Real-time Satellite Research Articles

A computationally efficient prior quality control approach for multi-GNSS real-time satellite clock estimation

A computationally efficient prior quality control approach for multi-GNSS real-time satellite clock estimation

Maintaining real-time precise point positioning during outages of the corrections broadcast by BDS-3 global short-message communication

Real-time precise point positioning (RTPPP) is a popular technique for precision applications, which is based on real-time service (RTS) products. However, one serious problem for RTPPP users is the potential communication break for a period from a few minutes to an hour. A significant decline in positioning accuracy may occur since the receiving of the RTS corrections is discontinuous. In this study, the real-time orbit and clock corrections are converted into the equivalent range corrections and broadcast by BeiDou global navigation satellite system (BDS-3) global short-message communication (GSMC). We focus on maintaining global navigation satellite systems (GNSS) RTPPP during outages of the range corrections. Three prediction methods are presented, including “linear function”, “mean value” and “last value” methods. The predicted corrections will be carried out with only at most 5 latest epochs of the registered corrections. The “last value” method shows the optimal prediction performance and the prediction errors of Galileo are minimal compared to GPS and BDS. Both simulated and vehicle kinematic experiments are conducted to evaluate the performance of RTPPP with the predicted corrections. The results show that the three proposed predicted corrections can maintain centimeter-to decimeter-level accuracy in both static and kinematic mode during outages of the BDS-3 GSMC that may be from a few minutes to an hour. Using the last registered equivalent range correction value is the most recommended choice to maintain multi-GNSS RTPPP.

A tri-band terrestrial beacon digital receiver system for TEC measurements

The ionosphere is an important component of near-Earth space that has a significant impact on communication and navigation. Furthermore, the total electron content (TEC) of the ionosphere is a widely used parameter in near-Earth plasma environment studies. A design of a digital beacon receiver system for automatically measuring TEC is presented. Universal Software Radio Peripheral (USRP) hardware equipment with software-defined radio technology was used in the tri-band beacon digital receiver to receive and rapidly preprocess beacon signals from satellites in different frequency bands. A software system is built to implement the automatic control of the receiver and to enable data processing of the preprocessed data in order to obtain its phase information and derive the TEC. The tri-band digital beacon receiver system is experimentally proven to automatically detect and archive transit satellites in real time, automatically receive and process satellite beacon signals with recording, and automatically generate TEC measurement results.

Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

Like many other African countries, Ghana's rain gauge networks are rapidly deteriorating, making it challenging to obtain real-time rainfall estimates. In recent years, significant progress has been made in the development and availability of real-time satellite precipitation products (SPPs). SPPs may complement or substitute gauge data, enabling better real-time forecasting of stream flows, among other things. However, SPPs still have significant biases that must be corrected before the rainfall estimates can be used for any hydrologic application, such as real-time or seasonal forecasting. The daily satellite-based rainfall estimate (CHIRPS-v2) data were bias-corrected using the Bias Correction and Spatial Disaggregation (BSCD) approach. The study further investigated how bias correction of daily satellite-based rainfall estimates affects the identification of seasonality and extreme rainfall indices in Ghana. The results revealed that the seasonal and annual rainfall patterns in the region were better represented after the bias correction of the CHIRPS-v2 data. We observed that, before bias correction, the cessation dates in the country's southwest and upper middle regions were slightly different. However, they matched those of the gauge well after bias correction. The novelty of this study is that, in addition to improving rainfall using CHIRPS data, it also enhances the identification of seasonality indices. The paper suggests the BCSD approach for correcting rainfall estimates from other algorithms using long-term historical records indicative of the rainfall variability area under consideration.

An improved approach for rapid filter convergence of GNSS satellite real-time orbit determination

