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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. [ABSTRACT FROM AUTHOR]

Deploying LSM-tree-based key-value stores on distributed file systems is a common approach to building distributed key-value storage systems, such as RocksDB on HDFS (RoH). However, due to the inherent read amplification characteristics of the LSM-tree, RoH faces performance degradation issues in read operations, including gets and scans, caused by the transmission of a large amount of irrelevant data between data nodes and the master node. To address these challenges, we firstly propose a computation offloading strategy that shifts get and scan operations from the master node to data nodes to reduce unnecessary data transmission. To boost the read operations, we then design concurrent execution methods: Concurrent-Get and Concurrent-Scan. Finally, we implement our prototype system, CDNRocks, based on RoH, and conduct extensive experiments in a distributed multi-node environment to demonstrate the effectiveness of CDNRocks. The results indicate that CDNRocks outperforms RoH by exhibiting better read performance, less data transmission, and lower CPU utilization on the master node. Furthermore, compared to other distributed key-value storage systems like Cassandra and HBase, CDNRocks excels in read performance within small cluster environments(with node counts up to 9), while achieving a balanced trade-off in load performance and efficient CPU and memory utilization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite Systems (GNSS), including the US's GPS, China's BDS, the European Union's Galileo, and Russia's GLONASS, offer real-time, all-weather, any-time, anywhere and high precision observations by transmitting L band signals continuously, which have been widely used for positioning, navigation and timing. With the development of GNSS technology, it has been found that GNSS-reflected signals can be used to detect Earth's surface characteristics together with other signals of opportunity. In this paper, the current status and latest advances are presented on Global Navigation Satellite System-Reflectometry (GNSS-R) in theory, methods, techniques and observations. New developments and progresses in GNSS-R instruments, theoretical modeling, and signal processing, ground and space-/air-borne experiments, parameters retrieval (e.g. wind speed, sea surface height, soil moisture, ice thickness), sea surface altimetry and applications in the atmosphere, oceans, land, vegetation, and cryosphere are given and reviewed in details. Meanwhile, the challenges in the GNSS-R development of each field are also given. Finally, the future applications and prospects of GNSS-R are discussed, including multi-GNSS reflectometry, new GNSS-R receivers, GNSS-R missions, and emerging applications, such as mesoscale ocean eddies, ocean phytoplankton blooms, microplastics detection, target recognition, river flow, desert studies, natural hazards and landslides monitoring. [ABSTRACT FROM AUTHOR]

Based on delay-Doppler maps (DDMs) in raw counts generated by spaceborne global navigation satellite system reflectometry (GNSS-R) receivers, retrieving ocean surface wind speeds is feasible, so several spaceborne GNSS-R missions have been carried out. However, it is currently troubled by global data latency of several hours or even more due to the bottleneck in the satellite downlink. Consequently, this article, for the first time, presents an algorithm for spaceborne GNSS-R receivers to conduct the DDM calibration in orbit and then to retrieve ocean surface wind speeds in real time, which contributes to not only lightening the burden on downloading a wealth of scientific data but also broadcasting real-time ocean surface wind speeds to users. Since there is a power correlation between direct and reflected signals from the same GNSS satellite with respect to the GNSS-R receiver, this algorithm calibrates direct signal power first, and then it estimates the real-time GNSS transmitter effective isotropic radiated power at the reflected signal according to the normalized antenna pattern of the corresponding GNSS satellite. Afterward, DDMs in raw counts are calibrated. Finally, ocean surface wind speeds are computed using pretrained geophysical model functions. Exploiting the scientific data from the GNOS-II onboard China's FY-3E satellite, this algorithm is validated carefully, and final retrieved ocean surface wind speeds against collocated European Centre for Medium-Range Weather Forecasts wind speeds have an overall root-mean-square error of 1.68 m/s and 1.50 m/s for GPS-R and BDS-R, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The FengYun-3E Global Navigation Satellite System (GNSS) occultation sounder II (FY-3E GNOS II) was launched on 5 July 2021. For the first time, based on the new GNOS II sensor, this mission realizes radio occultation (RO) and reflectometry observations using the navigation signals from the third-generation BeiDou System (BDS-3), and it is hence important to assess and analyze the BDS-3 remote sensing performances relative to other systems. Here, we assessed FY-3E GNOS II RO atmospheric retrievals by inter-comparing with corresponding data from the NCEP FNL global atmospheric analysis and FY-3D GNOS mission. The GNOS RO data quality and consistency of the different FY-3 meteorological satellites, i.e., FY-3D and FY-3E, as well as different GNSS systems (GPS, BDS-2, BDS-3) were analyzed. We find that the FY-3E GNOS II RO data exhibit better quality than FY-3D GNOS, particularly in the number, penetration height toward surface, and global coverage by BDS RO profiles, due to the integration of BDS-2 and BDS-3. Additionally, comparing with co-located NCEP FNL analysis profiles, the mean difference (and standard deviation) of the FY-3E GNOS II RO atmospheric refractivity profile retrievals is found to be smaller than 0.2% (and 1%), in the upper troposphere and lower stratosphere, from 5 to 30 km, and remains consistent at this accuracy and precision level with the FY-3D GNOS RO data. These features provide clear evidence for a high utility of the new GNOS II RO data for weather and climate research and applications. [ABSTRACT FROM AUTHOR]

