Reduced Root Mean Square Research Articles

The Effects of Adding TiO2 and CuO Nanoparticles to Fuel on Engine and Hand–Arm Driver Vibrations

Occupant comfort is a key consideration in automobile dynamics, with vibrations potentially causing long-term physical discomfort, especially for drivers. This study investigates the impact of adding TiO2 and CuO nanoparticles to fuel on engine-induced vibrations. Experiments were conducted at various nanoparticle concentrations (0, 50, 100, and 150 ppm) and engine speeds (1000, 2000, and 3000 rpm). Key performance metrics, including kinematic viscosity, density, heating value, thermal conductivity, and brake power (BP), were analyzed. The results indicated that increasing nanoparticle concentration led to a rise in BP. The highest reduction in root mean square (RMS) vibration accelerations occurred at 3000 rpm and 150 ppm, with vibration reductions of 30.33% for CuO and 28.61% for TiO2. Additionally, 8–10% of engine vibrations were transmitted to the steering wheel. The use of 150 ppm CuO nanoparticles resulted in reduced vibration transmission to the steering wheel at all tested speeds. These findings suggest that nanoparticle-enhanced fuels can significantly reduce engine vibrations, potentially improving driver comfort and reducing wear on vehicle components.

Real-time power optimization based on Q-learning algorithm for direct methanol fuel cell system

Efficient real-time power optimization of direct methanol fuel cell (DMFC) system is crucial for enhancing its performance and reliability. The power of DMFC system is mainly affected by stack temperature and circulating methanol concentration. However, the methanol concentration cannot be directly measured using reliable sensors, which poses a challenge for the real-time power optimization. To address this issue, this paper investigates the operating mechanism of DMFC system and establishes a system power model. Based on the established model, reinforcement learning using Q-learning algorithm is proposed to control methanol supply to optimize DMFC system power under varying operating conditions. This algorithm is simple, easy to implement, and does not rely on methanol concentration measurements. To validate the effectiveness of the proposed algorithm, simulation comparisons between the proposed method and the traditional perturbation and observation (P&O) algorithm are implemented under different operating conditions. The results show that proposed power optimization based on Q-learning algorithm improves net power by 1% and eliminates the fluctuation of methanol supply caused by P&O. For practical implementation considerations and real-time requirements of the algorithm, hardware-in-the-loop (HIL) experiments are conducted. The experiment results demonstrate that the proposed methods optimize net power under different operating conditions. Additionally, in terms of model accuracy, the experimental results are well matched with the simulation. Moreover, under varying load condition, compared with P&O, proposed power optimization based on Q-learning algorithm reduces root mean square error (RMSE) from 7.271% to 2.996% and mean absolute error (MAE) from 5.036% to 0.331%.

Hourly PM2.5 concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model

Accurate prediction of PM2.5 concentrations in ports is crucial for authorities to combat ambient air pollution effectively and protect the health of port staff. However, in port clusters formed by multiple neighboring ports, we encountered several challenges owing to the impact of unique meteorological conditions, potential correlation between PM2.5 levels in neighboring ports, and coupling influence of background pollutants in city zones. Therefore, considering the spatiotemporal correlation among the factors influencing PM2.5 concentration variations within the harbor cluster, we developed a novel blending ensemble deep learning model. The proposed model combined the strengths of four deep learning architectures: graph convolutional networks (GCN), long short-term memory networks (LSTM), residual neural networks (ResNet), and convolutional neural networks (CNN). GCN, LSTM, and ResNet served as the base models aimed at capturing the spatial correlation of PM2.5 concentrations in neighboring ports, the potential long-term dependence of meteorological factors and PM2.5 concentrations, and the effects of urban ambient air pollutants, respectively. Following the blending ensemble technique, the prediction outcomes of three base models were used as the input data for the meta-model CNN, which employs the blending ensemble technique to produce the final prediction results. Based on actual data obtained from 18 ports in Nanjing, the proposed model was compared and analyzed for its prediction performance against six state-of-the-art models. The findings revealed that the proposed model provided more accurate predictions. It reduced mean absolute error (MAE) by 10.59 %–20.00 %, reduced root mean square error (RMSE) by 13.22 %–17.11 %, improved coefficient of determination (R2) by 10 %–35.38 %, and improved accuracy (ACC) by 3.48 %–7.08 %. Additionally, the contribution of each component to the prediction performance of the proposed model was measured using a systematic ablation study. The results demonstrated that the GCN model exerted the most substantial influence on the prediction performance of the GCN-LSTM-ResNet model, followed by the LSTM model. The influence of urban background pollutants can significantly enhance the generalizability of the complete model. Moreover, a comparison with three blended ensemble models incorporating any two base models demonstrated that the GCN-LSTM-ResNet model exhibited superior prediction performance and was particularly excellent in predicting the occurrence of high-concentration events. Specifically, the GCN-LSTM-ResNet model improved MAE and RMSE by at least 12.3% and 9.2%, respectively, but reduced R2 and ACC by 26.1% and 6.8%, respectively. The proposed model provided reliable PM2.5 concentration prediction outcomes and decision support for air quality management strategies in dry bulk port clusters.

