched into an 836-km early-morning polar orbit on 5 July 2021. Different GNSS signals show different characteristics in the observations and thus require different calibration methods. With an average data latency of less than 3 h, many near real-time applications are possible. This article first introduces the FY-3E/GNOS-II mission and instrument design, then describes the extensive calibration methods for the multi-GNSS measurements, and finally presents application results in the remote sensing of ocean surface winds, land soil moisture and sea ice extent. Especially, the ocean surface wind product has been used in operational applications such as assimilation in the numerical weather prediction model and monitoring of tropical cyclones. Currently, GNOS-II has been carried by FY-3E, FY-3F (launched in August 2023) and FY-3G (launched in April 2023). It will be also carried by future follow-on FY series and a more complete multi-GNSS reflectometry constellation will be established. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)
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<p>A block diagram of the Spaceborne Integrated GNSS Remote Sensor (from [<a href="#B12-remotesensing-15-05756" class="html-bibr">12</a>]).</p>
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<p>Distribution of the data latency of FY-3E/GNOS-II product.</p>
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<p>Timeline of the FY-3E/GNOS-II mission stage.</p>
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<p>Illumination points (red asterisks) of FY-3E/GNOS-II for the collection of raw sampling data.</p>
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<p>Two DDMs generated from the raw IF data where the specular point is over land: (<b>a</b>) with a coherent integration time of 1 ms and incoherent integration time of 1 s and (<b>b</b>) with a coherent integration time of 2 ms and incoherent integration time of 1 s.</p>
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<p>DDMs and corresponding DMs in raw counts from GPS, BDS and GAL, respectively, over the ocean (<b>a</b>,<b>b</b>) and sea ice (<b>c</b>,<b>d</b>) (From [<a href="#B24-remotesensing-15-05756" class="html-bibr">24</a>]).</p>
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<p>DDMs and corresponding DMs in raw counts from GPS, BDS and GAL, respectively, over the ocean (<b>a</b>,<b>b</b>) and sea ice (<b>c</b>,<b>d</b>) (From [<a href="#B24-remotesensing-15-05756" class="html-bibr">24</a>]).</p>
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<p>Relationship between the total instrument gain and LNA temperature of GNOS-II measured by a prelaunch thermal cycling experiment (from [<a href="#B26-remotesensing-15-05756" class="html-bibr">26</a>]).</p>
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<p>The GNSS EIRP power monitor on the roof of NSSC building.</p>
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<p>The EIRP profiles of satellites from each GNSS system: (<b>a</b>) GPS, (<b>b</b>) BDS and (<b>c</b>) GAL. Each line stands for the profile of one satellite. Different color stands for different block types.</p>
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<p>The ESA at the DDM specular bin as a function of incidence anglefor different GNSS signals.</p>
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<p>Spatial resolution of observations from different GNSS signals under incoherent scattering.</p>
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<p>Parallel-track and Cross-track spatial resolution of observations from different GNSS signals under coherent reflection.</p>
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<p>Bin-averaged NBRCS from GPS, BDS and GAL versus collocated ECMWF wind speeds. The bin width is 1 m/s. The standard deviations of NBRCS at 4, 8 and 15 m/s are also shown as error bars (From [<a href="#B24-remotesensing-15-05756" class="html-bibr">24</a>]).</p>
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<p>Time series of monthly mean NBRCS at wind speed of 6.5–7.5 m/s for GPS, BDS and GAL observations from July 2021 to December 2022.</p>
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<p>Comparison of NBRCS between FY-3E and CYGNSS (v3.0 data) for observations over the ocean from 1 August 2021 to 31 October 2021 (From [<a href="#B25-remotesensing-15-05756" class="html-bibr">25</a>]).</p>
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<p>Comparison of reflectivities from different GNSS systems for data over sea ice from 7 July to 31 October (From [<a href="#B30-remotesensing-15-05756" class="html-bibr">30</a>]).</p>
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<p>Comparison of reflectivities from different GNSS systems for data over land from 7 July to 31 October: (<b>a</b>) before calibration; (<b>b</b>) after calibration (From [<a href="#B30-remotesensing-15-05756" class="html-bibr">30</a>]).</p>