Introduction: The real-time precise satellite orbit of Global Navigation Satellite System (GNSS) usually takes a long time to converge to a stable state using the filter method. The ultra-rapid orbit products are helpful to improve convergence speed by introducing them as external constraints. Reasonably determination of stochastic model of the constraint equation from the ultra-rapid products is the key for a better performance of convergence whereas it has not been well solved.Methods: We propose to establish the stochastic model of the orbit constraint equation by analyzing the differences between the predicted part of the ultra-rapid orbit and the filter orbit after convergence. To improve the orbit accuracy during the convergence, the constant stochastic model of the constraint equation is first determined for each system by averaging the root mean square (RMS) time-series of the differences between predicted orbit from the ultra-rapid products and the SRIF orbit after convergence in different time ranges. Besides, a time-dependent stochastic model of the constraint equation is then determined by analyzing the variation of the RMS time-series. To validate the proposed method, a month of multi-constellation data collected from 80 globally distributed stations is processed using the Square Root Information Filter (SRIF) algorithm.Results: Orbit results without introducing external orbit constraints show that the convergence time in the radial direction is 13.75, 15.25 and 17.75 h for GPS, Galileo and BDS-3 satellite, respectively. For the scheme of constant stochastic model using the average RMS over 6 h, results show that there is no significant convergence phenomenon for each system in all directions. The one-dimensional (1D) RMS during the constraint period is improved by 86.5%, 84.8%, 96.8% for GPS, Galileo and BDS-3 satellites when compared to the results without introducing external orbit constraints. As for the scheme of time-dependent stochastic model, results show that the quadratic function is suitable for modeling the RMS time-series for each system, and the accuracy of results during the constraint period has a further improvement of 1.3%, 3.7% in 1D direction for GPS, BDS-3 satellites when compared to the constant stochastic model using the average RMS over 6 h. In addition, the orbit accuracy with external orbit constraint is slightly better than those without external orbit constraint after the constraint period.Discussion: The above results show that when introducing the ultra-rapid product as the external constraint, there is basically no convergence phenomenon for GNSS satellite, while the orbit accuracy for time-dependent stochastic model has further improvement than constant stochastic model. These results indicate that the proposed method can significantly improve the convergence performance without damaging the orbit accuracy after convergence, and time-dependent stochastic model is better than constant stochastic model.

Highly miniaturized wideband angular stable linear–circular and linear–cross polarization converter for Ka-band satellite applications

AbstractThis work proposes a novel, extremely miniaturized wideband linear–circular and linear–cross metasurface-enabled reflective polarizer. This design comprises a Meander-line structure with horizontal dipoles as the top frequency selective surface, printed on a 1 mm thin-grounded FR-4 substrate. The x/y-polarized incident electromagnetic wave is converted as circularly polarized from 10.60 to 10.92 GHz (LHCP/RHCP), 12.12 to 17.32 GHz (RHCP/LHCP), and 22.72 to 37.76 GHz (LHCP/RHCP) during reflection with 2.97, 35.33, and 49.74% Fractional Bandwidth (FBW). In addition, this design shows linear–cross conversion with a minimum 90% polarization conversion ratio from 11.41 to 12, 19.01 to 22.34, and 40.74 to 46.82 GHz with an FBW of 5.04, 16.11, and 13.89%. The device performance is considerably stable under different oblique incidences, and the polarizer's unitcell is compact with a structural periodicity of 0.089 × 0.064 $\lambda _L^2$, and ultra-thin low-profile substrate thickness of 0.035λL. The proposed prototype is fabricated, and the measured results are in good agreement with the simulated one. This article also mentions how this polarizer could be tuned for dual-band K- and Ka-band satellite applications. The authors believe that the design's novelty lies in the multiband conversion with circular-polarization orthogonality, a highly miniaturized unitcell's volume of 0.199 $\lambda _L^3$/1000, and better angular stability made this design a potential candidate for real-time satellite applications.

Glacier changes and their impacts on the melt runoff in high and moderate elevation ranges of Himalayan Upper Ganges Basin