Short-Term Photovoltaic Power Probabilistic Forecasting Based on Temporal Decomposition and Vine Copula

With the large-scale development of solar power generation, highly uncertain photovoltaic (PV) power output has an increasing impact on distribution networks. PV power generation has complex correlations with various weather factors, while the time series embodies multiple temporal characteristics. To more accurately quantify the uncertainty of PV power generation, this paper proposes a short-term PV power probabilistic forecasting method based on the combination of decomposition prediction and multidimensional variable dependency modeling. First, a seasonal and trend decomposition using a Loess (STL)-based PV time series feature decomposition model is constructed to obtain periodic, trend, and residual components representing different characteristics. For different components, this paper develops a periodic component prediction model based on TimeMixer for multi-scale temporal feature mixing, a long short-term memory (LSTM)-based trend component extraction and prediction model, and a multidimensional PV residual probability density prediction model optimized by Vine Copula optimized with Q-Learning. These components’ results form a short-term PV probabilistic forecasting method that considers both temporal features and multidimensional variable correlations. Experimentation with data from the Desert Knowledge Australia Solar Center (DKASC) demonstrates that the proposed method reduced root mean square error (RMSE) and mean absolute percentage error (MAPE) by at least 14.8% and 22%, respectively, compared to recent benchmark models. In probability interval prediction, while improving accuracy by 4% at a 95% confidence interval, the interval width decreased by 19%. The results show that the proposed approach has stronger adaptability and higher accuracy, which can provide more valuable references for power grid planning and decision support.

Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

Abstract This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

ORB Visual and WiFi Online RSSI fusion SLAM

Simultaneous Localization and Mapping (SLAM) technologies are indispensable for indoor service robots, enabling them to navigate through and interact with environments. Visual SLAM systems often encounter significant challenges such as dynamic obstacles, variable lighting, feature scarcity, and perceptual aliasing in real-world scenarios. By merging the precise environmental mapping capabilities of visual SLAM with the ubiquity and stability of WiFi signals, our method effectively addresses the limitations typically associated with visual SLAM. Notably, our fusion technique leverages existing WiFi infrastructure, thus providing a cost-effective improvement in spatial awareness without the extensive offline database requirements of WiFi RSSI-based localization. Comparative performance evaluations highlight that our graph optimization-based approach not only surpasses the original ORBSLAM3 method but also significantly outperforms the Extended Kalman Filter (EKF) in terms of accuracy, particularly in environments characterized by poor lighting, feature-less scenes, and significant occlusions. This is evidenced by a reduced Root Mean Square Error (RMSE) in localization: 3.09m for our method versus 4.02m for EKF. This enhancement in precision underscores the potential of our integrated system to advance indoor navigation technologies, making it a crucial development in the field of robotics and automated systems.

Advancing neural network-based data assimilation for large-scale spatiotemporal systems with sparse observations

Data assimilation (DA) is a powerful technique for improving the forecast accuracy of dynamic systems by optimally integrating model forecasts with observations. Traditional DA approaches, however, encounter significant challenges when applied to complex, large-scale, highly nonlinear systems with sparse and noisy observations. To overcome these challenges, this study presents a new Neural Network-based Data Assimilation (DANet) model, specifically employing a Convolutional Long Short-Term Memory architecture. By leveraging the strengths of neural networks, DANet establishes the relationship among model forecasts, observations, and ground truth, facilitating efficient DA in large-scale spatiotemporal forecasting with sparse observations. The effectiveness of the DANet model is demonstrated through an initial case study of wind-driven oceanic flow forecasting, as described by a Quasi-Geostrophic (QG) model. Compared to the traditional Ensemble Kalman Filter (EnKF), DANet exhibits superior performance in cases involving both structured and unstructured sparse observations. This is evidenced by reduced Root Mean Square Errors (RMSEs) and improved correlation coefficients (R) and Structural Similarity Index. Moreover, DANet is seamlessly integrated with the QG model to operationally forecast vorticity and stream function in the long term, further confirming the accuracy and reliability of the DANet model. DANet achieves operational forecasting 60 times faster than EnKF, underscoring its efficiency and potential in DA advancement.