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<p>A monthly average of the FY-3E/GNOS-II wind speeds in October 2021. The map is generated in 0.2 degree grid.</p>
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<p>Density scatter plots and error statistics for the comparison of FY-3E/GNOS-II wind speeds versus ECMWF wind speeds for GPS, BDS and GAL data under 25 m/s.</p>
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<p>Time series of monthly wind speed bias for GPS, BDS and GAL observations from July 2021 to December 2022.</p>
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<p>Time series of monthly wind speed RMSE for GPS, BDS and GAL observations from July 2021 to December 2022.</p>
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<p>The comparison of GNOS-II wind speeds and HWRF wind speeds of four tropical cyclone cases when GNOS-II specular point tracks passed through the center of the tropical cyclones. The four cases are Typhoon Nyatoh on 2 December 2021 (<b>a</b>), Typhoon Tokage on 23 August 2022 (<b>b</b>), Typhoon Hinnamnor on 31 August 2022 (<b>c</b>) and Tropical cyclone Darian on 24 December 2022 (<b>d</b>). For each case, the left panel is GNOS-II wind speeds, the middle panel is HWRF wind speeds overlapped by collocated GNOS-II wind speeds and the right panel is the GNOS-II and HWRF wind speed profiles for the track passing through the center of the cyclone.</p>
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<p>The comparison of GNOS-II wind speeds and HWRF wind speeds of four tropical cyclone cases when GNOS-II specular point tracks passed through the center of the tropical cyclones. The four cases are Typhoon Nyatoh on 2 December 2021 (<b>a</b>), Typhoon Tokage on 23 August 2022 (<b>b</b>), Typhoon Hinnamnor on 31 August 2022 (<b>c</b>) and Tropical cyclone Darian on 24 December 2022 (<b>d</b>). For each case, the left panel is GNOS-II wind speeds, the middle panel is HWRF wind speeds overlapped by collocated GNOS-II wind speeds and the right panel is the GNOS-II and HWRF wind speed profiles for the track passing through the center of the cyclone.</p>
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<p>Density scatter plots for the comparison between GNOS-II and HWRF wind speeds.</p>
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<p>Wind speeds of FY-3E/GNOS-II (<b>left</b>) and FY-3E/WindRad (<b>right</b>) when they passed over Typhoon Mawar at around 20:15 UTC on 25 May 2023. The track of the typhoon reported by CMA is shown as dash line. The maximum wind speeds of GNOS-II and WindRad for this overpass are 60.1 and 40.0 m/s, respectively. The maximum wind speed of the typhoon reported by CMA is 62 m/s (shown in the legend).</p>
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<p>FY-3E/GNOS-II land reflectivity observations for data from 10 July to 9 September 2021 (From [<a href="#B30-remotesensing-15-05756" class="html-bibr">30</a>]).</p>
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<p>Retrieved soil moisture from FY-3E/GNOS-II for data from 7 July to 6 August 2022 (From [<a href="#B30-remotesensing-15-05756" class="html-bibr">30</a>]).</p>
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<p>The PDF of DDM PPR for GPS-R data over sea water and sea ice: (<b>a</b>) north hemisphere, (<b>b</b>) south hemisphere.</p>
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<p>Sea ice extent retrieved by FY-3E/GNOS-II (left) in July 2022 compared to OSI SAF Global SIC product (right): (<b>a</b>) north hemisphere, (<b>b</b>) south hemisphere. The blue color stands for sea water and white color stands for sea ice extent or SIC.</p>
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<p>Global distribution GNSS-R sea surface winds (SWS), land volumetric soil moisture (VSM) and GNSS RO events from FY-3E/GNOS-II on 20 August 2022.</p>
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<p>Specular points of FY-3E, FY-3F and FY-3G in one day.</p>
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23 pages, 12933 KiB  
Article
Evaluation of Tropopause Height from Sentinel-6 GNSS Radio Occultation Using Different Methods
by Mohamed Zhran, Ashraf Mousa, Fahad Alshehri and Shuanggen Jin
Remote Sens. 2023, 15(23), 5513; https://doi.org/10.3390/rs15235513 - 27 Nov 2023
Cited by 3 | Viewed by 1264
Abstract
The tropopause is described as the distinction between the troposphere and the stratosphere, and the tropopause height (TPH) is an indicator of climate change. GNSS Radio Occultation (RO) can monitor the atmosphere globally under all weather conditions with a high vertical resolution. In [...] Read more.