AbstractThe rise in temperatures over the Himalayan regions due to climate change have shown profound effects on the seasonality and long‐term availability of snow melt runoff and glacier melt runoff. As per the sixth assessment report by the Intergovernmental Panel on Climate Change, the Himalayan glaciers are retreating and the fraction of precipitation falling as snow is altering. This study analyses the effect of snow‐glacier changes on the melt runoff (1986–2020) over the Upper Ganga river basin (up to Rishikesh) using a fully distributed model namely, Spatial Process in Hydrology (SPHY), which governs by Temperature Index Model approach with varying degree‐day factors. This study performs the two‐step calibration approach for model parameterization and to evaluate the snow and glacier melt runoff utilizing the observed discharge and real time satellite datasets. The SPHY derived snow cover was compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) derived snow covers. Results show that the SPHY modelled fractional snow covers found comparable to MODIS derived snow covers (~70%–90% correlation). The R2 based comparison of SPHY modelled runoff (Q) and observed Q at two gauges namely, Maneri (~0.85) and Rishikesh (~0.80) found reasonable. For the assessment of glacier changes and corresponding melt runoff, temporal glacier maps (i.e., for years 1990, 2000, 2010, 2020) were prepared utilizing the Landsat imageries. Results showed a noticeable reduction in the glacier areas (~5% mass reduction in 30 years) and an increase in glacier melt runoff at different stations (e.g., at Bhojwasa ~3%–4% and at Rishikesh ~1%–2%). Similarly, a decrease in snow melt runoff (~3%–4%) and an increase in rainfall runoff (~3%) were computed. A month wise percent of change analysis has shown significant fluctuations in the magnitude and seasonality of all runoff components (i.e., snow Q, glacier Q, base Q and rainfall induced Q) in the Himalayan Upper Ganga basin.

External Measurement System for Aircraft Structural Parts Based on Digital Image Correlation in Signal-limited Spaces

An experimental study was performed on the application of the digital image correlation (DIC) technique to characterize aircraft components for which accurate time information is required. This study builds an aircraft external measurement system based on Stereo DIC. This system obtains real-time satellite time information from a GPS clock source and transmits it to a trigger unit. A camera acquires this information and displays it on the image. This solves the problem that some closed laboratories can use only computer time and cannot obtain accurate satellite time when using camera measurements. Moreover, we verify the reliability and accuracy of this approach, finding an average angular accuracy of better than 0.02 degrees and a relative error of less than 1%. Using the binocular Stereo DIC system, the accuracy of the measurements is clarified for specific conditions. Furthermore, we use this system to measure simulated aircraft landing gear, which moves at an angular velocity of 52.4 °/ s over a distance of 5 m with a wide field of view of 16.8°. The satellite time information acquired by the camera and the time information of the captured time targets are highly stable. The system’s reliability is demonstrated for use in practical engineering, contributing to the engineering application of Stereo DIC for external aircraft measurements.

A Real-Time Linear Prediction Algorithm for Detecting Abnormal BDS-2/BDS-3 Satellite Clock Offsets

Due to space environment interference, imperfect data processing model, and the performance of atomic clocks, real-time satellite clock products often contain outliers or irregular biases. We propose a real-time linear moving short-term prediction algorithm to predict clock offsets and detect abnormalities. The proposed algorithm mainly includes phase/frequency anomaly detection and real-time prediction part. Both the phase and frequency domains are used to detect abnormal clock offsets with previous epochs for building the clock prediction model accurately. The real-time moving prediction module utilizes the high short-term prediction performance to check the clock abnormality. The performance of the algorithm is then evaluated for all satellites with real-time estimated satellite clock offsets. To verify the feasibility and effectiveness of the proposed linear moving model and algorithm, the results of the grey model GM(1,1) and the ARIMA model are also compared. The experimental results indicated that the algorithm can detect clock outliers, frequency modulation, and phase jumps, and the linear model has a better clock performance improvement. After the abnormalities are removed with the proposed algorithm, the average STD accuracy of the real-time clock offsets for all satellites is improved by 15.5%, compared to an improvement of 11.4% by the GM(1,1) model and 11.5% by the ARIMA model. The PPP results demonstrate that the proposed clock prediction algorithm improves the positioning accuracy by 8.1%, 13.3%, and 16.9% in the east, north, and up components, respectively.

A New Method for Hour-by-Hour Bias Adjustment of Satellite Precipitation Estimates over Mainland China

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data.

Centimeter-Level Orbit Determination of GRACE-C Using IGS-RTS Data

GNSS real-time applications greatly benefit from the International GNSS Service’s (IGS) real-time service (RTS). This service does more than provide for terrestrial precise point positioning (PPP); it also brings more possibilities for space-borne technology. With this service, the State-Space Representation (SSR) product, which includes orbit corrections and clock corrections, is finally available to users. In this paper, the GPS real-time orbit and clock corrections provided by 11 analysis centers (ACs) from the day of the year (DOY) 144 to 153 of 2022 are discussed from 3 perspectives: integrity, continuity, and accuracy. Moreover, actual observation data from the GRACE-C satellite are processed, along with SSR corrections from different ACs. The following can be concluded: (1) In terms of integrity and continuity, the products provided by CNE, ESA, and GMV perform better. (2) CNE, ESA, and WHU are the most accurate, with values of about 5 cm for the satellite orbit and 20 ps for the satellite clock. Additionally, the clock accuracy is related to the Block. Block IIR and Block IIR-M are slightly worse than Block IIF and Block IIIA. (3) The accuracy of post-processing reduced-dynamic precise orbit determination (POD) and kinematic POD are at the centimeter level in radius, and the reduced-dynamic POD is more accurate and robust than the kinematic POD.

