ORCID Profile
0000-0001-9376-1148
Current Organisations
China University of Mining and Technology School of Environment Science and Spatial Informatics
,
Institute of Bei-Star Geospatial Innovations (Nanjing) Pty Ltd
,
RMIT University
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Publications
Significant Wave Height Retrieval Based on Multivariable Regression Models Developed With CYGNSS Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Analysis of ionospheric structure influences on residual ionospheric errors in GNSS radio occultation bending angles based on ray tracing simulations
Publisher: Copernicus GmbH
Date: 31-08-2017
DOI: 10.5194/AMT-2017-242
Abstract: Abstract. The Global Navigation Satellite System (GNSS) radio occultation (RO) technique is widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source to RO at upper stratospheric altitudes and a linear dual-frequency bending angle correction is commonly used to remove the first-order ionospheric effect. However, the residual higher-order ionospheric error (RIE) can still be significant so that it needs to be further mitigated for high accuracy applications, especially from 30 km altitude upward where the RIE is most relevant compared to the decreasing magnitude of the atmospheric bending angle. In a previous study we quantified RIEs using an ensemble of about 700 quasi-realistic end-to-end simulated RO events, finding typical RIEs at the 0.1 to 0.5 μrad noise level, but were left with 26 exceptional events with anomalous RIEs at the 1 to 10 μrad level that remained unexplained. In this study, we focused on investigating the causes of the high RIE of these exceptional events, employing detailed along-raypath analyses of atmospheric and ionospheric refractivities, impact parameter changes, and bending angles and RIEs under asymmetric and symmetric ionospheric structures. We found that the main causes of the high RIEs are a combination of physics-based effects, where asymmetric ionospheric conditions play the primary role, more than the ionization level driven by solar activity, and technical ray tracer effects due to occasions of imperfect smoothness in ionospheric refractivity model derivatives. We also found that along-ray impact parameter variations of more than 10 to 20 m are well possible due to ionospheric asymmetries, and depending on prevailing horizontal refractivity gradients are positive or negative relative to the initial impact parameter at the GNSS transmitter. Furthermore, mesospheric RIEs are found generally higher than upper stratospheric ones, likely due to being closer in tangent point heights to the ionospheric E layer peaking near 105 km, which increases RIE vulnerability. In future we will further improve the along-ray modeling system to fully isolate technical from physics-based effects and to use it beyond this work for additional GNSS RO signal propagation studies.
Quantifying residual ionospheric errors in GNSS radio occultation bending angles based on ensembles of profiles from end-to-end simulations
Publisher: Copernicus GmbH
Date: 20-01-2015
Abstract: Abstract. The radio occultation (RO) technique using signals from the Global Navigation Satellite System (GNSS), in particular from the Global Positioning System (GPS) so far, is meanwhile widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source in RO measurements at stratospheric altitudes and a linear ionospheric correction of dual-frequency RO bending angles is commonly used to remove the first-order ionospheric effect. However, the residual ionopheric error (RIE) can still be significant so that it needs to be further mitigated for high accuracy applications, especially above about 30 km altitude where the RIE is most relevant compared to the magnitude of the neutral atmospheric bending angle. Quantification and careful analyses for better understanding of the RIE is therefore important towards enabling benchmark-quality stratospheric RO retrievals. Here we present such an analysis of bending angle RIEs covering the stratosphere and mesosphere, using quasi-realistic end-to-end simulations for a full-day ensemble of RO events. Based on the ensemble simulations we assessed the variation of bending angle RIEs, both biases and SDs, with solar activity, latitudinal region, and with or without the assumption of ionospheric spherical symmetry and of co-existing observing system errors. We find that the bending angle RIE biases in the upper stratosphere and mesosphere, and in all latitudinal zones from low- to high-latitudes, have a clear negative tendency and a magnitude increasing with solar activity, in line with recent empirical studies based on real RO data. The maximum RIE biases are found at low latitudes during daytime, where they amount to with in −0.03 to −0.05 μrad, the smallest at high latitudes (0 to −0.01 μrad quiet space weather and winter conditions). Ionospheric spherical symmetry or asymmetries about the RO event location have only a minor influence on RIE biases. The RIE SDs are markedly increased both by ionospheric asymmetries and increasing solar activity and amount to about 0.3 to 0.7 μrad in the upper stratosphere and mesosphere. Taking into account also realistic observation errors of a modern RO receiving system, amounting globally to about 0.4 μrad (un-biased SD), shows that the random RIEs are typically comparable to the total observing system error. The results help to inform future RIE mitigation schemes that will improve upon the use of the linear ionospheric correction of bending angles and that will also provide explicit uncertainty estimates.
Editorial for the Special Issue ″Climate Modelling and Monitoring Using GNSS″
Publisher: MDPI AG
Date: 02-09-2022
DOI: 10.3390/RS14174371
Abstract: Reliably modelling and monitoring the climate requires robust data that can be used to feed meteorological models, and, most importantly, to independently validate those models [...]
Unconstrained, robust Kalman filtering for GNSS troposphere tomography
Publisher: Copernicus GmbH
Date: 24-10-2013
Abstract: Abstract. The mesoscale variability of water vapour (WV) in the troposphere is a highly complex phenomenon and modeling and monitoring the WV distribution is a very important but challenging task. Any observation technique that can reliably provide WV distribution is essential for both monitoring and predicting weather. GNSS tomography technique is a powerful tool that builds upon the critical ground-based GNSS infrastructure – Continuous Operating Reference Station (CORS) networks and can be used to sense the amount of WV. Previous research suggests that 3-D WV field from GNSS tomography has an uncertainty of 1 hPa. However all the models used in GNSS tomography heavily rely on a priori information and constraints from non-GNSS measurements. In this study, 3-D GNSS tomography models are investigated based on an unconstrained approach with limited a priori information. A case study is designed and the results show that unconstrained solutions are feasible by using a robust Kalman filtering technique and effective removal of linearly dependent observations and parameters. Discrepancies between reference wet refractivity data derived from the Australian Numerical Weather Prediction (NWP) model (i.e. ACCESS) and the GNSS tomography model using both simulated and real data are 4.2 ppm (mm km−1) and 6.5 ppm (mm km−1), respectively, which are essentially in the same order of accuracy. Therefore the accuracy of the integrated values should not be worse than 0.06 m in terms of zenith wet delay and the integrated water vapour is a fifth of this value which is roughly 10 mm.
Nutritional Interventions and Considerations for the Development of Low Calorie or Sugar Free Foods
Publisher: Bentham Science Publishers Ltd.
Date: 28-04-2020
DOI: 10.2174/1573399815666190807144422
Abstract: Diabetes is a globally prevalent chronic metabolic disease characterized by blood glucose levels higher than the normal levels. Sugar, a common constituent of diet, is also a major factor often responsible for elevating the glucose level in diabetic patients. However, diabetic patients are more prone to eat sweets amongst the human population. Therefore, we find a popular consumption of zero or low-calorie sweeteners, both natural and artificial. But, the uses of these sweeteners have proved to be controversial. Thus, the purpose of this review was to critically analyze and highlight the considerations needed for the development of sugar-free or low-calorie products for diabetic patients. For this purpose, various measures are taken such as avoiding sugary foods, using natural nectar, artificial sweeteners, etc. It cannot be ignored that many health hazards are associated with the overconsumption of artificial sweeteners only. These sweeteners are high-risk compounds and a properly balanced consideration needs to be given while making a diet plan for diabetic patients.
Ionospheric and Thermospheric Responses to the Recent Strong Solar Flares on 6 September 2017
Publisher: American Geophysical Union (AGU)
Date: 10-2018
DOI: 10.1029/2018JA025700
An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation
Publisher: MDPI AG
Date: 07-05-2021
DOI: 10.3390/AGRICULTURE11050420
Abstract: Rice bacterial leaf streak (BLS) is a serious disease in rice leaves and can seriously affect the quality and quantity of rice growth. Automatic estimation of disease severity is a crucial requirement in agricultural production. To address this, a new method (termed BLSNet) was proposed for rice and BLS leaf lesion recognition and segmentation based on a UNet network in semantic segmentation. An attention mechanism and multi-scale extraction integration were used in BLSNet to improve the accuracy of lesion segmentation. We compared the performance of the proposed network with that of DeepLabv3+ and UNet as benchmark models used in semantic segmentation. It was found that the proposed BLSNet model demonstrated higher segmentation and class accuracy. A preliminary investigation of BLS disease severity estimation was carried out based on our BLS segmentation results, and it was found that the proposed BLSNet method has strong potential to be a reliable automatic estimator of BLS disease severity.