The tropopause is described as the distinction between the troposphere and the stratosphere, and the tropopause height (TPH) is an indicator of climate change. GNSS Radio Occultation (RO) can monitor the atmosphere globally under all weather conditions with a high vertical resolution. In this study, four different techniques for identifying the TPH were investigated. The lapse rate tropopause (LRT) and cold point tropopause (CPT) methods are the traditional methods for determining the TPH based on temperature profiles according to the World Meteorological Organization (WMO) definition. Two advanced methods based on the covariance transform (CT) method are used to estimate the TPH from the refractivity (TPHN) and the TPH from the bending angle (TPHα). Data from the Sentinel-6 satellite were used to evaluate the different algorithms for the identification of the TPH. The analysis shows that the CPT height is greater than the LRT height and that the CPT is only valid in tropical regions. The CPT height, TPHN, and TPHα were compared with the LRT height. In general, the TPHα had the largest value, followed by the TPHN, and the LRT had the lowest value of TPH at and near the equator. In the equatorial region, the maximum TPH results from the TPHα (approximately 17.5 km), and at the poles, the minimum TPH results from the LRT (approximately 9 km). The results were also compared with the European Center for Medium-Range Weather Forecasts (ECMWF), and there was a strong correlation of 0.999 between the different approaches for identifying the TPH from RO and the ECMWF model. The identification of the TPH is critical for the transfer of mass, water, and trace gases between the troposphere and stratosphere. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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<p>A schematic of GNSS RO geometry.</p>
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<p>Distribution of GNSS RO events from Sentinel-6 on 14 January 2022 (<b>a</b>) and in January 2022 (<b>b</b>).</p>
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<p>Monthly RO profiles numbers from Sentinel-6 from January to June 2022 (<b>a</b>) and RO profiles statistics from MetOp-B, MetOp-C, and S6 for the global and latitudinal data sets from January to June 2022 (<b>b</b>).</p>
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<p>Graphical abstract of the processing flow.</p>
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<p>TPH determination from LRT and CPT on the temperature profile.</p>
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<p>Distribution of LRT height during the study period.</p>
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<p>Distribution of TPH based on CPT and LRT sliced at the same location of solved CPT from January to March 2022.</p>
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<p>Distribution of TPH based on refractivity during the study period.</p>
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<p>Distribution of TPH based on BA during the study period.</p>
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<p>Mean difference (CPT-LRT) (<b>a</b>) and correlation between LRT and CPT heights during the study period (<b>b</b>).</p>
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<p>Difference between LRT height and TPH based on refractivity for different latitude bands from 0 to 90°N every 10 degrees (<b>a</b>) showing latitude zone of maximum difference (<b>b</b>).</p>
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<p>Difference between LRT height and TPH based on BA for different latitude bands from 0 to 90°N every 10 degrees (<b>a</b>) showing latitude zone of maximum difference (<b>b</b>).</p>
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<p>Zonal mean LRT height, CPT height, TPH<span class="html-italic"><sub>N</sub></span>, and TPH<span class="html-italic"><sub>α</sub></span> during the study period.</p>
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<p>The STD of the zonal means for different algorithms for identification of TPH for June 2022.</p>
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<p>Zonal mean LRT temperature and CPT temperature during the study period.</p>
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<p>The monthly mean variation of TPH computed by different algorithms with the standard error from January to June 2022 in different latitude bands.</p>
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<p>The monthly mean variation of LRT and CPT temperature with the standard error from January to June 2022 at the low latitude band.</p>
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<p>The comparison between zonal mean CPT height (<b>a</b>), LRT height (<b>b</b>), TPH<span class="html-italic"><sub>N</sub></span> (<b>c</b>), and TPH<span class="html-italic"><sub>α</sub></span> (<b>d</b>) from GNSS RO and ECMWF model in June 2022.</p>
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<p>The zonal mean bias (GNSS RO—ECMWF) in June 2022.</p>
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<p>The comparison between zonal mean LRT height and TPHα from GNSS RO and the ECMWF model in June 2022 in tropical (<b>a</b>), subtropical (<b>b</b>), and polar regions (<b>c</b>).</p>
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<p>LRT height profiles in the tropical (<b>a</b>), subtropical (<b>b</b>), and polar regions (<b>c</b>) in the Northern Hemisphere, estimated from GNSS RO and the ECMWF model.</p>
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17 pages, 6142 KiB  
Article
Quality Assessment of the Atmospheric Radio Occultation Profiles from FY-3E/GNOS-II BDS and GPS Measurements
by Youlin He, Shaocheng Zhang, Sheng Guo and Yunlong Wu
Remote Sens. 2023, 15(22), 5313; https://doi.org/10.3390/rs15225313 - 10 Nov 2023
Cited by 2 | Viewed by 1183
Abstract
The Fengyun-3E (FY-3E) satellite carrying the advanced Global Navigation Satellite System (GNSS) Radio Occultation Sounder-II (GNOS-II) is already in operation for radio occultation (RO) observation, with the BeiDou Navigation Satellite System (BDS-2 and BDS-3) and Global Positioning System (GPS) signals tracking capability. FY-3E [...] Read more.