Rapid Estimation of Undifferenced Multi-GNSS Real-Time Satellite Clock Offset Using Partial Observations

Real-time satellite clock offset is a crucial element for real-time precise point positioning (RT-PPP). However, the elapsed time for undifferenced (UD) multi-global navigation satellite system (GNSS) real-time satellite clock offset estimation at each epoch is increased with the growth of stations, which may fall short of real-time application requirements. Therefore, a rapid estimation method for UD multi-GNSS real-time satellite clock offset is proposed to improve the computation efficiency, in which both the dimension of the normal equation (NEQ) and the number of redundant observations are calculated before adjustment; if these two values are larger than the predefined thresholds, the elevation mask is gradually increased until they are less than the predefined thresholds. Then, the clock offset estimation is conducted; this method is called clock offset estimation using partial observations. Totals of 50, 60, 70 and 80 stations are applied to perform experiments. Compared to clock offset estimation using all observations, the elapsed times of clock offset estimation using partial observations can be reduced from 6.80 to 3.10 s, 7.93 to 2.97 s, 12.04 to 3.14 s for 60, 70 and 80 stations, respectively. By using the proposed method, the elapsed time of the clock offset estimation at each epoch is less than 5 s. The estimated clock offset accuracy for GPS, BDS-3, Galileo and GLONASS satellites are better than 0.04, 0.05, 0.03 and 0.16 ns when using the partial observations to estimate clock offset with 50, 60, 70 and 80 stations, respectively. For the multi-GNSS kinematic PPP using the estimated clock offset from 50, 60, 70 and 80 stations with partial observations, the positioning accuracy at 95% confidence level in the east, north and up direction are better than 2.70, 2.20 and 5.60 cm, respectively.

Radiative Energy Budget for East Asia Based on GK-2A/AMI Observation Data

The incident and emitted radiative energy data for the top of the atmosphere (TOA) are essential in climate research. Since East Asia (11–61°N, 80–175°E) is complexly composed of land and ocean, real-time satellite data are used importantly for analyzing the detailed energy budget or climate characteristics of this region. Therefore, in this study, the radiative energy budget for East Asia, during the year 2021, was analyzed using GEO-KOMPSAT-2A/Advanced Metrological Imager (GK-2A/AMI) and the European Centre for Medium-range Weather Forecasts reanalysis (ERA5) data. The results showed that the net fluxes for the TOA and surface were −4.09 W·m−2 and −8.24 W·m−2, respectively. Thus, the net flux difference of 4.15 W·m−2 between TOA and surface implied atmospheric warming. These results, produced by GK-2A/AMI, were well-matched with the ERA5 data. However, they varied with surface characteristics; the atmosphere over ocean areas warmed because of the large amounts of longwave radiation emitted from surfaces, while the atmosphere over the plain area was relatively balanced and the atmosphere over the mountain area was cooled because large amount of longwave radiation was emitted to space. Although the GK2A/AMI radiative products used for this study have not yet been sufficiently compared with surface observation data, and the period of data used was only one year, they were highly correlated with the CERES (Clouds and the Earth’s Radiant Energy System of USA), HIMAWARI/AHI (Geostationary Satellite of Japan), and ERA5 data. Therefore, if more GK-2A/AMI data are accumulated and analyzed, it could be used for the analysis of radiant energy budget and climate research for East Asia, and it will be an opportunity to greatly increase the utilization of total meteorological products of 52 types, including radiative products.