Sensing small-scale structures in the troposphere with tomographic principles (IAG working group)
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-8469
Abstract: & & Within the International Association of Geodesy (IAG), a new working group was formed with the intention to bring together researchers and professionals working on tomography-based concepts for sensing the neutral atmosphere with space-geodetic techniques. Hereby the focus lies on Global Navigation Satellite Systems (GNSS) but also on complementary observation techniques, like Interferometric Synthetic Aperature Radar (InSAR) or microwave radiometers, sensitive to the water vapor distribution in the lower atmosphere.& & & & In the next four years (2019-2023), we will address current challenges in tropospheric tomography with focus on ground-based and space-based measurements, the combination of measurement techniques and the design of new observation concepts using tomographic principles. While geodetic GNSS networks are nowadays the backbone for troposphere tomography studies, further local densifications, e.g. at airports, cities or fundamental stations are necessary to achieve very fine spatial and temporal resolution. Besides, the combination of ground-based GNSS with other microwave techniques like radio occultation or InSAR seems to be beneficial due their complementary nature. Therefore, several further developments in the field of tropospheric tomography are required. This includes more dynamical tomography models - adaptable to varying input data, advanced ray-tracing algorithms for the reconstruction of space-based observations and the coordination of a benchmark c aign.& & & & In this presentation, we will give an overview about the current challenges in tropospheric tomography and the objectives of working group. The latter will also include standards for data exchange and therefore, make tomographic products available for the assimilation into numerical weather prediction models but also for various other disciplines, which rely on accurate wet refractivities or derived products like tropospheric signal delays.& &
Change Detection in SAR Images via Ratio-Based Gaussian Kernel and Nonlocal Theory
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Influence of station density and multi-constellation GNSS observations on troposphere tomography
Publisher: Copernicus GmbH
Date: 14-01-2019
Abstract: Abstract. Troposphere tomography, using multi-constellation observations from global navigation satellite systems (GNSSs), has become a novel approach for the three-dimensional (3-D) reconstruction of water vapour fields. An analysis of the integration of four GNSSs (BeiDou, GPS, GLONASS, and Galileo) observations is presented to investigate the impact of station density and single- and multi-constellation GNSS observations on troposphere tomography. Additionally, the optimal horizontal resolution of the research area is determined in Hong Kong considering both the number of voxels ided, and the coverage rate of discretized voxels penetrated by satellite signals. The results show that densification of the GNSS network plays a more important role than using multi-constellation GNSS observations in improving the retrieval of 3-D atmospheric water vapour profiles. The root mean square of slant wet delay (SWD) residuals derived from the single-GNSS observations decreased by 16 % when the data from the other four stations are added. Furthermore, additional experiments have been carried out to analyse the contributions of different combined GNSS data to the reconstructed results, and the comparisons show some interesting results: (1) the number of iterations used in determining the weighting matrices of different equations in tomography modelling can be decreased when considering multi-constellation GNSS observations and (2) the reconstructed quality of 3-D atmospheric water vapour using multi-constellation GNSS data can be improved by about 11 % when compared to the SWD estimated with precise point positioning, but this was not as high as expected.
Ionospheric Regions Producing Anomalous GNSS Radio Occultation Results
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Dynamic statistical optimization of GNSS radio occultation bending angles: advanced algorithm and performance analysis
Publisher: Copernicus GmbH
Date: 25-08-2015
Abstract: Abstract. We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS)-based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAllenging Minisatellite Payload (CHAMP) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction of random errors (standard deviations) of optimized bending angles down to about half of their size or more (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data (3) improved retrieval of refractivity and temperature profiles and (4) realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well-characterized and high-quality atmospheric profiles over the entire stratosphere.
Improvement of Reflection Detection Success Rate of GNSS RO Measurements Using Artificial Neural Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Integrated 1D, 2D, and 3D CNNs Enable Robust and Efficient Land Cover Classification from Hyperspectral Imagery
Publisher: MDPI AG
Date: 10-2023
DOI: 10.3390/RS15194797
An Improved Tropospheric Tomographic Model Based on Artificial Neural Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Hyperspectral Superresolution Reconstruction via Decomposition of Low-Rank and Sparse Tensor
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
An Improved Method for Rainfall Forecast Based on GNSS-PWV
Publisher: MDPI AG
Date: 30-08-2022
DOI: 10.3390/RS14174280
Abstract: Global navigation satellite systems (GNSS) has been applied to the sounding of precipitable water vapor (PWV) due to its high accuracy and high spatiotemporal resolutions. PWV obtained from GNSS (GNSS-PWV) can be used to investigate extreme weather phenomena, such as the formation mechanism and prediction of rainfalls. In the study, a new, improved model for rainfall forecasting was developed based on GNSS data and rainfall data for the 9-year period from 2010 to 2018 at 66 stations located in the USA. The new model included three prediction factors—PWV value, PWV increase, maximum hourly PWV increase. The two key tasks involved for the development of the model were the determination of the thresholds for each prediction factor and the selection of the optimal strategy for using the three prediction factors together. For determining the thresholds, both critical success index (CSI) and true skill statistic (TSS) were tested, and results showed that TSS outperformed CSI for all rainfall events tested. Then, various strategies by combining the three prediction factors together were also tested, and results indicated that the best forecast result was from the case that any two of the prediction factors were over their own thresholds. Finally, the new model was evaluated using the GNSS data for the 2-year period from 2019 to 2020 at the above mentioned 66 stations, and the probability of detection (POD) and false-alarms rate (FAR) were adopted to measure the model performances. Over the 66 stations, the POD values ranged from 73% to 97% with the mean of 87%, and the FARs ranged from 26% to 77% with the mean of 53%. Moreover, it was also found that both POD and FAR values were related to the region of the station e.g., the results at the stations that are located in humid regions were better than the ones located in dry regions. All these results suggest the feasibility and good performance of using GNSS-PWV for forecasting rainfall.
A Novel Method for Monitoring Tropical Cyclones’ Movement Using GNSS Zenith Tropospheric Delay
Publisher: MDPI AG
Date: 23-06-2023
DOI: 10.3390/RS15133247
Abstract: Precipitable water vapor (PWV) is an important meteorological factor for predicting extreme weather events such as tropical cyclones, which can be obtained from zenith tropospheric delay (ZTD) by using a conversion. A time difference of ZTD arrival (TDOZA) model was proposed to monitor the movement of tropical cyclones, and the fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ERA5)-derived ZTD (ERA5-ZTD) was used to estimate the movement of tropical cyclones based on the model. The global navigation satellite system-derived ZTD and radiosonde data-derived PWV (RS-PWV) were used to test the accuracy of the ERA5-ZTD and analyze the correlation between ZTD and PWV, respectively. The statistics showed that the mean Bias, RMS and STD of the ERA5-ZTD were 6.4 mm, 17.1 mm and 16.5 mm, respectively, and the mean correlation coefficient of the ERA5-ZTD and RS-PWV was 0.951, which indicates that the ZTD can be used to predict weather events instead of PWV. Then, spatiao-temporal characteristics of ZTD during the four tropical cyclone (i.e., Merbok, ROKE, Neast and Hato) periods in 2017 were analyzed, and the result showed that the moving directions of ZTD and the tropical cyclones were consistent. Thus, the ZTD time series over the ERA5 grids around the tropical cyclones’ paths were used to estimate the velocity of the tropical cyclones based on the TDOZA model, when the tropical cyclones are approaching or leaving. Compared with the result from the China Meteorological Administration, the mean absolute and relative deviations of the TDOZA model-derived velocity were 2.55 km/h and 10.0%, respectively. These results suggest that ZTD can be used as a new supplementary meteorological parameter for monitoring tropical cyclone events.
New Model for Vertical Distribution and Variation of Atmospheric Water Vapor – A Case Study for China
Publisher: Research Square Platform LLC
Date: 19-04-2022
DOI: 10.21203/RS.3.RS-1497870/V1
Abstract: For better modeling the variations in the vertical distribution of water vapor, in this study, a new function for the vertical variation in water vapor was derived, named lapse RPWV . From the analyses of lapseRPWV time-series, it was found that its vertical distribution is strongly correlated with the relative magnitude of total precipitable water vapor ( TPWV ). This study proposed a method that used six data ranges of TPWV to determine the relative magnitude of TPWV . For the periodic variations in the classified lapse RPWV time-series in each of six TPWV ranges, a spatio–temporal lapse RPWV model was developed for each range. The new models were validated by comparing their predictions against the references from sounding data at 12 radiosonde stations in China, and their performances were also compared with that of the commonly used water vapor scale height ( H ) model. Results showed that, first, the number of stations that had reduced annual RMSE of H values in TPWV ranges from 1 to 6 accounted for 92%, 92%, 67%, 83%, 100%, and 100% of the total stations, respectively. Second, the proportions of the height range that had reduced annual RMSE of water vapor density ( WVD ) in all height ranges within all TPWV ranges were above 75% at the 12 stations. Last, considering all TPWV ranges as a whole, in each of 10 height ranges, the annual RMSEs of WVD of all the stations reduced at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28%, respectively.
Limited constraint, robust Kalman filtering for GNSS troposphere tomography
Publisher: Copernicus GmbH
Date: 27-05-2014
Abstract: Abstract. The mesoscale variability of water vapour (WV) in the troposphere is a highly complex phenomenon and modelling and monitoring the WV distribution is a very important but challenging task. Any observation technique that can reliably provide WV distribution is essential for both monitoring and predicting weather. The global navigation satellite system (GNSS) tomography technique is a powerful tool that builds upon the critical ground-based GNSS infrastructure (e.g. Continuous Operating Reference Station – CORS – networks) that can be used to sense the amount of WV. Previous research shows that the 3-D WV field from GNSS tomography has an uncertainty of 1 hPa. However, all the models used in GNSS tomography heavily rely on a priori information and constraints from non-GNSS measurements. In this study, 3-D GNSS tomography models are investigated based on a limited constrained approach – i.e. horizontal and vertical correlations between voxels were not introduced, instead various a priori information were added into the system. A case study is designed and the results show that proposed solutions are feasible by using a robust Kalman filtering technique and effective removal of linearly dependent observations and parameters. Discrepancies between reference wet refractivity data derived from the Australian Numerical Weather Prediction (NWP) model (ACCESS) and the GNSS tomography model using both simulated and real data are 4.2 ppm (mm km−1) and 6.2 ppm (mm km−1), respectively, which are essentially in the same order of accuracy.
