An Integrated Method for Dynamic Height Error Correction in GNSS-IR Sea Level Retrievals

Snow depth retrieval by using robust estimation algorithm to perform multi-SNR and multi-system fusion in GNSS-IR

Multi-satellite and Multi-Signal-to-Noise Ratio (SNR) types provide more basic data for the monitoring of sea level height by Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. Few studies predict sea level height with high temporal resolution. This study proposes a theory based on GNSS-IR technology and Auto Regressive Integrated Moving Average (ARIMA) model. Taking the MAYG station as an example, the process of sea level height prediction and the determination of the best prediction scheme are shown. First, the sea level height monitored by the GNSS-IR technology is averaged hour by hour as the sea level height at the instantaneous moment. A linear interpolation method is used to compensate for the missing sea level time series. Then, the number of days to participate in the prediction of GNSS-IR sea level height and its forecast are selected for the experiment of predicting tide level. In the tide level prediction study from DOY125 to DOY153, the results of predicting the future 24 h, 48 h, and 72 h from one tide level period (29 days) illustrate that the accuracy of predicting the sea level height at 24 h is the best (R: 0.967, RMSE: 0.270 m, ME: −0.151 m), and the predicted tide level is highly consistent with the tide level measured at the tide gauge station. In addition, experiments reveal that the prediction accuracy gradually decreases with the increase of prediction time. This study verifies the feasibility of the hour-by-hour sea level prediction theory combined with the GNSS-IR technology and the ARIMA model. It proves that the theory can carry out continuous prediction of sea level, further promoting the breakthrough and transformation of GNSS-IR technology to the application level.

Storm surges, characterized by abnormal sea level rises induced by intense meteorological systems such as typhoons and tropical cyclones, pose severe threats to coastal ecosystems, economic activities, and human safety. Accurate and continuous storm surge monitoring is therefore essential. This study presents the first application of inverse modeling of Global Navigation Satellite System (GNSS) Signal-to-Noise Ratio (SNR) data for storm surge monitoring under typhoon events. To address the limitations of inverse modeling under rapidly varying sea states, three targeted improvements are introduced to enhance its robustness and stability. Using the GNSS station HKQT in Hong Kong, China, two representative events—Typhoon Hato (2017) and Typhoon Mangkhut (2018)—are analyzed. Our improved method produces stable and continuous sea level retrievals throughout the typhoon periods. Compared with co-located tide gauge observations, the root-mean-square errors (RMSEs) of sea level retrievals during Hato and Mangkhut are 5.2 cm and 5.4 cm, respectively—representing an average accuracy improvement of approximately 35% relative to Lomb–Scargle Periodogram (LSP) retrievals fitted with the cubic spline. For sea level change rate retrievals, RMSEs are reduced to 0.036 m/h and 0.034 m/h, corresponding to accuracy improvements of approximately 30-50%. Our improved method also achieves superior performance in storm surge peak detection, with an average timing deviation of 23 minutes and an average peak underestimation of 6.4 cm. Atmospheric refraction correction further yields a modest improvement in retrieval accuracy. These results confirm that the improved GNSS SNR inverse modeling is a robust and precise approach for sea level estimation under extreme marine conditions, providing an effective complement to traditional tide gauge networks.