In this study, we propose a method for wind speed retrieval using a random forest (RF) algorithm for Cyclone Global Navigation Satellite System (CYGNSS) data. We first compared CYGNSS data with soil moisture active passive (SMAP) data and found a certain deviation in the CYGNSS “young sea, limited fetch” (YSLF) data product for high winds. Then, we used SMAP as the “ground truth” to train an RF model and applied it to the wind speed retrieval of CYGNSS data. The experimental results show that using the RF algorithm for wind speed retrieval can eliminate noise in the CYGNSS YSLF wind speed data and improve retrieval accuracy. In addition, we explored the impact of different input parameter combinations on model performance and found that using an 11-parameter model in CYGNSS wind speed retrieval can achieve optimal performance. This can provide a valuable reference for rapid near-real-time retrieval of tropical cyclones using CYGNSS. [ABSTRACT FROM AUTHOR]

Reflected global navigation satellite system (GNSS) signals from Earth surface can be received by receivers at low Earth orbit for the remote sensing of geophysical parameters. While the technique has been studied for around 30 years, most early spaceborne GNSS reflectometry missions only adapted to receive GPS signals and the studies of reflected Galileo (GAL) signals in space are limited. The Navigation Satellite System Occultation Sounder II (GNOS-II) payload onboard the FY-3E satellite is the first mission that can operationally receive reflected GPS, BeiDou (BDS), and GAL signals at the same time. This letter presents the GAL reflectometry measurements from GNOS-II together with their calibration and wind speed (WS) retrieval methods. Results show that while GAL has a different signal modulation, the observables can be used to retrieve WSs using the same geophysical model functions (GMFs) of GPS after a dedicated calibration. The retrieved WSs from GAL also have a comparable accuracy as those from GPS and BDS. [ABSTRACT FROM AUTHOR]

The reflected GNSS signals at the L-band is significantly advantageous in soil moisture monitoring as they are sensitive to the dielectric properties determined by the volumetric water content of topsoil, and they can penetrate vegetation, except in very dense forests. The Global Navigation satellite system Occultation Sounder (GNOS-II) with a reflectometry technique onboard the Fengyun-3E (FY-3E) satellite, launched on 5 July 2021, is the first mission that can receive reflected Global Navigation Satellite System (GNSS) signals from GPS, BeiDou and Galileo systems. This paper presents the soil moisture retrieval results from the FY-3E GNOS-II mission using 16 months of data. In this study, the reflectivity observations from different GNSS systems were firstly intercalibrated with some differences analyzed. Observations were also corrected by considering vegetation attenuation for 16 different land cover classifications. Finally, an empirical model was constructed for volumetric soil moisture (VSM) estimation, where the reflectivity of GNOS-II was linearly related to the SMAP reference soil moisture for each 36 km ease grid. The overall root-mean-square error of the retrieved soil moisture is 0.049 compared with the SMAP product, and 0.054 compared with the in situ data. The results of the three GNSS systems show similar levels of accuracy. This paper, for the first time, demonstrates the feasibility of global soil moisture retrieval using multiple GNSS signals. [ABSTRACT FROM AUTHOR]