Detection of Red Tide Over Sea Surface Using GNSS-R Spaceborne Observations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
A Weakly Supervised Approach for Disease Segmentation of Maize Northern Leaf Blight from UAV Images
Publisher: MDPI AG
Date: 03-03-2023
Abstract: The segmentation of crop disease zones is an important task of image processing since the knowledge of the growth status of crops is critical for agricultural management. Nowadays, images taken by unmanned aerial vehicles (UAVs) have been widely used in the segmentation of crop diseases, and almost all current studies use the study paradigm of full supervision, which needs a large amount of manually labelled data. In this study, a weakly supervised method for disease segmentation of UAV images is proposed. In this method, auxiliary branch block (ABB) and feature reuse module (FRM) were developed. The method was tested using UAV images of maize northern leaf blight (NLB) based on image-level labels only, i.e., only the information as to whether NBL occurs is given. The quality (intersection over union (IoU) values) of the pseudo-labels in the validation dataset achieved 43% and the F1 score reached 58%. In addition, the new method took 0.08 s to generate one pseudo-label, which is highly efficient in generating pseudo-labels. When pseudo-labels from the train dataset were used in the training of segmentation models, the IoU values of disease in the test dataset reached 50%. These accuracies outperformed the benchmarks of the ACoL (45.5%), RCA (36.5%), and MDC (34.0%) models. The segmented NLB zones from the proposed method were more complete and the boundaries were more clear. The effectiveness of ABB and FRM was also explored. This study is the first time supervised segmentation of UAV images of maize NLB using only image-level data was applied, and the above test results confirm the effectiveness of the proposed method.
Correction: Hu, A. Using Bidirectional Long Short-Term Memory Method for the Height of F2 Peak Forecasting from Ionosonde Measurements in the Australian Region. Remote Sens. 2018, 10, 1658
Publisher: MDPI AG
Date: 21-11-2018
DOI: 10.3390/RS10111857
Abstract: After publication of the research paper [...]
Application of locality principle to radio occultation studies of the Earth's atmosphere and ionosphere
Publisher: Copernicus GmbH
Date: 20-01-2015
Abstract: Abstract. A new formulation of previously introduced principle of locality is presented. The principle can be applied for modernization of the radio occultation (RO) remote sensing of the atmospheres and ionospheres of the Earth and planets. The principle states that significant contributions to variations of the litude and phase of the radio waves passing through a layered medium are connected with influence of the vicinities of tangential points where the refractivity gradient is perpendicular to the radio ray trajectory. The RO method assumes spherical symmetry of the investigated medium. In this case if location of a tangent point relative to the spherical symmetry center is known, the derivatives on time of the RO signal phase and Doppler frequency variations can be recalculated into the refractive attenuation. Several important findings are consequences of the locality principle: (i) if position of the center of symmetry is known, the total absorption along the ray path can be determined at a single frequency, (ii) in the case of low absorption the height, displacement from the radio ray perigee, and tilt of the inclined ionospheric (atmospheric) layers can be evaluated, (iii) the contributions of the layered and irregular structures in the RO signal can be separated and parameters of layers and turbulence can be measured at a single frequency using joint analysis of the litude and phase variations. Specially for the Earth's troposphere, the altitude distributions of the weak total absorption (about of 1–4 db) of the radio waves at GPS frequencies corresponding to possible influence of the oxygen and water vapor can be measured with accuracy of about 0.1 db at a single frequency. According with the locality principle, a new index of ionospheric activity is introduced. This index is measured from the phase variations of radio waves passing through the ionosphere. Its high correlation with S4 scintillation index is established. This correlation indicates the significant influence of locally spherical symmetric ionospheric layers on variations of the phase and litude of the RO signal passing through transionospheric communication links. Obtained results expand the applicable domain of the RO method as a powerful remote sensing technique for geophysical and meteorological research.
Multi-Dimensional CNN Fused Algorithm for Hyperspectral Remote Sensing Image Classification,融合多维度CNN的高光谱遥感图像分类算法
Publisher: Shanghai Institute of Optics and Fine Mechanics
Date: 2021
Density‐Temperature Synchrony in the Hydrostatic Thermosphere
Publisher: American Geophysical Union (AGU)
Date: 2019
DOI: 10.1029/2018JA025973
A Comprehensive Study on Factors Affecting the Calibration of Potential Evapotranspiration Derived from the Thornthwaite Model
Publisher: MDPI AG
Date: 16-09-2022
DOI: 10.3390/RS14184644
Abstract: Potential evapotranspiration (PET) is generally estimated using empirical models thus, how to improve PET estimation accuracy has received widespread attention in recent years. Among all the models, although the temperature-driven Thornthwaite (TH) model is easy to operate, its estimation accuracy is rather limited. Although previous researchers proved that the accuracy of TH-PET can be greatly improved by using a limited number of variables to conduct calibration exercises, only preliminary experiments were conducted. In this study, to refine this innovation practice, we comprehensively investigated the factors that affect the calibration performances, including the selection of variables, seasonal effects, and spatial distribution of Global Navigation Satellite System (GNSS)/weather stations. By analyzing the factors and their effects, the following conclusions have been drawn: (1) an optimal variable selection scheme containing zenith total delay, temperature, pressure, and mean Julian Date was proposed (2) the most salient improvements are in the winter and summer seasons, with improvement rates over 80% (3) with the changes in horizontal (2.771–44.723 km) and height (1.239–344.665 m) differences among ten pairs of GNSS/weather stations, there are no obvious differences in the performances. These findings can offer an in-depth understanding of this practice and provide technical references to future applications.
The First Comparison Between Swarm‐C Accelerometer‐Derived Thermospheric Densities and Physical and Empirical Model Estimates
Publisher: American Geophysical Union (AGU)
Date: 06-2018
DOI: 10.1029/2017JA025118
Development of a New Vertical Water Vapor Model for GNSS Water Vapor Tomography
Publisher: MDPI AG
Date: 09-11-2022
DOI: 10.3390/RS14225656
Abstract: One of the main challenges of Global Navigation Satellite System (GNSS) tomography is in solving ill-conditioned system equations. Vertical constraint models are typically used in the solution procedure and play an important role in the quality of the GNSS tomography, in addition to helping resolve ill-posed problems in system equations. In this study, based on a water vapor (WV) parameter, namely IRPWV, a new vertical constraint model with six sets of coefficients for six different WV states was developed and tested throughout 2019 in the Hong Kong region with four tomographic schemes, which were carried out with the model and the traditional vertical constraint model using three different types of water vapor scale height parameters. Experimental results were numerically compared against their corresponding radiosonde-derived WV values. Compared with the tests that used the traditional model, our results showed that, first, for the daily relative error of WV density (WVD) less than 30%, the new model can lead to at least 10% and 49% improvement on average at the lower layers (below 3 km, except for the ground surface) and the upper layers (about 5–10 km), respectively. Second, the skill score of the monthly root-mean-square error (RMSE) of layered WVD above 10 accounted for about 83%, 87%, and 64%. Third, for the annual biases of layered WVD, the new model significantly decreased by 1.1–1.5 g/m3 at layers 2–3 (about 1 km), where all schemes showed the maximal bias value. Finally, for the annual RMSE of layered WVD, the new model at the lower (about 0.6–3 km) and upper layers improved by 13–42% and 5–47%, respectively. Overall, the new model performed better on GNSS tomography and significantly improved the accuracy of GNSS tomographic results, compared to the traditional model.
A new voxel-based model for the determination of atmospheric-weighted-mean temperature in GPS atmospheric sounding
Publisher: Copernicus GmbH
Date: 12-2016
DOI: 10.5194/AMT-2016-338
Abstract: Abstract. The Global Positioning System (GPS) has been regarded as a powerful atmospheric observing system for determining precipitable water vapour (PWV) nowadays. One of the most critical variables in PWV remote sensing using GPS technique is the zenith tropospheric delay (ZTD). The conversion from ZTD to PWV requires a good knowledge of the atmospheric-weighted-mean temperature (Tm) over the station. Thus the quality of PWV is affected by the accuracy of both ZTD and Tm. In this study, an improved voxel-based Tm model, named GWMT−D, was developed and validated using global reanalysis data from 2010 to 2014 provided by NCEP-DOE Reanalysis 2 data (NCEP2). The performance of GWMT−D, along with other three selected empirical Tm models, GTm−III, GWMT−IV and GTm_N, was assessed with reference Tm derived from different sources – the NCEP2, Global Geodetic Observing System (GGOS) data and radiosonde measurements. The results showed that the new GWMT−D model outperformed all the other three models with a root-mean-square error of less than 5.0 K at different altitudes over the globe. The new GWMT−D model can provide an alternative Tm determination method in real-time/near real-time GPS-PWV remote sensing system.