<p>On the 12<sup>th</sup> of November of 2019, flood levels in the Venice Lagoon have reached the mark of 1.87 metres, the second-highest level since records began in 1923. Although a recurrent problem in Venice, the significance of this event have raise awareness of the issue of coastal inundation hazard in Italy, particularly at the highly vulnerable territory of the regions facing the North Adriatic Sea. Several are the processes that contribute to a costal inundation event. On the short term, processes such as high tide and storm surge events can result in sea levels, potentially triggering devastating impacts on human settlements and activities. On the long term, the land subsidence and mean sea level (MSL) changes are important factors; in fact, in some regions such as Jakarta and Bangkok the land is expected to subside by more than 1 meter, while MSL is expected to rise during the next decades, reaching global mean absolute values ranging from 0.3–0.6m (RCP 2.6) to 0.5–1.1m (RCP 8.5) by the end of the century. The combined effect of global sea level rise, local subsidence, and short term phenomena can potentially increase the frequency and intensity of extreme sea levels (ESL), posing a major threat to coastal areas. Currently, almost 700 million people live in low-lying coastal areas, and about 13% of them are exposed to a 100-year flood. In Italy, a territory that is highly vulnerable to coastal flooding are the Regions facing the North Adriatic Sea, mainly due to two factors: the morphological characteristic of this territory, characterised by low-lying areas, and the bathymetry and shape of the Adriatic basin, which cause water level to accumulate and increase rapidly during storm surge events, especially during winter. In this paper, we evaluate two different coastal inundation modelling techniques, one hydrostatic (as part of the EIT Climate-KIC SaferPLACES project) and another hydrodynamic (the ANUGA model), by stressing the models with different ESL, both for the historical mean sea level and for MSL projections at 2050 and 2100. The two different inundation models are tested on three pilot sites particularly vulnerable to coastal flooding located in the North Adriatic Sea: Venice, Cesenatico, and Rimini. We compare our modelling results with existing hazard records and previous hazard and risk assessments. Finally, we apply a flood damage model developed for Italy to estimate the potential economic damages linked to the different flood scenarios, and we calculate the change in expected annual damages according to the relative extreme sea levels.</p>

Assessing changes in mean sea level (MSL) has become increasingly critical due to the significance of climate changes. Soft computing techniques are now widely used to reduce the time and cost associated with traditional MSL estimation methods. Historical MSL data is frequently used to predict future values, yet the application of soft computing models to analyze climate change's impact on MSL remains relatively unexplored. This study aims to develop and compare various soft computing techniques for modeling MSL fluctuations using meteorological data. Random forest (RF), support vector regression (SVR), K-nearest neighbors (KNN) regression, deep neural network (DNN), Gaussian process regression (GPR), and stacked ensemble methods are employed in this study. The newly developed models are statistically assessed for their effectiveness in modeling MSL at Damietta station, Egypt. Variables environmental data such as surface water temperature, pressure, air temperature (average air temperature, dewpoint, wet-bulb, and heat index), and humidity and wind attributes (speed and direction) are utilized and evaluated in modeling MSL. The results indicate that RF, KNN, and GP outperformed other proposed models in modeling MSL during both training and testing phases. The developed weighted stacked ensemble model, integrating RF, KNN, and GPR, outperformed the base models with a correlation coefficient (R) of 0.88 and normalized root mean square error (RMSE) of 0.056 m. MSL modeling at the study station was particularly sensitive to variations in water temperature, wind speed and direction, and atmospheric pressure. This methodology serves as a valuable framework for climate-driven MSL forecasting in developing coastal regions lacking long-term tide records, directly contributing to UNESCO's Ocean Decade Challenge 5 on coastal resilience.

Fine spatial resolution remotely sensed imagery has considerable potential for mapping a shoreline. Although fine spatial resolution imagery typically allows the instantaneous shoreline to be mapped with high accuracy, interest is normally focused on a reference shoreline, defined on a stable vertical datum, which is generally not apparent in the imagery unless acquired at a time carefully coordinated with the tidal characteristics of the region. To map a tide‐coordinated shoreline, such as the mean sea level (MSL), information on terrain topography, bathymetry and tidal characteristics is required. In this study, IKONOS imagery was used to derive topographic and bathymetric information for an extract of the Malaysian coast and combined with a tide chart for the region to map the MSL. The digital elevation model (DEM) derived had a root mean square error (RMSE), calculated on independent control points, of ∼2.2 m while the bathymetric model had an RMSE of 0.87 m. The shoreline derived from the combination of the DEM, bathymetry and tidal information was mapped with an RMSE of 1.8 m.