A comprehensive evaluation of the satellite precipitation products across Iran

Study regionIran Study focusThis study evaluated three satellite precipitation products, including PERSIANN-CCS and TRMM-3B42RT V7 (near real-time satellites) and CMOROH (post real-time satellite), on hourly, daily, monthly, and annual time scales in 52 synoptic stations over Iran. This study aims to evaluate the accuracy of satellite- precipitation products over different return periods and durations as well as their performance in different climates and altitudes. The Pearson’s correlation coefficient (Rp), Relative Root Mean Square Error (RRMSE), and Relative Bias (RBias) as well as three probability indices including Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) were applied to evaluate the accuracy of these products. In addition, the sub-daily IDF curves (6, 12, 18, and 24 h) were derived to evaluate the studied satellites precipitation for 5, 10-, 20-, 50-, and 100-year return periods. New hydrological insights for the regionThe results showed that the modification method could significantly improve all satellites' performance in precipitation estimation. The IDF curves derived from the CMORPH satellite as post real-time satellite precipitation product, showed the highest accuracy compared to synoptic stations. The TRMM and CMORPH underestimated precipitation by 56% and 8%, respectively, while PERSIANN-CCS overestimated precipitation by 143%. However, PERSIANN-CCS performed better in detecting the occurrence and non-occurrence of precipitation than other products. For Northeastern and Southwestern Iran, all products showed the same performance in accurately detecting occurrence and precipitation.

Performance Analysis of Real-Time GPS/Galileo Precise Point Positioning Integrated with Inertial Navigation System

The integration of global navigation satellite system (GNSS) precise point positioning (PPP) and inertial navigation system (INS) is widely used in navigation for its robustness and resilience, especially in case of GNSS signal blockage. With GNSS modernization, a variety of PPP models have been developed and studied, which has also led to various PPP/INS integration methods. In this study, we investigated the performance of a real-time GPS/Galileo zero-difference ionosphere-free (IF) PPP/INS integration with the application of uncombined bias products. This uncombined bias correction was independent of PPP modeling on the user side and also enabled carrier phase ambiguity resolution (AR). CNES (Centre National d'Etudes Spatiales) real-time orbit, clock, and uncombined bias products were used. Six positioning modes were evaluated, including PPP, PPP/INS loosely coupled integration (LCI), PPP/INS tightly coupled integration (TCI), and three of these with uncombined bias correction through a train positioning test in an open sky environment and two van positioning tests at a complex road and city center. All of the tests used a tactical-grade inertial measurement unit (IMU). In the train test, we found that ambiguity-float PPP had almost identical performance with LCI and TCI, which reached an accuracy of 8.5, 5.7, and 4.9 cm in the north (N), east (E) and up (U) direction, respectively. After AR, significant improvements on the east error component were achieved, which were 47%, 40%, and 38% for PPP-AR, PPP-AR/INS LCI, and PPP-AR/INS TCI, respectively. In the van tests, frequent signal interruptions due to bridges, vegetation, and city canyons make the IF AR difficult. TCI achieved the highest accuracies, which were 32, 29, and 41 cm for the N/E/U component, respectively, and also effectively eliminated the solution re-convergence in PPP.

Estimation and impact of GLONASS inter-frequency code bias in single-frequency precise point positioning

The current GLONASS system suffers from the inter-frequency code bias (IFCB) and the inter-frequency phase bias (IFPB) due to its employment of the frequency division multiple access (FDMA) technology. The IFCB affects the code observation’s accuracy and further degrades the positioning accuracy, while the IFPB can be absorbed into the float ambiguity term. With increasing demand on precise point positioning (PPP) in mass market applications using low-cost single-frequency (SF) receivers, the impact of IFCB on SF-PPP with low-cost receivers need to be investigated. In this paper, we propose the use of the University of Calgary (UofC) model to estimate the IFCB in SF-PPP. The feasibility of the proposed approach is first verified using GNSS observations from the IGS stations. Comparing to the traditional SF-PPP model, the UofC model enables more stable IFCB estimations with 80.2% of the maximum changes less than 1.0 ns within one week. Then, the stability of IFCB in the low-cost SF receiver is analyzed with static data collected with u-blox M8T, and the STandard Deviation (STD) of IFCB for each satellite within 5 days is less than 1.5 ns when applying real-time orbit, clock and ionospheric corrections. Further, it’s verified that the power off won’t affect the IFCB’s value significantly in the low-cost receiver. Finally, six different strategies are designed to evaluate the impact of the GLONASS IFCB on SF-PPP. Comparing to those strategies ignoring IFCB, the strategies considering IFCB in SF-PPP can improve the positioning accuracy in both static and kinematic tests.