Investigating the Optimal Spatial Resolution for Assimilating GNSS PWV into an NWP System to Improve the Accuracy of Humidity Field
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Using Bidirectional Long Short-Term Memory Method for the Height of F2 Peak Forecasting from Ionosonde Measurements in the Australian Region
Publisher: MDPI AG
Date: 19-10-2018
DOI: 10.3390/RS10101658
Abstract: The height of F2 peak (hmF2) is an essential ionospheric parameter and its variations can reflect both the earth magnetic and solar activities. Therefore, reliable prediction of hmF2 is important for the study of space, such as solar wind and extreme weather events. However, most current models are unable to forecast the variation of the ionosphere effectively since real-time measurements are required as model inputs. In this study, a new Australian regional hmF2 forecast model was developed by using ionosonde measurements and the bidirectional Long Short-Term Memory (bi-LSTM) method. The hmF2 value in the next hour can be predicted using the data from the past five hours at the same location. The inputs chosen from a location of interest include month of the year, local time (LT), K p , F 10 . 7 and hmF2 as an independent variable vector. The independent variable vectors in the immediate past five hours are considered as an independent variable set, which is used as an input of the new Australian regional hmF2 forecast model developed for the prediction of hmF2 in the hour to come. The performance of the new model developed is evaluated by comparing with those from other popular models, such as the AMTB, Shubin, ANN and LSTM models. Results showed that: (1) the new model can substantially outperform all the other four models. (2) Compared to the LSTM model, the new model is proven to be more robust and rapidly convergent. The mew model also outperforms that of the ANN model by around 30%. (3) the minimum s le number for the bi-LSTM method (i.e., 2000) to converge is about 50% less than that is required for the LSTM method (i.e., 3000). (4) Compared to the Shubin model, the bi-LSTM method can effectively forecast the hmF2 values up to 5 h. This research is a first attempt at using the deep learning-based method for the application of the ionospheric prediction.
A new approach for GNSS tomography from a few GNSS stations
Publisher: Copernicus GmbH
Date: 19-06-2018
Abstract: Abstract. The determination of the distribution of water vapor in the atmosphere plays an important role in the atmospheric monitoring. Global Navigation Satellite Systems (GNSS) tomography can be used to construct 3-D distribution of water vapor over the field covered by a GNSS network with high temporal and spatial resolutions. In current tomographic approaches, a pre-set fixed rectangular field that roughly covers the area of the distribution of the GNSS signals on the top plane of the tomographic field is commonly used for all tomographic epochs. Due to too many unknown parameters needing to be estimated, the accuracy of the tomographic solution degrades. Another issue of these approaches is their unsuitability for GNSS networks with a low number of stations, as the shape of the field covered by the GNSS signals is, in fact, roughly that of an upside-down cone rather than the rectangular cube as the pre-set. In this study, a new approach for determination of tomographic fields fitting the real distribution of GNSS signals on different tomographic planes at different tomographic epochs and also for discretization of the tomographic fields based on the perimeter of the tomographic boundary on the plane and meshing techniques is proposed. The new approach was tested using three stations from the Hong Kong GNSS network and validated by comparing the tomographic results against radiosonde data from King's Park Meteorological Station (HKKP) during the one month period of May 2015. Results indicated that the new approach is feasible for a three-station GNSS network tomography. This is significant due to the fact that the conventional approaches cannot even solve a network tomography from a few stations.
A new global grid-based weighted mean temperature model considering vertical nonlinear variation
Publisher: Copernicus GmbH
Date: 31-03-2021
Abstract: Abstract. Global navigation satellite systems (GNSS) have been proved to be an excellent technology for retrieving precipitable water vapor (PWV). In GNSS meteorology, PWV at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals received at the station using a conversion factor which is a function of weighted mean temperature (Tm) along the vertical direction in the atmosphere over the site. Thus, the accuracy of Tm directly affects the quality of the GNSS-derived PWV. Currently, the Tm value at a target height level is commonly modeled using the Tm value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in Tm is neglected. This may result in large errors in the Tm result for the target height level, as the variation trend in the vertical direction of Tm may not be linear. In this research, a new global grid-based Tm empirical model with a horizontal resolution of 1∘ × 1∘ , named GGNTm, was constructed using ECMWF ERA5 monthly mean reanalysis data over the 10-year period from 2008 to 2017. A three-order polynomial function was utilized to fit the vertical nonlinear variation in Tm at the grid points, and the temporal variation in each of the four coefficients in the Tm fitting function was also modeled with the variables of the mean, annual, and semi-annual litudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted Tm values in 2018 to compare with the following two references in the same year: (1) Tm from ERA5 hourly reanalysis with the horizontal resolution of 5∘ × 5∘ (2) Tm from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model-predicted Tm values over all global grid points at the 950 and 500 hPa pressure levels were 3.35 and 3.94 K, respectively. Compared to the second reference, the mean bias and mean RMSE of the model-predicted Tm values over the 428 radiosonde stations at the surface level were 0.34 and 3.89 K, respectively the mean bias and mean RMSE of the model's Tm values over all pressure levels in the height range from the surface to 10 km altitude were −0.16 and 4.20 K, respectively. The new model results were also compared with that of the GTrop and GWMT_D models in which different height correction methods were also applied. Results indicated that significant improvements made by the new model were at high-altitude pressure levels in all five height ranges, GGNTm results were generally unbiased, and their accuracy varied little with height. The improvement in PWV brought by GGNTm was also evaluated. These results suggest that considering the vertical nonlinear variation in Tm and the temporal variation in the coefficients of the Tm model can significantly improve the accuracy of model-predicted Tm for a GNSS receiver that is located anywhere below the tropopause (assumed to be 10 km), which has significance for applications requiring real-time or near real-time PWV converted from GNSS signals.
Dynamic statistical optimization of GNSS radio occultation bending angles: an advanced algorithm and its performance analysis
Publisher: Copernicus GmbH
Date: 22-01-2015
Abstract: Abstract. We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS) based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically-varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAMP and COSMIC measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction in random errors (standard deviations) of optimized bending angles down to about two-thirds of their size or more (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data (3) improved retrieval of refractivity and temperature profiles (4) produces realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well characterized and high-quality atmospheric profiles over the entire stratosphere.
New Models for Vertical Distribution and Variation of Tropospheric Water Vapor—A Case Study for China
Publisher: MDPI AG
Date: 05-12-2022
Abstract: For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively.
Seasonal Multifactor Modelling of Weighted-Mean Temperature for Ground-Based GNSS Meteorology in Hunan, China
Publisher: Hindawi Limited
Date: 2017
DOI: 10.1155/2017/3782687
Abstract: In this study, radiosonde observations during the period of 2012-2013 from three stations in the Hunan region, China, were used to establish regional T m models (RTMs) that are a fitting function of multiple meteorological factors ( T s , E s , and P s ). One-factor, two-factor, and three-factor RTMs were assessed by comparing their T m against the radiosonde-derived T m (as the truth) during the period of 2013-2014. Statistical results showed that the bias and RMS of the one-factor RTM, in comparison to the BTM result, were reduced by 88% and 28%, respectively. The two-factor and three-factor RTMs showed similar accuracy and both outperformed the one-factor RTM, with an improvement of 7% in RMS. The bias and RMS of all the four seasonal two-factor RTMs were smaller than the yearly two-factor RTM, with the improvements of 3%, 10%, 2%, and 3% in RMS. The improvement of the conversion factors in mean bias and RMS resulting from the seasonal two-factor RTM is 92% and 31%. The bias and RMS of the PWV resulting from the seasonal two-factor RTM are improved by 37% and 12%, respectively. Therefore, the seasonal two-factor RTMs are recommended for the research and applications of GNSS meteorology in the Hunan region, China.
Forecasting of the Thermosphere via Assimilating Electron Density and Temperature Data
Publisher: Wiley
Date: 14-05-2019
Sea Surface Green Algae Density Estimation Using Ship-Borne GEO-Satellite Reflection Observations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Analysis of ionospheric structure influences on residual ionospheric errors in GNSS radio occultation bending angles based on ray tracing simulations
Publisher: Copernicus GmbH
Date: 26-04-2018
Abstract: Abstract. The Global Navigation Satellite System (GNSS) radio occultation (RO) technique is widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source to RO at upper stratospheric altitudes, and a linear dual-frequency bending angle correction is commonly used to remove the first-order ionospheric effect. However, the higher-order residual ionospheric error (RIE) can still be significant, so it needs to be further mitigated for high-accuracy applications, especially from 35 km altitude upward, where the RIE is most relevant compared to the decreasing magnitude of the atmospheric bending angle. In a previous study we quantified RIEs using an ensemble of about 700 quasi-realistic end-to-end simulated RO events, finding typical RIEs at the 0.1 to 0.5 µrad noise level, but were left with 26 exceptional events with anomalous RIEs at the 1 to 10 µrad level that remained unexplained. In this study, we focused on investigating the causes of the high RIE of these exceptional events, employing detailed along-ray-path analyses of atmospheric and ionospheric refractivities, impact parameter changes, and bending angles and RIEs under asymmetric and symmetric ionospheric structures. We found that the main causes of the high RIEs are a combination of physics-based effects – where asymmetric ionospheric conditions play the primary role, more than the ionization level driven by solar activity – and technical ray tracer effects due to occasions of imperfect smoothness in ionospheric refractivity model derivatives. We also found that along-ray impact parameter variations of more than 10 to 20 m are possible due to ionospheric asymmetries and, depending on prevailing horizontal refractivity gradients, are positive or negative relative to the initial impact parameter at the GNSS transmitter. Furthermore, mesospheric RIEs are found generally higher than upper-stratospheric ones, likely due to being closer in tangent point heights to the ionospheric E layer peaking near 105 km, which increases RIE vulnerability. In the future we will further improve the along-ray modeling system to fully isolate technical from physics-based effects and to use it beyond this work for additional GNSS RO signal propagation studies.