This research is carried out in the framework of the program Surface Water and Ocean Topography (SWOT) which is a partnership between NASA and CNES. Here, a new hybrid model is implemented for filling gaps and forecasting the hourly sea level variability by combining classical harmonic analyses to high statistical methods to reproduce the deterministic and stochastic processes, respectively. After simulating the mean trend sea level and astronomical tides, the nontidal residual surges are investigated using an autoregressive moving average (ARMA) methods by two ways: (1) applying a purely statistical approach and (2) introducing the SLP in ARMA as a main physical process driving the residual sea level. The new hybrid model is applied to the western Atlantic sea and the eastern English Channel. Using ARMA model and considering the SLP, results show that the hourly sea level observations of gauges with are well reproduced with a root mean square error (RMSE) ranging between 4.5 and 7 cm for 1 to 30 days of gaps and an explained variance more than 80 %. For larger gaps of months, the RMSE reaches 9 cm. The negative and the positive extreme values of sea levels are also well reproduced with a mean explained variance between 70 and 85 %. The statistical behavior of 1-year modeled residual components shows good agreements with observations. The frequency analysis using the discrete wavelet transform illustrate strong correlations between observed and modeled energy spectrum and the bands of variability. Accordingly, the proposed model presents a coherent, simple, and easy tool to estimate the total sea level at timescales from days to months. The ARMA model seems to be more promising for filling gaps and estimating the sea level at larger scales of years by introducing more physical processes driving its stochastic variability.

Background and purposeThe study evaluated the differences in leaf positioning deviations by the log files of three advanced accelerators with two delivery techniques, and established specific assessment parameters of leaf positioning deviations for different types of accelerators.MethodsA total of 420 treatment plans with 5 consecutive treatment log files were collected from the Trilogy, TrueBeam and Halcyon accelerators. Millennium MLC was equipped on the Trilogy and TrueBeam accelerators. A jawless design and dual-layer MLC were adopted on the Halcyon accelerator. 70 IMRT and 70 VMAT plans were selected randomly on each accelerator. The treatment sites of all plans included head and neck, chest, breast, pelvis and other sites. The parsing tasks for 2100 log files were proceeded by SunCheck software from Sun Nuclear Corporation. The maximum leaf root mean square (RMS) errors, 95th percentile errors and percentages of different leaf positioning errors were statistically analyzed. The correlations between these evaluation parameters and accelerator performance parameters (maximum leaf speed, mean leaf speed, gantry and arc angle) were analyzed.ResultsThe average maximum leaf RMS errors of the Trilogy in the IMRT and VMAT plans were 0.44 ± 0.09 mm and 0.79 ± 0.07 mm, respectively, which were higher than the TrueBeam's 0.03 ± 0.01 mm, 0.03 ± 0.01 mm and the Halcyon's 0.05 ± 0.01 mm, 0.07 ± 0.01 mm. Similar data results were shown in the 95th percentile error. The maximum leaf RMS errors were strongly correlated with the 95th percentile errors (Pearson index > 0.5). The leaf positioning deviations in VMAT were higher than those in IMRT for all accelerators. In TrueBeam and Halcyon, leaf position errors above 1 mm were not found in IMRT and VMAT plans. The main influencing factor of leaf positioning deviation was the leaf speed, which has no strong correlation with gantry and arc angles.ConclusionsCompared with the quality assurance guidelines, the MLC positioning deviations tolerances of the three accelerators should be tightened. For both IMRT and VMAT techniques, the 95th percentile error and the maximum RMS error are suggested to be tightened to 1.5 and 1 mm respectively for the Trilogy accelerator. In TrueBeam and Halcyon accelerators, the 95th percentile error and maximum RMS error of 1 and 0.5 mm, respectively, are considered appropriate.

BackgroundThe multileaf collimator (MLC) is a critical component to accurate intensity-modulated radiotherapy (IMRT) delivery. This study examined MLC positional accuracy via MLC logs from Varian machines from six institutions and three delivery techniques to evaluate typical positional accuracy and treatment and mechanical parameters that affect accuracy. Typical accuracy achieved was compared against TG-142 recommendations for MLC performance; more appropriate recommendations are suggested.MethodsOver 85,000 Varian MLC treatment logs were collected from six institutions and analyzed with FractionCHECK. Data were binned according to institution and treatment type to determine overall root mean square (RMS) and 95th percentile error values, and then to look for correlations between those errors and with mechanical and treatment parameters including mean and maximum leaf speed, gantry angle, beam-on time, mean leaf error, and number of segments.ResultsResults of treatment logs found that leaf RMS error and 95th percentile leaf error were consistent between institutions, but varied by treatment type. The step and shoot technique had very small errors: the mean RMS leaf error was 0.008 mm. For dynamic treatments the mean RMS leaf error was 0.32 mm, while volumetric-modulated arc treatment (VMAT) showed an RMS leaf error of 0.46 mm. Most MLC leaf errors were found to be well below TG-142 recommended tolerances. For the dynamic and VMAT techniques, the mean and maximum leaf speeds were significantly linked to the leaf RMS error. Additionally, for dynamic delivery, the mean leaf error was correlated with RMS error, whereas for VMAT the average gantry speed was correlated. For all treatments, the RMS error and the 95th percentile leaf error were correlated.ConclusionsRestricting the maximum leaf speed can help improve MLC performance for dynamic and VMAT deliveries. Furthermore, the tolerances of leaf RMS and error counts for all treatment types should be tightened from the TG-142 values to make them more appropriate for clinical performance. Values of 1 mm for the 95th percentile of leaf RMS error and 1.5 mm for the 95th percentile leaf error are suggested as action levels for all treatment types.