Online fusion of multi-resolution multispectral images with weakly supervised temporal dynamics

Real-time satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena such as floods, earthquakes, etc. One important constraint of satellite imaging is the trade-off between spatial/spectral resolution and their revisiting time, a consequence of design and physical constraints imposed by satellite orbit among other technical limitations. In this paper, we focus on fusing multi-temporal, multi-spectral images where data acquired from different instruments with different spatial resolutions is used. We leverage the spatial relationship between images at multiple modalities to generate high-resolution image sequences at higher revisiting rates. To achieve this goal, we formulate the fusion method as a recursive state estimation problem and study its performance in filtering and smoothing contexts. Furthermore, a calibration strategy is proposed to estimate the time-varying temporal dynamics of the image sequence using only a small amount of historical image data. Differently from the training process in traditional machine learning algorithms, which usually require large datasets and computation times, the parameters of the temporal dynamical model are calibrated based on an analytical expression that uses only two of the images in the historical dataset. A distributed version of the Bayesian filtering and smoothing strategies is also proposed to reduce its computational complexity. To evaluate the proposed methodology we consider a water mapping task where real data acquired by the Landsat and MODIS instruments are fused generating high spatial–temporal resolution image estimates. Our experiments show that the proposed methodology outperforms the competing methods in both estimation accuracy and water mapping tasks.

Monthly extended ocean predictions based on a convolutional neural network via the transfer learning method

Sea surface temperature anomalies (SSTAs) and sea surface height anomalies (SSHAs) are indispensable parts of scientific research, such as mesoscale eddy, current, ocean-atmosphere interaction and so on. Nowadays, extended-range predictions of ocean dynamics, especially in SSTA and SSHA, can provide daily prediction services in the range of 30 days, which bridges the gap between synoptic-scale weather forecasts and monthly average scale climate predictions. However, the forecast efficiency of extended range remains problematic. With the development of ocean reanalysis and satellite remote sensing products, large amounts datasets provide an unprecedented opportunity to use big data for the extended range prediction of ocean dynamics. In this study, a hybrid model, combing convolutional neural network (CNN) model with transfer learning (TL), was established to predict SSTA and SSHA at monthly scales, which makes full use of these data resources that arise from delayed gridding reanalysis products and real-time satellite remote sensing observations. The proposed model, where both ocean and atmosphere reanalysis datasets serve as the pretraining dataset and the satellite remote sensing observations are employed for fine-tuning based on the transfer learning (TL) method, can effectively capture the evolving spatial characteristics of SSTAs and SSHAs with low prediction errors over the 30 days range. When the forecast lead time is 30 days, the root means square errors for the SSTAs and SSHAs model results are 0.32°C and 0.027 m in the South China Sea, respectively, indicating that this model has not only satisfactory prediction performance but also offers great potential for practical operational applications in improving the skill of extended-range predictions.

Real-time Satellite Anomaly Data Tagging Based on DAE-LSTM

Abstract Spacecraft is the main carrier of human exploration of outer space, exploration and understanding of the Earth and the universe, and the development of spaceflight can promote human civilization andsocial development, and can meet the nee-ds of economic construction, scientific and technological development, security construction, social progress and other aspects. The current global number of satellites in orbit reaches 5,465, of which China has 541. The vigorous development of the space industry symbolizes the steady improvement of the country’s comprehensive national power and overall technology. During the operation, the satellite in orbit needs to transmit data to the ground, these data may be subject to interference from various aspects, or even equipment failure, we find these data in real time is very important to reduce losses. The data transmitted by satellite has obvious temporal characteristics, and Long Short-Term Memory (LSTM) network has obvious advantages for processing temporal data, so this paper proposes a BER marking model based on the combination of LSTM network and self-coding technology. By comparing the data before and after noise reduction, a threshold value can be determined, and the BERs can be accurately distinguished by this method. After testing with real satellite temperature data, the accuracy of the model detection reaches a high level.

Realizing rapid re-convergence in multi-GNSS real-time satellite clock offset estimation with dual-thread integer ambiguity resolution

Realizing rapid re-convergence in multi-GNSS real-time satellite clock offset estimation with dual-thread integer ambiguity resolution