A new approach for GNSS tomography from a few GNSS stations
Publisher: Copernicus GmbH
Date: 26-03-2018
DOI: 10.5194/AMT-2017-426
Abstract: Abstract. The determination of the distribution of water vapor in the atmosphere plays an important role in the atmospheric monitoring. Global Navigation Satellite Systems (GNSS) tomography can be used to construct 3D distribution of water vapor over the field covered by a GNSS network with high temporal and spatial resolutions. In current tomographic approaches, a pre-set fixed rectangular field that roughly covers the area of the distribution of the GNSS signals on the top plane of the tomographic field is commonly used for all tomographic epochs. Due to too many unknown parameters needing to be estimated, the accuracy of the tomographic solution degrades. Another issue of these approaches is their unsuitability for GNSS networks with a few stations as the shape of the field covered by the GNSS signals is in fact roughly an upside-down cone rather than the rectangular cube as the pre-set. In this study, a new approach for determination of tomographic fields fitting the real distribution of GNSS signals on different tomographic planes at different tomographic epochs and also for discretization of the tomographic fields based on the perimeter of the tomographic boundary on the plane and meshing techniques is proposed. The new approach was tested using three stations from the Hong Kong GNSS network and validated by comparing the tomographic results against radiosonde data from King's Park Meteorological Station (HKKP) during the one month period of May, 2015. Results indicated that the new approach is feasible for a three-station GNSS network tomography. This is significant due to the fact that the conventional approaches cannot even solve a few stations network tomography.
Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm
Publisher: MDPI AG
Date: 25-08-2020
DOI: 10.3390/RS12172744
Abstract: Global Navigation Satellite Systems (GNSS) tomography plays an important role in the monitoring and tracking of the tropospheric water vapor. In this study, a new approach for improving the node-based GNSS tomography is proposed, which makes a trade-off between the real observed region and the complexity of the discretization of the tomographic region. To obtain dynamically the approximate observed region, the convex hull algorithm and minimum bounding box algorithm are used at each tomographic epoch. This new approach can dynamically define the tomographic model for all types of study areas based on the GNSS data. The performance of the new approach is tested by comparing it against the common node-based GNSS tomographic approach. Test data in May 2015 are obtained from the Hong Kong GNSS network to build the tomographic models and the radiosonde data as a reference are used for validating the quality of the new approach. The experimental results show that the root-mean-square errors of the new approach, in most cases, have a 38 percent improvement and the values of standard deviation reduce to over 43 percent compared with the common approach. The results indicate that the new approach is applicable to the node-based GNSS tomography.
Application of the locality principle to radio occultation studies of the Earth's atmosphere and ionosphere
Publisher: Copernicus GmbH
Date: 17-07-2015
Abstract: Abstract. A new formulation of the previously introduced principle of locality is presented. The principle can be applied for modernization of the radio occultation (RO) remote sensing of the atmospheres and ionospheres of the Earth and other planets. The principle states that significant contributions to variations of the intensity and phase of the radio waves passing through a layered medium are connected with influence of the vicinities of tangential points where the refractivity gradient is perpendicular to the radio ray trajectory. The RO method assumes spherical symmetry of the investigated medium. In this case, if location of a tangent point relative to the spherical symmetry centre is known, the time derivatives of the RO signal phase and Doppler frequency variations can be recalculated into the refractive attenuation. Several important findings are consequences of the locality principle: (i) if position of the centre of symmetry is known, the total absorption along the ray path can be determined at a single frequency (ii) in the case of low absorption the height, displacement from the radio ray perigee, and tilt of the inclined ionospheric (atmospheric) layers can be evaluated (iii) the contributions of the layered and irregular structures in the RO signal can be separated and parameters of layers and turbulence can be measured at a single frequency using joint analysis of the intensity and phase variations. Specially for the Earth's troposphere, the altitude distributions of the weak total absorption (about of 1–4 db) of the radio waves at GPS frequencies corresponding to possible influence of the oxygen, water vapour, and hydrometeors can be measured with accuracy of about 0.1 db at a single frequency. In accordance with the locality principle, a new index of ionospheric activity is introduced. This index is measured from the phase variations of radio waves passing through the ionosphere. Its high correlation with the S4 scintillation index is established. This correlation indicates the significant influence of locally spherical symmetric ionospheric layers on variations of the phase and intensity of the RO signal passing through transionospheric communication links. Obtained results expand applicable domain of the RO method as a powerful remote sensing technique for geophysical and meteorological research.
A BDS-2/BDS-3 Integrated Method for Ultra-Rapid Orbit Determination with the Aid of Precise Satellite Clock Offsets
Publisher: MDPI AG
Date: 25-07-2019
DOI: 10.3390/RS11151758
Abstract: The accuracy of ultra-rapid orbits is a key parameter for the performance of GNSS (Global Navigation Satellite System) real-time or near real-time precise positioning applications. The quality of the current BeiDou demonstration system (BDS) ultra-rapid orbits is lower than that of GPS, especially for the new generational BDS-3 satellites due to the fact that the availability of the number of ground tracking stations is limited, the geographic distribution of these stations is poor, and the data processing strategies adopted are not optimal. In this study, improved data processing strategies for the generation of ultra-rapid orbits of BDS-2/BDS-3 satellites are investigated. This includes both observed and predicted parts of the orbit. First, the predicted clock offsets are taken as constraints in the estimation process to reduce the number of the unknown parameters and improve the accuracy of the parameter estimates of the orbit. To obtain more accurate predicted clock offsets for the BDS’ orbit determination, a denoising method (also called the Tikhonov regularization algorithm), inter-satellite correlation, and the partial least squares method are all incorporated into the clock offsets prediction model. Then, the Akaike information criterion (AIC) is used to determine the arc length in the estimation models by taking the optimal arc length in the estimation of the initial orbit states into consideration. Finally, a number of experiments were conducted to evaluate the performance of the ultra-rapid orbits resulting from the proposed methods. Results showed that: (1) Compared with traditional models, the accuracy improvement of the predicted clock offsets from the proposed methods were 40.5% and 26.1% for BDS-2 and BDS-3, respectively (2) the observed part of the orbits can be improved 9.2% and 5.0% for BDS-2 and BDS-3, respectively, by using the predicted clock offsets as constraints (3) the accuracy of the predicted part of the orbits showed a high correlation with the AIC value, and the accuracy of the predicted orbits could be improved up to 82.2%. These results suggest that the approaches proposed in this study can significantly enhance the accuracy of the ultra-rapid orbits of BDS-2/BDS-3 satellites.
Determination of zenith hydrostatic delays and the development of new global long-term GNSS-derived precipitable water vapor
Publisher: Copernicus GmbH
Date: 16-08-2016
DOI: 10.5194/AMT-2016-264
Abstract: Abstract. Surface pressure is a vital meteorological variable for the accurate determination of precipitable water vapor (PWV) using Global Navigation Satellite Systems (GNSS). The lack of pressure observations is a big issue for the study of climate using historical GNSS observations, which is a relatively new area of GNSS applications in climatology. Hence the use of the surface pressure derived from either an empirical model (e.g. Global Pressure and Temperature 2 wet, GPT2w) or a global atmospheric reanalysis (e.g. ERA-Interim) becomes an important alternative solution. In this study, pressure derived from these two methods is compared against the pressure observed at 108 global GNSS stations for the period 2000–2013. Results show that a good accuracy is achieved from the GPT2w-derived pressure in the latitude band of −30 to 30° and the average value of Root-Mean-Square (RMS) errors across all the stations in this region is 2.4 mb. Correspondingly, an error of 5.6 mm and 1.0 mm in its resultant zenith hydrostatic delay (ZHD) and PWV is expected. In addition, GPT2w-derived pressure usually has a larger error in the cold season due to large diurnal ranges, which is not considered in the GPT2w model. The average value of the RMS errors of the ERA-Interim-derived pressure across all the 108 stations is 1.1 mb, which will lead to an equivalent error of 2.5 mm and 0.4 mm in its resultant ZHD and PWV respectively. Our research also indicates that the ERA-Interim-derived pressure has the potential to be used as a useful meteorological data source to obtain high accuracy PWV on a global scale for climate studies and the GPT2w-derived pressure can be potentially used for climatology as well although it may be only suitable for the tropical regions.