The Fengyun-3E (FY-3E) satellite carrying the advanced Global Navigation Satellite System (GNSS) Radio Occultation Sounder-II (GNOS-II) is already in operation for radio occultation (RO) observation, with the BeiDou Navigation Satellite System (BDS-2 and BDS-3) and Global Positioning System (GPS) signals tracking capability. FY-3E BDS and GPS RO signals tracking capability were first evaluated by comparing their penetration depths, and then the quality of the refractivity, temperature, and specific humidity profiles was analyzed with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) data. Results show the mean penetration depth of BDS occultations was 1.65 km compared to 1.09 km of GPS occultations. Between 5 and 25 km, the mean refractivity bias of the BDS (GPS) was −0.14% (0.01%) with the mean standard deviation (SD) being 1.11% (1.52%); the mean temperature biases of both were within ±0.1 K, and the mean SD of BDS was 1.1 K compared to 1.2 K for the GPS; BDS/GPS specific humidity bias was within ±0.3 g/kg with corresponding SD being less than 1.3 g/kg. Seasonal deviations of specific humidities were largest in summer and smallest in winter. Latitudinal deviations over the tropics were generally higher than in other areas. Enriched quantity and high accuracy and precision after careful calibration will promote the FY-3E RO profiles as a reliable data source for the RO community. Full article
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<p>The daily number of the FY-3E/GNOS-II occultation events and its global distribution: (<b>a</b>) the daily number of BDS occultation events in 12 months from 1 September 2022 to 31 August 2023; (<b>b</b>) same as (<b>a</b>), but for GPS; (<b>c</b>) the distribution of occultation events on 1 September 2022, which were 268 rising BDS RO events (upward-pointing blue triangle), 277 setting BDS RO events (downward-pointing blue triangle), 263 rising GPS RO events (upward-pointing red triangle), and 249 setting GPS RO events (downward-pointing red triangle).</p>
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<p>The daily number of the FY-3E/GNOS-II occultation events and its global distribution: (<b>a</b>) the daily number of BDS occultation events in 12 months from 1 September 2022 to 31 August 2023; (<b>b</b>) same as (<b>a</b>), but for GPS; (<b>c</b>) the distribution of occultation events on 1 September 2022, which were 268 rising BDS RO events (upward-pointing blue triangle), 277 setting BDS RO events (downward-pointing blue triangle), 263 rising GPS RO events (upward-pointing red triangle), and 249 setting GPS RO events (downward-pointing red triangle).</p>
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<p>The SNR (gray) and the Doppler shift (blue) of BDS and GPS RO events: (<b>a</b>) a setting BDS occultation event in July 2022; (<b>b</b>) a rising BDS occultation event in September 2022; (<b>c</b>,<b>d</b>) two setting and rising GPS occultation events in corresponding months, with OL signal tracking model.</p>
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<p>FY-3E RO penetration depths and the number of samples versus latitudes: (<b>a</b>) penetration depths of rising and setting BDS occultations versus latitudes in July 2022; (<b>b</b>) the number of samples for rising and setting BDS occultations versus latitudes in July 2022; (<b>c</b>,<b>d</b>) same as (<b>a</b>,<b>b</b>), but in September 2022; (<b>e</b>–<b>h</b>) same as (<b>a</b>–<b>d</b>), but for GPS occultations.</p>