Bias correction of the hourly satellite precipitation product using machine learning methods enhanced with high-resolution WRF meteorological simulations

Accurate precipitation data are crucial in atmospheric and hydrological studies, especially for water resource management and disaster early warning. Satellite precipitation product (SPP) with high spatiotemporal resolution has been regarded as a valuable alternative precipitation source to ground observations. However, the hourly SPP generally performs poorly compared to daily SPP, thereby bias correction is urgently required. This study investigates the viability of utilizing machine learning methods to correct the bias of the hourly Integrated Multi-satellitE Retrievals for Global Precipitation Measurement-Early (IMERG-E) product. Meanwhile, the Weather Research and Forecasting (WRF) model is utilized to generate high-resolution fields of four hourly meteorological variables, namely, temperature at 2 m (TEMP2), specific humidity at 2 m (Q2), wind direction at 10 m (WDIR10), and wind speed at 10 m (WSPD10), which further serve as covariates in machine learning models to enhance the correction process. Four machine learning models were developed, i.e., Random Forest (RF) and Bidirectional Long Short-Term Memory Networks (Bi-LSTM) without WRF-simulated covariates, and RF-WRF and Bi-LSTM-WRF with meteorological covariates. The results demonstrated that incorporating WRF-simulated meteorological covariates improved model performance. Specifically, correlation coefficient (CC) values increased from 0.47 (RF) to 0.51 (RF-WRF) and rose from 0.55 (Bi-LSTM) to 0.60 (Bi-LSTM-WRF), along with reduced root mean square error (RMSE) and increased critical success index (CSI) values. Furthermore, two Bi-LSTM models consistently outperformed two RF models. Overall, the Bi-LSTM-WRF model emerged as the most effective correction method, which increased CC from 0.43 (IMERG-E) to 0.60, reduced RMSE from 1.91 mm to 1.08 mm, and enhanced CSI from 0.34 to 0.41. This study underscores the potential of integrating high-resolution WRF meteorological outputs into machine learning frameworks for correcting hourly SPPs, contributing significantly to the advancement of precipitation estimation in meteorological and hydrological applications.

Enhanced lake elevation mapping using a zone-based method

Abstract Inland lakes play a crucial role in monitoring global climate change and managing responses to extreme weather events, with lake elevation being critical for assessing their regulatory capacities. However, due to the limited temporal resolution of current altimetry satellites, obtaining high-frequency, high-precision elevation data for water bodies remains challenging. Consequently, most studies utilize elevation-area (E-A) models constructed from historical elevation and area records, integrated with area observations from high-temporal resolution optical satellites to infer precise water levels. Yet, the construction of the E-A model often assumes a uniform water level across the lake, thus overlooking potential segmentation during dry periods. To address this, our study implemented a zone-based approach, utilizing hydrological connectivity principles to ensure that elevation data within E-A models are confined to appropriate zonal regions. This method effectively minimized uncertainties by preventing errors from zonal discrepancies, significantly improving accuracy compared to traditional methods. It reduced root mean square errors (RMSE) by 0.71–1.73 m during the dry season, achieving RMSEs of 0.35, 0.64, and 0.37 m across three segments. Furthermore, this method ensures water level data are confined to specific zones, preventing the inconsistencies typically caused by averaging data across multiple stations or selecting data from varying elevations. This consistent domain definition reduces extrapolation errors during the model prediction and inversion. Moreover, the method compensates for time information losses often incurred by relying on multi-year percentile charts, thereby enabling more precise aquatic boundary delineation than traditional regional boundaries.

Mitigating Singularities in Control of Magnetic Capsule Endoscopes Using a Novel Nested Electromagnetic Coil System