The hydrographic survey reduction using ellipsoid has been available since the advent of the global navigation satellite system (GNSS), with a potential to streamline operation and enhance bathymetric output. Spatially continuous separation surfaces connecting a chart datum (CD) to a geodetic ellipsoid is required for this technique. Universiti Teknologi Malaysia (UTM) has invented a new quasi-seamless separation model for Malaysian waters, known as the Malaysian Vertical Separation (MyVSEP) model, through semi-empirical models to capture the spatial variability of a tidal datum between coastal and offshore areas. A continuous vertical datum is established to develop MyVSEP models by combining the coastal and offshore datasets. The coastal datasets referred to the vertical reference point computed from coastal tide gauges, while the offshore datasets referred to the vertical reference surfaces derived from satellite altimetry. Mean sea level (MSL) or mean sea surface (MSS), mean dynamic topography (MDT), lowest astronomical tide (LAT), and highest astronomical tide (HAT) are the vertical datums involved in developing the continuous MyVSEP model. However, the integration of the vertical datum has only been conducted over the Peninsular Malaysia region. For Sabah and Sarawak, datum integration cannot be implemented due to the limitation of coastal datasets. The assessment of the integrated vertical datum with coastal tide gauges is discussed in this study. The finding shows that the root mean square error (RMSE) agreement between the integrated Universiti Teknologi Malaysia 2020 (iUTM20) model and coastal tide gauges yields below 2.0 cm. The iUTM20 lowest astronomical tide and highest astronomical tide models also show significant improvement compared to the altimetric-derived tidal models, which recorded the root mean square error agreement with coastal tide gauges of 1.8 cm and 2.0 cm, respectively. The development of a continuous vertical separation model for the Ellipsoidally Referenced Surveying technique indirectly optimizes marine geospatial information resources, especially for the National Hydrographic Centre in Malaysia.

Long-term sea level variations in the northern South China Sea (SCS) are known to significantly impact coastal ecosystems and socio-economic activities. To improve sea level prediction accuracy, four models—harmonic analysis and three artificial neural networks (ANNs), namely genetic algorithm-optimized back propagation (GA-BP), radial basis function (RBF), and long short-term memory (LSTM)—are developed and compared using 52 years of observational data (1960–2004). Key evaluation metrics are presented to demonstrate the models’ effectiveness: for harmonic analysis, the root mean square error (RMSE) is reported as 14.73, the mean absolute error (MAE) is 12.61, the mean bias error (MBE) is 0.0, and the coefficient of determination (R2) is 0.84; for GA-BP, the RMSE is measured as 29.1371, the MAE is 24.9411, the MBE is 5.6809, and the R2 is 0.4003; for the RBF neural network, the RMSE is calculated as 27.1433, the MAE is 22.7533, the MBE is 2.1322, and the R2 is 0.4690; for LSTM, the RMSE is determined as 23.7929, the MAE is 19.7899, the MBE is 1.3700, and the R2 is 0.5872. The key findings include the following: (1) A significant sea level rise trend at 1.4 mm/year is observed in the northern SCS. (2) Harmonic analysis is shown to outperform all ANN models in both accuracy and robustness, with sea level variations effectively characterized by four principal and six secondary tidal constituents. (3) Despite their complexity, ANN models (including LSTM) are found to fail in surpassing the predictive capability of the traditional harmonic method. These results highlight the continued effectiveness of harmonic analysis for long-term sea level forecasting, offering critical insights for coastal hazard mitigation and sustainable development planning.