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<p>FY-3E RO penetration depths and the number of samples versus latitudes: (<b>a</b>) penetration depths of rising and setting BDS occultations versus latitudes in July 2022; (<b>b</b>) the number of samples for rising and setting BDS occultations versus latitudes in July 2022; (<b>c</b>,<b>d</b>) same as (<b>a</b>,<b>b</b>), but in September 2022; (<b>e</b>–<b>h</b>) same as (<b>a</b>–<b>d</b>), but for GPS occultations.</p>
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<p>Statistics and global maps of the penetration depths for FY-3E RO events from 1 September 2022 to 31 August 2023: (<b>a</b>,<b>b</b>) mean, median, and RMS values of the BDS RO penetration depth and its global map in a latitude-longitude grid; (<b>c</b>,<b>d</b>) same as (<b>a</b>,<b>b</b>), but for GPS RO.</p>
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<p>Biases and SDs of FY-3E BDS and GPS RO profiles from 1 September 2022 to 31 August 2023: (<b>a</b>) biases and SDs of refractivity including rising and setting occultations; (<b>b</b>) number of samples corresponding to the refractivity profiles; (<b>c</b>,<b>d</b>) same as (<b>a</b>), but for temperature and specific humidity, respectively.</p>
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<p>Seasonal differences of the FY-3E RO profiles compared with the co-located ERA5 data between 1 September 2022 and 31 August 2023: (<b>a</b>–<b>d</b>) biases and SDs of BDS and GPS occultations refractivity, number of samples corresponding to the refractivity profiles, biases and SDs of BDS and GPS occultations temperature, and biases and SDs of BDS and GPS occultations specific humidity over the northern hemisphere; (<b>e</b>–<b>h</b>) same as (<b>a</b>–<b>d</b>), but over the southern hemisphere.</p>
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<p>Seasonal differences of the FY-3E RO profiles compared with the co-located ERA5 data between 1 September 2022 and 31 August 2023: (<b>a</b>–<b>d</b>) biases and SDs of BDS and GPS occultations refractivity, number of samples corresponding to the refractivity profiles, biases and SDs of BDS and GPS occultations temperature, and biases and SDs of BDS and GPS occultations specific humidity over the northern hemisphere; (<b>e</b>–<b>h</b>) same as (<b>a</b>–<b>d</b>), but over the southern hemisphere.</p>
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<p>Biases and SDs between the FY-3E RO profiles with the co-located ERA5 data over the northern hemisphere (30°N~90°N), tropics (30°N~30°S), and the southern hemisphere (30°S~90°S): (<b>a</b>) biases and SDs of the refractivity in three different latitude bands; (<b>b</b>) number of samples corresponding to the refractivity profiles; (<b>c</b>) same as (<b>a</b>), but for temperature; (<b>d</b>) same as (<b>a</b>), but for specific humidity.</p>
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17 pages, 9822 KiB  
Article
A Comparison of Atmospheric Boundary Layer Height Determination Methods Using GNSS Radio Occultation Data
by Cong Qiu, Xiaoming Wang, Haobo Li, Kai Zhou, Jinglei Zhang, Zhe Li, Dingyi Liu and Hong Yuan
Atmosphere 2023, 14(11), 1654; https://doi.org/10.3390/atmos14111654 - 4 Nov 2023
Cited by 1 | Viewed by 1573
Abstract
The accurate determination of Atmospheric Boundary Layer Height (ABLH) is crucial in various atmospheric studies and practical applications. In this study, we present a comprehensive comparative analysis of five distinct methods for estimating ABLH using Global Navigation Satellite System (GNSS) Radio Occultation (RO) [...] Read more.