An Improved Principal Component Analysis Method for the Interpolation of Missing Data in GNSS-Derived PWV Time Series
Publisher: MDPI AG
Date: 28-10-2023
DOI: 10.3390/RS15215153
Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor
Publisher: Copernicus GmbH
Date: 07-08-2017
Abstract: Abstract. Surface pressure is a necessary meteorological variable for the accurate determination of integrated water vapor (IWV) using Global Navigation Satellite System (GNSS). The lack of pressure observations is a big issue for the conversion of historical GNSS observations, which is a relatively new area of GNSS applications in climatology. Hence the use of the surface pressure derived from either a blind model (e.g., Global Pressure and Temperature 2 wet, GPT2w) or a global atmospheric reanalysis (e.g., ERA-Interim) becomes an important alternative solution. In this study, pressure derived from these two methods is compared against the pressure observed at 108 global GNSS stations at four epochs (00:00, 06:00, 12:00 and 18:00 UTC) each day for the period 2000–2013. Results show that a good accuracy is achieved from the GPT2w-derived pressure in the latitude band between −30 and 30° and the average value of 6 h root-mean-square errors (RMSEs) across all the stations in this region is 2.5 hPa. Correspondingly, an error of 5.8 mm and 0.9 kg m−2 in its resultant zenith hydrostatic delay (ZHD) and IWV is expected. However, for the stations located in the mid-latitude bands between −30 and −60° and between 30 and 60°, the mean value of the RMSEs is 7.3 hPa, and for the stations located in the high-latitude bands from −60 to −90° and from 60 to 90°, the mean value of the RMSEs is 9.9 hPa. The mean of the RMSEs of the ERA-Interim-derived pressure across at the selected 100 stations is 0.9 hPa, which will lead to an equivalent error of 2.1 mm and 0.3 kg m−2 in the ZHD and IWV, respectively, determined from this ERA-Interim-derived pressure. Results also show that the monthly IWV determined using pressure from ERA-Interim has a good accuracy − with a relative error of better than 3 % on a global scale thus, the monthly IWV resulting from ERA-Interim-derived pressure has the potential to be used for climate studies, whilst the monthly IWV resulting from GPT2w-derived pressure has a relative error of 6.7 % in the mid-latitude regions and even reaches 20.8 % in the high-latitude regions. The comparison between GPT2w and seasonal models of pressure–ZHD derived from ERA-Interim and pressure observations indicates that GPT2w captures the seasonal variations in pressure–ZHD very well.
Adaptive Voxel-Based Model for the Dynamic Determination of Tomographic Region
Publisher: MDPI AG
Date: 13-01-2023
DOI: 10.3390/RS15020492
Abstract: Water vapor is a dominant greenhouse gas. It significantly impacts the atmosphere by trapping heat and infrared radiation. The greenhouse effect is essential for life on Earth but can also be harmful. Although the amount of water vapor in the atmosphere is not much during the water cycle, it is the most active element in rapid changes in both spatial and temporal domains. GNSS tomography’s ability to model the high-resolution 3D distribution of water vapor is a promising means of measuring and monitoring the spatial-temporal variation of water vapor. This study developed and tested a new GNSS tomographic model using adaptive voxel parameterization. It uses a 3D traversal algorithm to dynamically determine the position of voxels at each tomographic s ling epoch. It means that the new algorithm can exclude the voxels that no GNSS signals pass through, reducing the influence of such voxels in the construction of the tomographic model. This study provides a new approach to investigating the inversion of atmospheric water vapor. The experiment used one-month data from the Hong Kong network in September 2020, and the results were compared with the general system. The local radiosonde data is a reference for verification of the two approaches. The mean root-mean-square error (RMSE) and IQR of the water vapor profiles derived from AAR are decreased by 55% and 48% with respect to the GFR results, respectively. The results show that the accuracy of the new method outperforms the general approach in the result statistics. The successful implementation of the research has significant potential to drive the development of GNSS tomography in the study of weather and climate change.
Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery
Publisher: MDPI AG
Date: 30-09-2022
DOI: 10.3390/RS14194887
Abstract: The timely and accurate detection of wheat lodging at a large scale is necessary for loss assessments in agricultural insurance claims. Most existing deep-learning-based methods of wheat lodging detection use data from unmanned aerial vehicles, rendering monitoring wheat lodging at a large scale difficult. Meanwhile, the edge feature is not accurately extracted. In this study, a semantic segmentation network model called the pyramid transposed convolution network (PTCNet) was proposed for large-scale wheat lodging extraction and detection using GaoFen-2 satellite images with high spatial resolutions. Multi-scale high-level features were combined with low-level features to improve the segmentation’s accuracy and to enhance the extraction sensitivity of wheat lodging areas in the proposed model. In addition, four types of vegetation indices and three types of edge features were added into the network and compared to the increment in the segmentation’s accuracy. The F1 score and the intersection over union of wheat lodging extraction reached 85.31% and 74.38% by PTCNet, respectively, outperforming other compared benchmarks, i.e., SegNet, PSPNet, FPN, and DeepLabv3+ networks. PTCNet can achieve accurate and large-scale extraction of wheat lodging, which is significant in the fields of loss assessment and agricultural insurance claims.
Monitoring the Migration of Water Vapor Using Ground-Based GNSS Tropospheric Products
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Long-Term Subsidence in Lava Fields at Piton de la Fournaise Volcano Measured by InSAR: New Insights for Interpretation of the Eastern Flank Motion
Publisher: MDPI AG
Date: 12-04-2018
DOI: 10.3390/RS10040597
Abstract: Long-term deformation often occurs in lava fields at volcanoes after flow emplacements. The investigation and interpretation of deformation in lava fields is one of the key factors for the assessment of volcanic hazards. As a typical Hawaiian volcano, Piton de la Fournaise volcano’s (La Réunion Island, France) main eruptive production is lava. Characteristics of the lava flows at Piton de la Fournaise, including the geometric parameters, location, and elevation, have been investigated by previous studies. However, no analysis focusing on the long-term post-emplacement deformation in its lava fields at a large spatial extent has yet been performed. One of the previous studies revealed that the post-emplacement lava subsidence played a role in the observed Eastern Flank motion by conducting a preliminary investigation. In this paper, an InSAR time series analysis is performed to characterize the long-term deformation in lava fields emplaced between 1998 and 2007 at Piton de la Fournaise, and to conduct an in-depth investigation over the influence of post-emplacement lava subsidence processes on the instability of the Eastern Flank. Results reveal an important regional difference in the subsidence behavior between the lava fields inside and outside of the Eastern Flank Area (EFA), which confirms that, in addition to the post-lava emplacement processes, other processes must have played a role in the observed subsidence in the EFA. The contribution of other processes is estimated to be up to ~78%. The spatial variation of the observed displacement in the EFA suggests that a set of active structures (like normal faults) could control a slip along a pre-existing structural discontinuity beneath the volcano flank. This study provides essential insights for the interpretation of the Eastern Flank motion of Piton de la Fournaise.
An Improved Model for Detecting Heavy Precipitation Using GNSS-Derived Zenith Total Delay Measurements
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Supplementary material to "A new global grid-based weighted mean temperature model considering vertical nonlinear variation"
Publisher: Copernicus GmbH
Date: 07-10-2020
Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong
Publisher: MDPI AG
Date: 27-12-2019
DOI: 10.3390/RS12010104
Abstract: Typhoons can be serious natural disasters for the sustainability and development of society. The development of a typhoon usually involves a pre-existing weather disturbance, warm tropical oceans, and a large amount of moisture. This implies that a large variation in the atmospheric water vapor over the path of a typhoon can be used to study the characteristics of the typhoon. This is the reason that the variation in precipitable water vapor (PWV) is often used to capture the signature of a typhoon in meteorology. This study investigates the usability of real-time PWV retrieved from global navigation satellite systems (GNSS) for typhoons’ characterizations, and especially, the following aspects were investigated: (1) The correlation between PWV and atmospheric parameters including pressure, temperature, precipitation, and wind speed (2) water vapor transportation during a typhoon period and (3) the correlation between the movement of a typhoon and the transportation of water vapor. The case study selected for this research was Super Typhoon Mangkhut that occurred in mid-September 2018 in Hong Kong. The PWV time series were obtained from a conversion of GNSS-derived zenith total delays (ZTDs) using observations at 10 stations selected from the Hong Kong GNSS continuously operating reference stations (CORS) network, which are also located along the path of the typhoon. The Bernese GNSS Software (ver. 5.2) was used to obtain the ZTDs and the root mean square (RMS) of the differences between the GNSS-ZTDs and International GNSS Service post-processed ZTDs time series was less than 8 mm. The RMS of the differences between the GNSS-PWVs (i.e., the ZTDs converted PWVs) and radiosonde-derived PWVs (RS-PWVs) time series was less than 2 mm. The changes in PWV reflect the variation in wind speed during the typhoon period to a certain degree, and their correlation coefficient was 0.76, meaning a significant positive correlation. In addition, a new approach was proposed to estimate the direction and speed of a typhoon’s movement using the time difference of PWV arrival at different sites. The direction and speed estimated agreed well with the ones published by the China Meteorological Administration. These results suggest that GNSS-derived PWV has a great potential for the monitoring and even prediction of typhoon events, especially for near real-time warnings.