Among the Earth Observation (EO) instruments used in environmental and agricultural applications, the SPOT4-VEGETATION (SPOT4-VGT) instrument allows daily monitoring of terrestrial vegetation cover through remote sensing, at regional and global levels. The characterization of the geometric and radiometric performances of remote sensing data and their temporal stability play an important role in the analysis of EO images and are essential in environmental and agricultural monitoring applications. The validation and characterization of the SPOT4-VGT image geometric performances and temporal stability are investigated in this letter. Three time series of data acquired during 1999, 2000 and 2001 were used for this purpose. These images were downlinked by several VGT L-band receiving stations and processed by the SPACE-VGT software. From analyses made, it can be concluded that the geometric characteristics of the VGT L-band images agree with the VGT International User Committee requirements: the multispectral registration has a root mean square (RMS) error in the range of 0.25±0.17 pixels for B2, B3 and MIR bands, and 0.48±0.21 pixels for B0 band; the multi-temporal registration has a RMS error in the range of 0.38±0.23 pixels; and the absolute geo-location has a RMS error in the range of ±660 m.

Water level forecasting using records of past time series is of importance in water resources engineering and management. For example, water level affects groundwater tables in low-lying coastal areas, as well as hydrological regimes of some coastal rivers. Then, a reliable prediction of sea-level variations is required in coastal engineering and hydrologic studies. During the past two decades, the approaches based on the Genetic Programming (GP) and Artificial Neural Networks (ANN) were developed. In the present study, the GP is used to forecast daily water level variations for a set of time intervals using observed water levels. The measurements from a single tide gauge at Urmia Lake, Northwest Iran, were used to train and validate the GP approach for the period from January 1997 to July 2008. Statistics, the root mean square error and correlation coefficient, are used to verify model by comparing with a corresponding outputs from Artificial Neural Network model. The results show that both these artificial intelligence methodologies are satisfactory and can be considered as alternatives to the conventional harmonic analysis. Keywords—Water-Level variation, Forecasting, Artificial Neural Networks, Genetic Programming, Comparative analysis.

OPINION article Front. Physiol., 22 January 2014Sec. Integrative Physiology Volume 4 - 2013 | https://doi.org/10.3389/fphys.2013.00403

The non-conventional water resources of seawater desalination, wastewater treatment, and stormwater harvesting are promising water resources to enhance the water supply and to cope with the groundwater depletion of the Gaza coastal aquifer (GCA). In total, the current daily operation of the short-term low-volume (STLV) seawater desalination plants produces 36,000 m3 and, on the large-scale perspective, the seawater desalination capacity is planned to increase from 150,000 to 300,000 m3 per day by the years 2025 and 2035, respectively. The wastewater treatment and reuse activities are processed through three wastewater treatment plants with a total daily capacity of 130,000 m3 which is proposed to be increased to a capacity of 235,000 m3 by the beginning of 2025. Stormwater collecting and harvesting supply the water sector by about 550–820 cubic meters per day. The proposed stochastic and artificial intelligence model that was developed in this study to simulate the interactive conditions between the groundwater and the water intervention plan showed proper performance in terms of (r) = 0.95–0.99 and the root mean square error (RMSE) = 0.09–0.21. The model outputs reveal that the annual groundwater abstractions will reach 192 million cubic meters by 2040 with an annual increasing rate of +3%. By applying the model, the optimum utilization of the unconventional water resources contributes positively to the recovery of the GCA, which is experiencing a decline hot spot in the water level that reaches to −19 m below mean sea level (MSL) and is expected to drop to −28 m MSL by 2040. The impact of unconventional water resources interventions was investigated by simulating the water table trend using stochastic models and artificial neural networks (ANNs) through three scenarios. The first scenario, which addresses the non-intervention status, indicates that the groundwater table will decline by −1.5% in the northern governorates and by −51% in the southern governorates of the Gaza Strip within 2020–2040. The second scenario demonstrates the impact of the existing water interventions that revealed an interim recovery in the groundwater balance until 2025 in which the water consumption tended to increase rapidly. The third scenario illustrates the impact of applying the full water management intervention plan in which the depression cone in the groundwater level will be restored by about +10 m.