Magnetic actuation systems that utilize external electromagnetic coils present a promising approach for controlling untethered small-scale robots, notably in medical applications. However, these systems frequently experience actuation singularities at which robotic control over the agent is lost or severely diminished. Such singularities may lead to excessively large current or rapid current changes that significantly affect the robots’ stability in specific areas of the workspace. In order to effectively mitigate these actuation singularities, this paper introduces a closed-loop control method using a novel nested electromagnetic coil system, leveraging finite element analysis-based magnetic field data and a novel controller combining a singularity solver and optimal current control method. The electromagnetic system has 12 coils nested in four coil units, each having three concentric coils with different outer radius, surrounding a central workspace. We call the 12-coil system Variable Outer Radius Individually Addressable Coil Stacks (VORIACS). Here, we experimentally evaluate the method for closed-loop trajectory tracking of a soft magnetic capsule endoscope (PillCam™) and a permanent magnet robot inside the VORIACS system at different magnetic field orientations. The results demonstrate that the VORIACS system with a singularity solver reduces root mean square (RMS) position tracking errors of the PillCam™by 46% to 50% and orientation tracking errors by 41% to 42% compared to a standard 4-coil system. Moreover, the system can achieve a notable 60.73% reduction in RMS position error, with sub-millimeter precision control when controlling the permanent magnet robot. We further manipulate the PillCam™for tracking a medical application-inspired complex trajectory, showcasing the system’s potential in precise control of magnetic devices for medical use.

Bayesian inversion of HFC-134a emissions in southern China from a new AGAGE site: Results from an observing system simulation experiment

The abundance of hydrofluorocarbons (HFCs) in the atmosphere is increasing and is of significant importance to the Earth's system. It is thus crucial to investigate the spatial distribution of HFC emissions. However, inversion modeling, a commonly used approach, is susceptible to errors from a-priori emissions, inversion grids, observations, and other parameters. In this study, we conduct Observing System Simulation Experiment (OSSE) to evaluate the impact of inversion parameters on HFC emission estimates, focusing on HFC-134a at the newly established Xichong (XCG) AGAGE site in Shenzhen, China. We regard the EDGAR (v7) dataset as a reference for “true” emissions and simulate the “true” atmosphere using the GEOS-Chem model. Our OSSE indicates that conducting inversions in the medium-sensitivity region with source-receptor sensitivity larger than 6.0 (nmol mol−1)/(mol m−2 s−1) minimizes the bias between a-posteriori and “true” total emissions to 0.58 %–1.88 %. In the high-sensitivity region, enhancing the spatial resolution of inversion grids lowers this error from −27.07 % to +0.36 %. The accuracy of a-priori spatial distribution crucially influences that of the a-posteriori: the higher correlation coefficient between a-priori and “true” emissions, the better a-posteriori agree with the “true” emissions, as evidenced by a reduced root mean square error (RMSE) of the a-posteriori vs. “true” emissions from 1.44 × 10−9 g m−2 s−1 (GDP-based) to 8.78 × 10−10 g m−2 s−1 (VIIRS-based). Furthermore, selecting regularization parameters and balancing instrumental and a-priori errors are also important. Our OSSE setups allow for parameter testing during the inversion, offering a framework to assess the regional representativeness of future HFC measurement sites.

Research on time series prediction of hybrid intelligent systems based on deep learning

Power forecasting plays a crucial role in the operation of smart grid system, which is indispensable for making the operation plan of power system, improving economic efficiency and ensuring the quality of power supply. In order to enhance the accuracy of power load forecasting, a hybrid intelligent power load forecasting system is proposed in this paper. The system first preprocesses the raw data using Savitzky-Golay smoothing technology to eliminate noise and improve data quality. Then, a long and short term memory network with attention mechanism is used to enhance the generalization ability of the model. In addition, in order to further improve the prediction performance, an improved genetic algorithm is integrated to optimize the model parameters. Finally, a data set is used to verify the proposed prediction method. In terms of short-term forecasting ability of experiment of the testing data set, compared with LSTM model, the proposed method shows superior performance in root mean square error and mean absolute error indicators, with root mean square error reduced by 18.7 % and mean absolute error reduced by 26.2 %. In terms of long-term prediction ability of experiment of the testing data set, compared with GBRT model, the proposed method reduces root mean square error and mean absolute error by 24.8 % and 30.7 %, respectively. The experimental results show that compared with the existing benchmark algorithm, the proposed method is significantly improved in two key indexes of prediction accuracy, which proves its effectiveness and superiority in power load prediction.