A New Method for Refining the GNSS-Derived Precipitable Water Vapor Map
Publisher: MDPI AG
Date: 08-02-2019
DOI: 10.3390/S19030698
Abstract: Abstract: The objective of the study was to put forth an interpolation method (the LZ method) for refining the GNSS-derived precipitable water vapor (PWV) map. We established a regional weighted mean temperature (Tm) model for this experiment, which introduced a minor difference into the resultant GNSS-derived PWV compared to the previous Tm models. The kernel of the LZ method consists of increasing the s le density via the virtual s le points. These virtual s le points originated from the digital elevation model (DEM) were constructed on the basis of the statistically significant correlation between PWV and geographical location (i.e., geographical coordinates and elevation). The LZ method was validated and compared to the conventional interpolation approach only accounting for the original s le points. The results reflect that the PWV maps generated by the LZ method showed more details than through conventional one. In addition, the prediction performance of the LZ method was better than that of the conventional method by using cross-validation.
Correction: Chen, Y., et al. Long-Term Subsidence in Lava Fields at the Piton de la Fournaise Volcano Measured by InSAR: New Insights for Interpretation of the Eastern Flank Motion. Remote Sens. 2018,
Publisher: MDPI AG
Date: 25-12-2018
DOI: 10.3390/RS11010030
Abstract: The authors wish to make the following corrections to this paper [...]
Improved tracklet association for space objects using short-arc optical measurements
Publisher: Elsevier BV
Date: 10-2018
The Impact of Solar Activity on Forecasting the Upper Atmosphere via Assimilation of Electron Density Data
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-10756
Abstract: & & We present a comprehensive comparison of the impact of solar activity& on forecasting the ionosphere and thermosphere. Here we investigate the& response of physics-based TIE-GCM (thermosphere-ionosphere-electrodynamics general circulation model) in a data assimilation scheme through& assimilating radio occultation (RO)-derived electron density (Ne) using an ensemble Kalman filter (KF). Constellation observations of Ne& rofiles offer opportunities to assess the accuracy of the model forecasted state on a global scale. In this study, we emphasise the& importance of understanding how the assimilation results vary with solar activity, which is one of the primary drivers of thermosphere-ionosphere dynamics.& & & & We validate the assimilation results with independent RO-derived GRACE& (Gravity Recovery and Climate Experiment mission) Ne data. The main& result is that the forecast Ne agree best with data during the solar& minimum compared to solar maximum. The results also show that the& assimilation scheme significantly adjusts both the nowcast and forecast& states during the two solar activity periods. The results show that TIE-GCM significantly underestimate Ne in low altitudes below 250 km& and the assimilation of Ne is not as effective in these lower altitudes compared to higher altitudes. The results demonstrate that assimilation& of Ne significantly impacts the neutral mass density estimates via the KF state vector. This impact is larger during solar maximum than solar& minimum relative to a control run. The results also demonstrate that the impact of assimilation of Ne on neutral mass density state persists& through to forecast state better during solar minimum compared to solar& maximum. The results are useful to explain the inherent model bias, to& understand the limitations of the data, and to demonstrate the capability of the assimilation technique.& &
Monitoring Land Surface Displacement over Xuzhou (China) in 2015–2018 through PCA-Based Correction Applied to SAR Interferometry
Publisher: MDPI AG
Date: 24-06-2019
DOI: 10.3390/RS11121494
Abstract: Land surface deformation in metropolitan areas, which can cause varying degrees of hazard to both human lives and to properties, has been documented for decades in cities worldwide. Xuzhou, is one of the most important energy and industrial bases in eastern China, and has experienced significant land subsidence due to both excessive extraction of karst underground water and exploitation of mineral resources in recent decades. Furthermore, Xuzhou has recently undergone rapid urbanization in terms of urban expansion and underground construction, which could induce additional pressure on the urban land surface. However, most previous research on land surface deformation in the Xuzhou urban areas has been conducted based on traditional ground-based deformation monitoring techniques with sparse measurements. Little is known about the regional spatiotemporal behavior of land surface displacement in Xuzhou. In this study, a detailed interferometric synthetic aperture radar (InSAR) time series analysis was performed to characterize the spatial pattern and temporal evolution of land surface deformation in central areas of Xuzhou during 2015–2018. A method based on principal component analysis was adopted to correct artifacts in the InSAR signal. Results showed the correction strategy markedly reduced the discrepancy between global navigation satellite systems and InSAR measurements. Noticeable land subsidence (−5 to −41 mm/yr) was revealed widely within the Xuzhou urban areas, particularly along subway lines under construction, newly developed districts, and in old coal goafs. Remarkable consistent land uplift (up to +25 mm/yr) was found to have significantly affected two long narrow areas within the old goafs since 2015. The possible principal influencing factors contributing to the land surface displacements such as subway tunneling, building construction, mining, underground water levels and geological conditions are then discussed.
Ionospheric Phase Scintillation Index Estimation Based on 1 Hz Geodetic GNSS Receiver Measurements by Using Continuous Wavelet Transform
Publisher: American Geophysical Union (AGU)
Date: 04-2022
DOI: 10.1029/2021SW003015
Abstract: The adverse effect of the ionospheric scintillation on Global Navigation Satellite System (GNSS) requires scintillation monitoring on a global scale. Ionospheric Scintillation Monitoring Receivers (ISMR) are usually adopted to monitor scintillation, while they are not suitable for global monitoring due to the 50 Hz data collecting rate, which restricts the distribution. This paper proposes a new method to extract the phase scintillation index from each GNSS carrier with 1s‐s ling‐interval, mainly based on the cycle slip detection, the geodetic detrending and the wavelet transform, in which the optimal symmetry parameter and the time‐bandwidth product are determined with trial calculation. Taken the index provided by ISMR as the reference, 1‐year observations are utilized to evaluate the scintillation monitoring performance of the extracted index regarding the correlation of the magnitude in each observation arc, the detected daily scintillation occurrence rate, the diurnal variation pattern of the ionospheric scintillation, the correlation between the scintillation occurrence rate and the space weather parameter, and the complementary cumulative distribution of the magnitudes. Compared to the performance of Rate of Total electron content Index, a higher consistency can be achieved between the extracted index and the index, indicating the rationality of applying the proposed method in monitoring scintillations. The extracted scintillation index can be expected to introduce geodetic receivers operating at 1s‐s ling‐interval into the field of ionospheric scintillation monitoring on a global scale.
Haze Optical Properties from Long-Term Ground-Based Remote Sensing over Beijing and Xuzhou, China
Publisher: MDPI AG
Date: 26-03-2018
DOI: 10.3390/RS10040518
Abstract: Aerosol haze pollution has had a significant impact on both global climate and the regional air quality of Eastern China, which has a high proportion of high level pollution days. Statistical analyses of aerosol optical properties and direct radiative forcing at two AERONET sites (Beijing and Xuzhou) were conducted from 2013 to 2016. Results indicate: (1) Haze pollution days accounted for 26% and 20% of days from 2013 to 2016 in Beijing and Xuzhou, respectively, with the highest proportions in winter (2) The averaged aerosol optical depth (AOD) at 550 nm on haze days were about 3.7 and 1.6 times greater than those on clean days in Beijing and Xuzhou, respectively. At both sites, the maximum AOD occurred in summer (3) Hazes were dominated by fine particles at both sites. However, as compared to Xuzhou, Beijing had larger coarse mode AOD and higher percentage of small α. This data, together with an analysis of size distribution, suggests that the hazes in Beijing were more susceptible to coarse dust particles than Xuzhou (4) During hazes in Beijing, the single scattering albedo (SSA) is significantly higher when compared to clean conditions (0.874 vs. 0.843 in SSA440 nm), an increase much less evident in Xuzhou. The most noticeable differences in both SSA and the imaginary part of the complex refractive index between Beijing and Xuzhou were found in winter (5) In Beijing, the haze radiative forcing produced an averaged cooling effect of −113.6 ± 63.7 W/m2 at the surface, whereas the averaged heating effect of 77.5 ± 49.7 W/m2 within the atmosphere was at least twice as strong as clean days. In Xuzhou, such a radiative forcing effect appeared to be much smaller and the difference between haze and clean days was insignificant. Derived from long-term observation, these findings are more significant for the improvement of our understanding of haze formation in China and the assessment of its impacts on radiative forcing of climate change than previous short-term case studies.