The vigorous development of the global navigation satellite system (GNSS) has led to a boom in GNSS radio occultation (GNSS RO) and GNSS reflectometry (GNSS-R) techniques. Consequently, we have proposed an innovative signal processing scheme for spaceborne integrated GNSS remote sensors (SIGRS), combining a GNSS RO and a GNSS-R module. In the SIGRS, the GNSS-R module shares one precise orbit determination (POD) module with the GNSS RO module, and the GNSS-R module first achieves compatibility with GPS, BDS, and Galileo. Moreover, the programmable non-uniform delay resolution was introduced and first used by the SIGRS to generate the output DDM, which achieves a high delay resolution in the DDM central region around the specular point to improve the accuracy of basic observables but requires fewer delay bins than the conventional DDM with uniform delay resolution. The SIGRS has been successfully used to design the GNOS II onboard the Chinese FY-3E satellite, and the results of in-orbit operation validate the performance of the SIGRS, which means the SIGRS is an economically and technically efficient design and has become the first successful signal processing scheme for spaceborne integrated GNSS remote sensors around the world. [ABSTRACT FROM AUTHOR]

Direct remote‐sensing observations (e.g., radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g., ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS‐R), the lower‐level delay‐Doppler map (DDM) observable shows a complicated relationship with the ocean surface wind field. Prior studies have demonstrated DA using GNSS‐R wind retrievals inferred from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of ingesting DDM observables directly into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Bias correction and quality‐control methods are described. Several models for the required observation‐error covariance matrix are developed and evaluated, with the conclusion that a diagonal matrix performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. The 10‐m surface winds from the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational forecast are used as the background (i.e., prior in the variational analysis). Results are compared with independent scatterometer (the advanced scatterometer (ASCAT), the oceansat‐2 Scatterometer (OSCAT)) winds. For one month (June 2017) of data, the root‐mean‐square difference (RMSD) was reduced from 1.17 to 1.07 m·s−1 and bias from −0.14 to −0.08 m·s−1 for the wind speed at the specular point. Within a 150‐km wide swath along the specular point track, the RMSD was reduced from 1.20 to 1.13 m·s−1. These RMSD and bias statistics are smaller than other CYGNSS wind products available at this time. [ABSTRACT FROM AUTHOR]

Delay-Doppler maps (DDMs) are generally the lowest level of calibrated observables produced from global navigation satellite system reflectometry (GNSS-R). A forward model is presented to relate the DDM, in units of absolute power at the receiver, to the ocean surface wind field. This model and the related Jacobian are designed for use in assimilating DDM observables into weather forecast models. Given that the forward model represents a full set of DDM measurements, direct assimilation of this lower level data product is expected to be more effective than using individual specular-point wind speed retrievals. The forward model is assessed by comparing DDMs computed from hurricane weather research and forecasting (HWRF) model winds against measured DDMs from the Cyclone Global Navigation Satellite System (CYGNSS) Level 1a data. Quality controls are proposed as a result of observed discrepancies due to the effect of swell, power calibration bias, inaccurate specular point position, and model representativeness error. DDM assimilation is demonstrated using a variational analysis method (VAM) applied to three cases from June 2017, specifically selected due to the large deviation between scatterometer winds and European Centre for Medium-Range Weather Forecasts (ECMWF) predictions. DDM assimilation reduced the root-mean-square error (RMSE) by 15%, 28%, and 48%, respectively, in each of the three examples. [ABSTRACT FROM AUTHOR]

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) measurements of ocean winds present a challenge because the surface wind speed cannot be assumed homogeneous over the large glistening zone observable from orbit. The 25-km resolution requirement of the Cyclone GNSS (CYGNSS) mission limits wind speed retrievals to using only a small fraction of the delay-Doppler map (DDM) near the specular point. This paper presents a new method to invert a larger portion of the DDM and estimate the wind field within a swath defined by the maximum delay. An extended Kalman filter (EKF) is applied to combine a series of DDMs and estimates the wind speed on a uniformly gridded ocean surface exploiting the large overlap between sequential looks. Wind retrievals at the specular point using simulated data generated using wind fields from two hurricanes (Danielle and Earl, 2010) were found to meet the CYGNSS measurement requirements (2 m/s for U < 20 m/s and 10% for U > 20 m/s) and perform better than a typical single-point observable. Wind retrievals were also found to meet these requirements below 20 m/s within a 90-km swath and meet them for hurricane-force winds (<30 m/s) within a 22-km swath. Similar results were obtained using a “compressed” ($17 \times 11$) DDM, demonstrating benefits of sequential processing within a limited data budget. Delay-Doppler ambiguities introduced noticeable artifacts in circumstances where the wind varies asymmetrically within the observed ($90\times 90$ km) surface area but did not appear to affect specular point wind retrievals. [ABSTRACT FROM AUTHOR]