Chemical reactivity, molecular electrostatic potential, FTIR, NMR, in vitro, and in silico studies of mannopyranoside derivatives: 3-Nitrobenzoylation leads to improve antimicrobial activity

This investigation Extensively explored the synthesis and potential therapeutic applications of derivatives derived from methyl α-d-mannopyranoside. It encompasses a wide range of evaluations, including antimicrobial assessments, molecular docking, dynamic simulations, and ADMET analysis. Spectroscopic methods (FTIR, 1H NMR, 13C NMR, mass) were used to confirm the structures of the synthesized mannopyranoside derivatives. This study assessed the antibacterial activity of the strains against a range of both gram-positive and gram-negative bacteria and revealed significant inhibitory effects. In addition, the mannopyranoside derivatives exhibited significant antifungal activity. Additionally, we studied how to improve the thermal, frontier molecular orbital (FMO), and molecular electrostatic potential (MEP) properties of mannopyranoside and its acylated analogs by using density functional theory (DFT). Molecular docking studies provided further evidence of the advantageous antibacterial effects of the mannopyranoside derivatives 3, 6, and 7 against the penicillin-binding protein PBP2a (1VQQ) from the methicillin-resistant binding protein Staphylococcus aureus. The docking results were confirmed via molecular dynamics (MD) simulations, which provided a dynamic view of protein stability. The conformational stability increased in the simulations, and the values of the solvent-accessible surface area (SASA) and radius of gyration (Rg) were found to be consistent and stable. Reduced root mean square fluctuations (RMSFs), facilitated by strong hydrogen bonding interactions, characterize ligand–protein interactions. The toxicological and pharmacokinetic profiles of the molecule were assessed using ADMET analysis. The results showed that the molecule had favorable drug-like features, making it a strong candidate for further development. Overall, these findings indicate that 3-nitobenzoylated mannopyranoside derivatives exhibit considerable potential as therapeutic agents for treating microbial infections.

A Variational Mode Decomposition–Grey Wolf Optimizer–Gated Recurrent Unit Model for Forecasting Water Quality Parameters

Water is a critical resource globally, covering approximately 71% of the Earth’s surface. Employing analytical models to forecast water quality parameters based on historical data is a key strategy in the field of water quality monitoring and treatment. By using a forecasting model, potential changes in water quality can be understood over time. In this study, the gated recurrent unit (GRU) neural network was utilized to forecast dissolved oxygen levels following variational mode decomposition (VMD). The GRU neural network’s parameters were optimized using the grey wolf optimizer (GWO), leading to the development of a VMD–GWO–GRU model for forecasting water quality parameters. The results indicate that this model outperforms both the standalone GRU model and the GWO–GRU model in capturing key information related to water quality parameters. Additionally, it shows improved accuracy in forecasting medium to long-term water quality changes, resulting in reduced root mean square error (RMSE) and mean absolute percentage error (MAPE). The model demonstrates a significant improvement in the lag of forecasting water quality parameters, ultimately boosting forecasting accuracy. This approach can be applied effectively in both monitoring and forecasting water quality parameters, serving as a solid foundation for future water quality treatment strategies.

Dynamic performance evaluation of a magnetorheological damper-based sting support in wind tunnel tests

The sting support faces an issue of being prone to resonance due to its low-damping characteristics, which will compromise the accuracy of test data in wind tunnel experiments. Magnetorheological damper (MRD)-based tail support (MRSS) features varying stiffness and damping abilities, enabling it to suppress the vibration with random, time-variation characteristics in a self-adaptive way, and it also exhibits excellent fail–safe properties. This work aims to validate its controllability and fail–safe property in a wind tunnel environment. First, the natural frequency, root mean square (RMS), and power spectral density (PSD) of the designed MRSS’ response for random excitation were theoretically analyzed. Then, wind tunnel experiments were conducted to test the response under different attack angles, wind speeds, and test currents. Subsequently, the controllability and fail–safe property were demonstrated by analyzing the aircraft model response. Also, the variable stiffness and damping properties of MRD behind the observed phenomena were revealed based on the theoretical analysis. Results demonstrate that as the input current increases and the MRD’s stiffness continues to increase, the damping initially increases and then decreases. Increasing wind speed leads to a decrease in the MRD’s stiffness. Additionally, approximately 50% reduction in RMS and a multi-modal response attenuation was achieved.

Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models

As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm’s efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.

Enhancing water-carbon fluxes and yield predictions of winter wheat using irrigation and data assimilation techniques in a land surface model

