Search Results (102)

Atmospheric Rivers (ARs) transport significant amounts of moisture and cause extreme precipitation events, yet their behavior over Africa is not well understood. This study addresses this gap by analyzing the occurrence, seasonal variability, and spatial dynamics of ARs across the continent from 2009 to 2019. Utilizing ERA5 reanalysis data, Global Navigation Satellite Systems Radio Occultation (GNSS RO) measurements, and the Image-Processing-based Atmospheric River Tracking (IPART) method, distinct seasonal AR patterns are identified. Southern Africa experiences peak activity during austral summer, while AR occurrence in Northern Africa peaks in boreal winter and spring, aligning with regional rainy seasons. Moisture sources include the Atlantic Ocean, the Arabian Sea, and the Red Sea. A comparison of ERA5 Integrated Water Vapor (IWV) estimates with high-resolution GNSS RO data shows that both datasets effectively capture broad-scale moisture patterns. However, ERA5 consistently delivers higher IWV values compared to GNSS RO, which is likely due to underrepresentation of GNSS RO IWV values, since profiles generally do not reach all the way down to the surface—but also due to an overrepresentation of humidity in the ERA5 reanalyses. Understanding AR dynamics in Africa is essential to improve climate resilience, water management and understanding extreme precipitation events. Full article

This paper accompanies the initial public release of the EDI multi-modal SLAM dataset, a collection of long tracks recorded with a portable sensor package. These include two global shutter RGB camera feeds, LiDAR scans, as well as inertial and GNSS data from an RTK-enabled IMU-GNSS positioning module—both as satellite fixes and internally fused interpolated pose estimates. The tracks are formatted as ROS1 and ROS2 bags, with separately available calibration and ground truth data. In addition to the filtered positioning module outputs, a second form of sparse ground truth pose annotation is provided using independently surveyed visual fiducial markers as a reference. This enables the meaningful evaluation of systems that directly utilize data from the positioning module into their localization estimates, and serves as an alternative when the GNSS reference is disrupted by intermittent signals or multipath scattering. In this paper, we describe the methods used to collect the dataset, its contents, and its intended use. Full article

Global Navigation Satellite System (GNSS) Radio Occultation (RO) and GNSS Reflectometry (GNSS-R) are the two major spaceborne GNSS remote sensing (GNSS-RS) techniques, providing observations of atmospheric profiles and the Earth’s surface. With the rapid development of GNSS-RS techniques and spaceborne missions, many experiments and studies were conducted to assimilate those observational data into numerical weather-prediction models for tropical cyclone (TC) forecasts. GNSS RO data, known for its high precision and all-weather observation capability, is particularly effective in forecasting mid-to-upper atmospheric levels. GNSS-R, on the other hand, plays a significant role in improving TC track and intensity predictions by observing ocean surface winds under high precipitation in the inner core of TCs. Different methods were developed to assimilate these remote sensing data. This review summarizes the results of assimilation studies using GNSS-RS data for TC forecasting. It concludes that assimilating GNSS RO data mainly enhances the prediction of precipitation and humidity, while assimilating GNSS-R data improves forecasts of the TC track and intensity. In the future, it is promising to combine GNSS RO and GNSS-R data for joint retrieval and assimilation, exploring better effects for TC forecasting. Full article

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

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

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

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

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 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

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 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

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

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

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

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 (TPH_N) and the TPH from the bending angle (TPH_α). 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, TPH_N, and TPH_α were compared with the LRT height. In general, the TPH_α had the largest value, followed by the TPH_N, and the LRT had the lowest value of TPH at and near the equator. In the equatorial region, the maximum TPH results from the TPH_α (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