The accurate determination of Atmospheric Boundary Layer Height (ABLH) is crucial in various atmospheric studies and practical applications. In this study, we present a comprehensive comparative analysis of five distinct methods for estimating ABLH using Global Navigation Satellite System (GNSS) Radio Occultation (RO) data. These methods encompass the use of bending angle and refractivity profiles, namely Minimum Gradient methods of the Bending Angle (MGBA) and Refractivity (MGR) profiles, breaking point, Wavelet Covariance Transform (WCT), and Double-Parameter Model Function (DPMF). GNSS-RO data from COSMIC-2 and Spire are used. To establish robust validation, radiosonde data are employed as a reference, ensuring the reliability of our findings. The results reveal notable variations in the performances of these ABLH estimation methods. Specifically, the MGBA, MGR, breaking point, and DPMF methods exhibit strong correlations with the reference data. Conversely, the WCT method displays weaker correlations, higher biases, and elevated root-mean-square-errors, suggesting limitations in capturing the true ABLH. Furthermore, we remove outlier screening to facilitate a comparison of the differences among the five methods. The WCT and DPMF methods can detect strong variations in the profiles near the Earth’s surface and consider them as ABLH. However, these variations are caused by errors. The MGBA method emerges as a reliable and stable option, while the WCT and DPMF methods should be used with caution due to the lower quality of the GNSS-RO profiles near the Earth’s surface. Full article
(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)
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<p>The COSMIC-2 RO-retrieved ABLH from CDAAC. (<b>a</b>) Geographic distribution of ABLH values, including <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>B</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>B</mi> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>, exceeding the threshold of 3.5 km from 1 January 2023 to 7 January 2023. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>B</mi> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>, violating the criterion of the BP method with |A| &gt; 50 km<sup>−1</sup> for C2E1.2023.001.06.04.G30.</p>
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<p>Scatterplots between the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>B</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math> for COSMIC-2: (<b>a</b>) dilation: 0.2 km, (<b>b</b>) dilation: 0.4 km, and (<b>c</b>) dilation: 0.6 km.</p>
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<p>Location mapping: selected stations on islands or along coastlines from IGRA2 dataset.</p>
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<p>Scatter density plots between the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>A</mi> <mi>O</mi> <mi>B</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>O</mi> </mrow> </msub> </mrow> </semantics></math> over oceans for COSMIC-2 with colorbar representing the Gaussian kernel density estimation: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>B</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>B</mi> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math>, and (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>D</mi> <mi>P</mi> <mi>M</mi> <mi>F</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Scatter density plots between the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>A</mi> <mi>O</mi> <mi>B</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>O</mi> </mrow> </msub> </mrow> </semantics></math> over oceans for Spire with colorbar representing the Gaussian kernel density estimation: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>B</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>B</mi> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math>, and (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>D</mi> <mi>P</mi> <mi>M</mi> <mi>F</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Location mapping: selected stations over land from IGRA2 dataset.</p>
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<p>Scatter density plots between the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>A</mi> <mi>O</mi> <mi>B</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>O</mi> </mrow> </msub> </mrow> </semantics></math> inland for COSMIC-2 with colorbar representing the Gaussian kernel density estimation: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>B</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>B</mi> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math>, and (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>D</mi> <mi>P</mi> <mi>M</mi> <mi>F</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Scatter density plots between the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>A</mi> <mi>O</mi> <mi>B</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>O</mi> </mrow> </msub> </mrow> </semantics></math> inland for Spire with colorbar representing the Gaussian kernel density estimation: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>B</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>B</mi> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math>, and (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>D</mi> <mi>P</mi> <mi>M</mi> <mi>F</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Biases (<b>a</b>) and RMSE (<b>b</b>) of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>O</mi> </mrow> </msub> </mrow> </semantics></math> over oceans in the Northern Hemisphere using five methods. The data are grouped into four different seasons (MAM: from March to May, JJA: from June to August, SON: from September to November, and DJF: from December to February). <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> <mrow> <mi>*</mi> </mrow> </msubsup> </mrow> </semantics></math> represents the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math> without a bias of more than 1.2 km with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>A</mi> <mi>O</mi> <mi>B</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Biases (<b>a</b>) and RMSE (<b>b</b>) of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>O</mi> </mrow> </msub> </mrow> </semantics></math> inland in the Northern Hemisphere using five methods. The data are grouped into four different seasons (MAM: from March to May, JJA: from June to August, SON: from September to November, and DJF: from December to February). <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> <mrow> <mi>*</mi> </mrow> </msubsup> </mrow> </semantics></math> represents the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math> without a bias of more than 1.2 km with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>A</mi> <mi>O</mi> <mi>B</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>The annual-averaged differences of (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>B</mi> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math>, and (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>D</mi> <mi>P</mi> <mi>M</mi> <mi>F</mi> </mrow> </msub> </mrow> </semantics></math> w.r.t. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>M</mi> <mi>G</mi> <mi>B</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math> over oceans from 1 March 2022 to 28 February 2023 in a given latitude–longitude bin (1° × 1°).</p>
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<p>The boxplot of the MBLHs using the five methods from 1 March 2022 to 28 February 2023.</p>
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<p>The boxplot of the MBLHs without outlier screening using the five methods from 1 March 2022 to 28 February 2023.</p>
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<p>The GNSS-RO event profile of bending angle (red line) and refractivity (blue line) with five <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>O</mi> </mrow> </msub> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>z</mi> </mrow> <mrow> <mi>W</mi> <mi>C</mi> <mi>T</mi> </mrow> <mrow> <mo>′</mo> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>z</mi> </mrow> <mrow> <mi>D</mi> <mi>P</mi> <mi>M</mi> <mi>F</mi> </mrow> <mrow> <mo>′</mo> </mrow> </msubsup> </mrow> </semantics></math> represented using <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>z</mi> </mrow> <mrow> <mi>R</mi> <mi>O</mi> </mrow> </msub> </mrow> </semantics></math> with the other three methods as auxiliary information to determine the correct ABLH by selecting the minimum point heights from the three methods closest to them.</p>
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2953 KiB  
Proceeding Paper
Performance Characterization of Hexagon|NovAtel’s Robust Dual-Antenna Receiver (RoDAR) during the Norwegian Jamming Trial 2022
by Ali Broumandan and Sandy Kennedy
Eng. Proc. 2023, 54(1), 28; https://doi.org/10.3390/ENC2023-15470 - 29 Oct 2023
Cited by 1 | Viewed by 826
Abstract
NovAtel has recently leveraged its expertise in both receiver design and anti-jam technology to develop solutions for space- and weight-constrained applications in challenged GNSS environments. Robust Dual-Antenna Receiver (RoDAR), is based on a commercial dual-antenna receiver, originally designed for attitude determination, and employs [...] Read more.
NovAtel has recently leveraged its expertise in both receiver design and anti-jam technology to develop solutions for space- and weight-constrained applications in challenged GNSS environments. Robust Dual-Antenna Receiver (RoDAR), is based on a commercial dual-antenna receiver, originally designed for attitude determination, and employs special firmware to mitigate jammers and spoofers without an increase in size or power consumption. With RoDAR, the multi-frequency, multi-constellation dual-antenna receiver is capable of null-steering at two different frequency bands (e.g., L1 and L5). In September 2022, the Norwegian Public Roads Administration hosted JammerTest, a live, over-the-air broadcast jamming and spoofing test. This paper presents the jamming and spoofing detection and mitigation performance of RoDAR during this live broadcast test. The interference detection provides spectrum monitoring and jamming characterization on all GNSS bands. The mitigation is carried out by steering a null formed on-board the receiver towards a jamming/spoofing source at GPS L1 and L5 bands. The null steering performance is characterized as a function of signal and position availability compared to a non-protected NovAtel receiver. The effectiveness of the anti-jam and anti-spoofing technology is demonstrated using representative complex spoofing and jamming test cases during this event. Full article
(This article belongs to the Proceedings of European Navigation Conference ENC 2023)
Show Figures

Figure 1

Figure 1
<p>RoDAR operation.</p>
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<p>NovAtel’s test vehicle with 4 RoDAR array and one GNSS-850 antenna on the roof.</p>
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<p>Input power measured by ITK during the ramp power jammer at L1, L2 and L5 bands.</p>
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<p>Average CN0 variation during the ramp power jamming for GPS L1 and L5.</p>
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<p>L1 Total in-band power, 3D position standard deviation for RoDAR and PwrPak7.</p>
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<p>Spectrum of GNSS bands (L1, L2, L5) under clean, open-sky condition.</p>
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<p>Spectrum of GNSS L1 band under high power jamming attack.</p>
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<p>Spectrum of GNSS L1 band under jamming attack and spoofing attack.</p>
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<p>GPS L1 input power, mean CN0 for GPS L1CA for RoDAR and PwrPak7 units.</p>
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<p>Average CN0 of signals at L1 band tracked by PwrPak7.</p>
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<p>Average CN0 of signals at L1 band tracked by RoDAR.</p>
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