Croplands are essential to land–atmosphere interactions and have a great impact on water balance, carbon fluxes, regional weather and climate, and food supply. This study utilized the Noah-MP land surface model, enhanced by a dynamic crop growth process known as Noah-MP-Crop, and employed a multi-pass data assimilation scheme (MLDAS) to assimilate leaf area index, surface soil moisture, and solar-induced chlorophyll fluorescence into the model for refining model’s simulations of land state variables (soil moisture and leaf biomass) and optimize the model vegetation parameters [specific leaf area (SLA) and maximum carboxylation rate normalized to 25 °C (Vcmax25)]. In conjunction with the irrigation module in this model, four experiments were conducted at the local observation-site scale to estimate and validate water-carbon fluxes [actual evapotranspiration (ETa) and gross primary productivity (GPP)] and crop yield within the winter wheat growing phase, which included open-loop simulation with and without irrigation (OLirr and OL), data assimilation with and without irrigation (DAirr and DA), separately. In-situ observations from the Yucheng Station in 2006 were used to evaluate these four experiments. Results indicated that the combined experiment (DAirr) provided the best agreement with the observed ETa, GPP, and yield. This superior performance is mainly attributed to the accurate vegetation information and soil moisture profile provided by the combined experiments with the model. Results indicated that the DAirr experiment performed better than the OL experiment in ETa estimation, with a higher correlation coefficient (R) of 0.85 compared to 0.71, a reduced root mean square error (RMSE) from 1.51 to 1.06 mm day−1, a lower normalized RMSE (NRMSE) from 43.21 % to 23.39 %, and a decreased mean absolute error (MAE) from 1.16 to 0.78 mm day−1. Similarly, DAirr increased R from 0.71 to 0.87 and cut down RMSE (MAE) from 5.81 (4.32) to 3.48 (2.26) g m−2 day−1 and NRMSE from 70.39 % to 18.20 % for GPP estimation. Moreover, winter wheat yield from DAirr (770.9 g m−2) agreed better with ground measurements (819.7 g m−2) than that from OL (400.6 g m−2). Additionally, results revealed that assimilating all three variables simultaneously is superior to single-variable assimilation and significantly outperforms the OL approach. All in all, this study has provided initial insights into the feasibility of assimilating three variables at the site scale into the model for improving the estimation accuracy of water-carbon fluxes and winter wheat yield, which laid a foundation for subsequent regional simulations.

Improving Coastal Storm Surge Monitoring Through Joint Modeling Based on Permanent and Temporary Tide Gauges

AbstractWith climate change, there will be higher requirements for monitoring storm surges (SSs) in nearshore areas. However, this capability is limited by the sparseness of tide gauge (TG) stations. Establishing and maintaining a permanent, high‐spatial coverage, in situ TG network is complex and expensive. Here, we propose a joint modeling method developed from the all‐site modeling data‐driven framework by importing temporary TGs into coastal regions with insufficient permanent TG stations. The assessments show that this method can significantly optimize the capability of extreme SS monitoring during typhoons and hurricanes. Moreover, the evaluation based on Coupled Model Intercomparison Project Phase 6 data indicates that it will monitor extreme SSs more effectively during 2025–2050 compared with only using existing permanent in situ TGs (reducing root mean square error and absolute mean bias by ∼50%). The joint modeling method provides an applicable and sustainable solution for optimizing the SS monitoring capability in coastal areas.

Detection and interpretation of the time-varying seasonal signals in China with multi-geodetic measurements

The time-varying periodic variations in Global Navigation Satellite System (GNSS) stations affect the reliable time series analysis and appropriate geophysical interpretation. In this study, we apply the singular spectrum analysis (SSA) method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic datasets. These include 23-year observations from the Crustal Movement Observation Network of China (CMONOC), displacements inferred from the Gravity Recovery and Climate Experiment (GRACE), and loadings derived from Geophysical models (GM). The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations, and the SSA method outperforms the traditional least squares fitting (LSF) method in extracting and interpreting the time-varying seasonal signals from the original time series. The decrease in the root mean square (RMS) correlates well with the annual cycle variance estimated by the SSA method, and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method. With SSA analysis, the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues. Moreover, both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations, and the geophysical effects can account for 71% of the GNSS annual amplitudes, and the average RMS reduction is 15%. The SSA method has proved to be useful for investigating the GNSS time-varying seasonal signals. It could be applicable as an auxiliary tool in the improvement of non-linear variations investigations.

Generation of Virtual Ground Control Points Using a Binocular Camera

This paper presents a methodology for generating virtual ground control points (VGCPs) using a binocular camera mounted on a drone. We compare the measurements of the binocular and monocular cameras between the classical method and the proposed one. This work aims to decrease human processing times while maintaining a reduced root mean square error (RMSE) for 3D reconstruction. Additionally, we propose utilizing COLMAP to enhance reconstruction accuracy by solely utilizing a sparse point cloud. The results demonstrate that implementing COLMAP for pre-processing reduces the RMSE by up to 16.9% in most cases. We prove that VGCPs further reduce the RMSE by up to 61.08%.