A new global grid-based weighted mean temperature model considering vertical nonlinear variation
Publisher: Copernicus GmbH
Date: 07-10-2020
DOI: 10.5194/AMT-2020-274
Abstract: Abstract. Global Navigation Satellite Systems (GNSS) have been proved to be an excellent technology for retrieving precipitable water vapor (PWV). In GNSS meteorology, PWV at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals received at the station using a conversion factor which is a function of weighted mean temperature (Tm) along the vertical direction in the atmosphere over the site. Thus, the accuracy of Tm directly affects the quality of the GNSS-derived PWV. Currently, the Tm value at a target height level is commonly modelled using the Tm value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in Tm is neglected. This may result in large errors in the Tm result for the target height level, as the variation trend in the vertical direction of Tm may not be linear. In this research, a new global grid-based Tm empirical model with a horizontal resolution of 1°×1°, named GGNTm, was constructed using ECMWF ERA5 monthly mean reanalysis data over the 10-year period from 2008 to 2017. A three-order polynomial function was utilized to fit the vertical nonlinear variation in Tm at the grid points, and the temporal variation in each of the four coefficients in the Tm fitting function was also modelled with the variables of the mean, annual and semi-annual litudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted Tm values in 2018 to compare with the following two references in the same year 1) Tm from ERA5 hourly reanalysis with the horizontal resolution of 5°×5° 2) Tm from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model predicted Tm values over all global grid points at the 950 hPa and 500 hPa pressure levels were 3.35 K and 3.94 K respectively. Compared to the second reference, the mean bias and mean RMSE of the model predicted Tm values over the 428 radiosonde stations at the surface level were 0.34 K and 3.89 K respectively the mean bias and mean RMSE of the model’s Tm values at all pressure levels in the height range from the surface to 10 km altitude were −0.16 K and 4.20 K respectively. The new model results were also compared with that of the GPT3, GTrop and GWMT_D models in which different height correction methods were also applied. Results indicated that significant improvements made by the new model were at high-altitude pressure levels in all five height ranges, GGNTm results were generally unbiased, and their accuracy varied little with height. The impact of Tm on GNSS-PWV was evaluated in terms of relative error, and significant improvement was found compared to the widely used GPT3 model. These results suggest that considering the vertical nonlinear variation in Tm and the temporal variation in the coefficients of the Tm model can significantly improve the accuracy of model-predicted Tm for a GNSS receiver that is located in anywhere below the tropopause (assumed to be 10 km), which has significance for applications needing real-time or near real-time PWV converted from GNSS signals.
Influence of station density and multi-constellation GNSS observations on troposphere tomography
Publisher: Copernicus GmbH
Date: 02-10-2018
Abstract: Abstract. Troposphere tomography, using multi-constellation GNSS observations, has become a novel approach for the three-dimensional (3-d) reconstruction of water vapour fields. An analysis of the integration of four Global Navigation Satellite Systems (BeiDou, GPS, GLONASS and Galileo) observations is presented to investigate the impact of station density and single/multi-constellation GNSS observations on troposphere tomography. Additionally, the optimal horizontal resolution of research area is determined in Hong Kong, which considers both the number of voxels ided, and the coverage rate of discretized voxels penetrated by satellite signals. Tomography experiment reveals that the influence of station density in a GNSS network is more significant than the multi-constellation GNSS observations on the reconstruction of 3-d atmospheric water vapour profiles. Compared to the tomographic result from the multi-constellation GNSS (BeiDou, GPS, GLONASS and Galileo) observations, the RMS of SWD residuals derived from the single-GNSS observations has been decreased by 16 % when the data from the other four stations are added. Furthermore, more experiments have been carried out to analyse the contributions of different combined GNSS data to the reconstructed results, and the comparisons show some interesting results: (1) the number of iterations used in determining the weighting matrices of different equations in tomography modelling can be decreased when considering multi-constellation GNSS observations (2) the tomographic result with multi-constellation GNSS data can improve the reconstructed quality of 3-d atmospheric water vapour by the largest RMS value of about 11 % when compared to the PPP-estimated SWD, but this was not as high as was expected.
The Impact of Solar Activity on Forecasting the Upper Atmosphere via Assimilation of Electron Density Data
Publisher: American Geophysical Union (AGU)
Date: 05-2021
DOI: 10.1029/2020SW002660
Abstract: This study presents a comprehensive comparison of the impact of solar activity on forecasting the upper atmosphere through assimilation of radio occultation (RO)‐derived electron density ( Ne ) into a physics‐based model (TIE‐GCM) using an ensemble Kalman filter (KF). Globally abundant RO‐derived Ne offers one of the most promising means to test the effect of assimilation on the model forecasted state on a global scale. This study emphasizes the importance of understanding how the assimilation results vary with solar activity, which is one of the main drivers of thermosphere‐ionosphere dynamics. This study validates the forecast states with independent RO‐derived GRACE (Gravity Recovery and Climate Experiment mission) Ne data. The principal result of the study is that the agreement between forecast Ne and data is better during solar minimum than solar maximum. The results also show that the agreement between data and forecast is mostly better than that of the standalone TIE‐GCM driven with observed geophysical indices. The results emphasize that TIE‐GCM significantly underestimate Ne in altitudes below 250 km and the assimilation of Ne is not as effective in these lower altitudes as it is in higher altitudes. The results demonstrate that assimilation of Ne significantly impacts the neutral mass density estimates via the KF state vector—the impact is larger during solar maximum than solar minimum relative to a control case that does not assimilate Ne . The results are useful to explain the inherent model bias, to understand the limitations of the data, and to demonstrate the capability of the assimilation technique.
Quantifying residual ionospheric errors in GNSS radio occultation bending angles based on ensembles of profiles from end-to-end simulations
Publisher: Copernicus GmbH
Date: 29-07-2015
Abstract: Abstract. The radio occultation (RO) technique using signals from the Global Navigation Satellite System (GNSS), in particular from the Global Positioning System (GPS) so far, is currently widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source in RO measurements at stratospheric altitudes, and a linear ionospheric correction of dual-frequency RO bending angles is commonly used to remove the first-order ionospheric effect. However, the residual ionospheric error (RIE) can still be significant so that it needs to be further mitigated for high-accuracy applications, especially above about 30 km altitude where the RIE is most relevant compared to the magnitude of the neutral atmospheric bending angle. Quantification and careful analyses for better understanding of the RIE is therefore important for enabling benchmark-quality stratospheric RO retrievals. Here we present such an analysis of bending angle RIEs covering the stratosphere and mesosphere, using quasi-realistic end-to-end simulations for a full-day ensemble of RO events. Based on the ensemble simulations we assessed the variation of bending angle RIEs, both biases and standard deviations, with solar activity, latitudinal region and with or without the assumption of ionospheric spherical symmetry and co-existing observing system errors. We find that the bending angle RIE biases in the upper stratosphere and mesosphere, and in all latitudinal zones from low to high latitudes, have a clear negative tendency and a magnitude increasing with solar activity, which is in line with recent empirical studies based on real RO data although we find smaller bias magnitudes, deserving further study in the future. The maximum RIE biases are found at low latitudes during daytime, where they amount to within −0.03 to −0.05 μrad, the smallest at high latitudes (0 to −0.01 μrad quiet space weather and winter conditions). Ionospheric spherical symmetry or asymmetries about the RO event location have only a minor influence on RIE biases. The RIE standard deviations are markedly increased both by ionospheric asymmetries and increasing solar activity and amount to about 0.3 to 0.7 μrad in the upper stratosphere and mesosphere. Taking also into account the realistic observation errors of a modern RO receiving system, amounting globally to about 0.4 μrad (unbiased standard deviation), shows that the random RIEs are typically comparable to the total observing system error. The results help to inform future RIE mitigation schemes that will improve upon the use of the linear ionospheric correction of bending angles and also provide explicit uncertainty estimates.
An Investigation of Winter Wheat Leaf Area Index Fitting Model Using Spectral and Canopy Height Model Data from Unmanned Aerial Vehicle Imagery
Publisher: MDPI AG
Date: 12-10-2022
DOI: 10.3390/RS14205087
Abstract: The leaf area index (LAI) is critical for the respiration, transpiration, and photosynthesis of crops. Color indices (CIs) and vegetation indices (VIs) extracted from unmanned aerial vehicle (UAV) imagery have been widely applied to the monitoring of the crop LAI. However, when the coverage of the crop canopy is large and only spectral data are used to monitor the LAI of the crop, the LAI tends to be underestimated. The canopy height model (CHM) data obtained from UAV-based point clouds can represent the height and canopy structure of the plant. However, few studies have been conducted on the use of the CHM data in the LAI modelling. Thus, in this study, the feasibility of combining the CHM data and CIs and VIs, respectively, to establish LAI fitting models for winter wheat in four growth stages was investigated, and the impact of image resolution on the extraction of remote sensing variables (the CHM data, CIs, and VIs) and on the accuracy of the LAI models was evaluated. Experiments for acquiring remote sensing images of wheat canopies during the four growth stages from the RGB and multispectral sensors carried by a UAV were carried out. The partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVR) were used to develop the LAI fitting models. Results showed that the accuracy of the wheat LAI models can be improved in the entire growth stages by the use of the additional CHM data with the increment of 0.020–0.268 in R2 for three regression methods. In addition, the improvement from the Cis-based models was more noticeable than the Vis-based ones. Furthermore, the higher the spatial resolution of the CHM data, the better the improvement made by the use of the additional CHM data. This result provides valuable insights and references for UAV-based LAI monitoring.
Change Detection in SAR Images Based on Progressive Nonlocal Theory
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Unsupervised Encoder–Decoder Network Under Spatial and Spectral Guidance for Hyperspectral and Multispectral Image Fusion
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Related Organisations
University Of Nottingham
Location: United Kingdom of Great Britain and Northern Ireland
China University Of Mining And Technology School Of Environment Science And Spatial Informatics
Location: China
Institute Of Bei-Star Geospatial Innovations (Nanjing) Pty Ltd
Location: China
Related Funding Activities
No related grants have been discovered for Kefei Zhang.