In radio occultation (RO) data processing and data assimilation, the forward model (FM) is used to calculate bending angle (BA) from refractivity (N). The accuracy and precision of forward modeled BA are affected by refractivity profiles and FM methods, including Abel integral algorithms (direct, exp, exp_T, linear) and methods of interpolating refractivity during integral (log-cubic spline and log-linear). Experiment 1 compares these forward model methods by comparing the difference and relative difference (RD) of the experimental value (forward modeled ECMWF analysis) and the true value (BA of FY3D RO data). Results suggested that the exp with log-cubic spline (log-cubic) interpolation is the most accurate FM because it has better integral accuracy (less than 2%) to inputs, especially when the input is lower than an order of magnitude of 1 × 10−2 (that is, above 60 km). By contrast, the direct induced a 10% error, and the improvement of exp T to exp is limited. Experiment 2 simulated the exact errors of an FM (exp) based on inputs on different vertical resolutions. The inputs are refractivity profiles on model levels of three widely used analyses, including ECMWF 4Dvar analysis, final operational global analysis data (FNL), and ERA5. Results demonstrated that based on exp and log-cubic interpolation, BA on model level of ECMWF 4Dvar has the highest accuracy, whose RD is 0.5% between 0–35 km, 4% between 35–58 km, and 1.8% between 58–80 km. By contrast, the other two analyses have low accuracy. This paper paves the way to better understanding the FM, and simulation errors on model levels of three analyses can be a helpful FM error reference. [ABSTRACT FROM AUTHOR]

FengYun-3E (FY3E), launched on 5 July 2021, is one of China's polar-orbiting meteorological satellite series. The GNOS II onboard FY3E is an operational GNSS remote sensor that for the first time combines GNSS radio occultation (GNSS RO) and GNSS reflectometry (GNSS-R). It has eight reflection channels that can track eight specular points at the same time, receiving reflected signals from multiple GNSS systems, including GPS, BeiDou and Galileo. The basic GNSS-R output generated by GNOS II is a 122 × 20 non-uniform delay-Doppler map whose high resolution portion captures more information near the specular point. This paper introduces the GNSS-R aspect of the FengYun-3E GNOS II, including the instrument, power calibration and wind speed retrieval algorithm. Preliminary validation results for its first four months of data are also presented. After preliminary quality control, the overall wind speed error is less than 2 m/s at wind speeds below 20 m/s for data from both GPS satellites and BeiDou satellites when compared to the ECMWF reanalysis winds. [ABSTRACT FROM AUTHOR]

Based on deep learning, this paper proposes a new hybrid neural network model, a recurrent deep neural network using a feature attention mechanism (FA-RDN) for GNSS-R global sea surface wind speed retrieval. FA-RDN can process data from the Cyclone Global Navigation Satellite System (CYGNSS) satellite mission, including characteristics of the signal, spatio-temporal, geometry, and instrument. FA-RDN can receive data extended in temporal dimension and mine the temporal correlation information of features through the long-short term memory (LSTM) neural network layer. A feature attention mechanism is also added to improve the model's computational efficiency. To evaluate the model performance, we designed comparison and validation experiments for the retrieval accuracy, enhancement effect, and stability of FA-RDN by comparing the evaluation criteria results. The results show that the wind speed retrieval root mean square error (RMSE) of the FA-RDN model can reach 1.45 m/s, 10.38%, 6.58%, 13.28%, 17.89%, 20.26%, and 23.14% higher than that of Backpropagation Neural Network (BPNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), respectively, confirming the feasibility and effectiveness of the designed method. At the same time, the designed model has better stability and applicability, serving as a new research idea of data mining and feature selection, as well as a reference model for GNSS-R-based sea surface wind speed retrieval. [ABSTRACT FROM AUTHOR]