Root Mean Square Difference Research Articles

The operational CMEMS wind wave forecasting system of the Black Sea

ABSTRACT Accurate wind wave data is essential for ship navigation, safety at sea, and offshore energy production. Access to real-time and forecast wave data, with quality metrics, is vital for operational services. The Copernicus Marine Environment Monitoring Service (CMEMS) operational wave forecasting system of the Black Sea relies on the third-generation WAve Model (WAM). It offers a 1-day hindcast and 10-day forecast at 2.5 km spatial and 1-hour temporal resolutions. The model is driven by ECMWF IFS010 winds as well as surface currents and sea surface heights from a hydrodynamic model. The model's validation against satellite and buoy data indicates good overall performance. For two-year satellite significant wave heights, error statistics include a root mean square difference (RMSD) of 27 cm, a bias of – 16 cm, a scatter index of 0.22, and a correlation coefficient of 0.93. Wave buoy comparisons show a RMSD of 1.09 s for the TM02 period and a 30.5° standard deviation of the mean direction. The model performs well during storm events and provides reliable wave forecasts for up to four days, despite slightly underestimating the significant wave height. The driving wind fields generally perform well, but assessments reveal shortcomings along the coasts.

Effects of radial extracorporeal shock wave therapy on flexor spasticity of the upper limb in post-stroke patients: A randomized controlled trial

Objective This study aimed to assess the efficacy of radial extracorporeal shock wave therapy in treating upper limb spasticity after a stroke. Design Randomized controlled trial. Setting Zhujiang Hospital of Southern Medical University. Subjects This study included 95 people with stroke. Intervention The active (n = 47) and sham-placebo (n = 48) radial extracorporeal shockwave therapy groups received three treatment sessions (every third day). Main measures The Modified Ashworth Scale, Hmax/Mmax ratio, root mean square, co-contraction ratio, mechanical parameters of the muscle and temperature were measured at baseline and days 2, 5 and 8. Results Among the 135 potential participants screened, 100 were enrolled and allocated randomly, with 95 participants ultimately being included in the intent-to-treat analysis dataset. The active group showed significantly better improvements in upper limb spasticity and muscle function than did the sham-placebo group. Greater improvements in the Modified Ashworth Scale were observed in the active group than in the sham-placebo group (difference, −0.45; 95% CI, −0.69 to −0.22; P < 0.001). Moreover, significant differences in root mean square, co-contraction ratio and Hmax/Mmax ratio were observed between the two groups (all P < 0.001). The mechanical parameters of the biceps muscle were significantly better in the active group than in the sham-placebo group (P < 0.001). The active group had a higher temperature than the sham-placebo group, although the difference was not significant (P = 0.070). Conclusions This study revealed that the treatment with extracorporeal shockwave therapy can relieve upper limb spasticity in people with stroke.

Effective attention model for global sea surface temperature prediction

Sea Surface Temperature (SST) is an essential parameter of the ocean–atmosphere system, so accurately predicting the future SST is of great significance. To address the low prediction accuracy due to insufficient consideration of spatiotemporal correlations in existing machine learning models, this paper proposes an Effective Attention Model (EAM) to achieve daily global SST prediction (predicting the SST of the next 15 days using those of the past 15 days) and monthly global SST prediction (predicting the SST of the next 12 months using those of the past 12 months). The EAM adopts an attention mechanism to avoid information redundancy and improve prediction accuracy. This is the first work to realize high-accuracy global SST prediction with a high spatial resolution of 0.25° on two time scales (daily and monthly) based on deep learning. Experiments show that (1) the prediction results have a consistent global spatiotemporal distribution with the ground truth, with the Root Mean Squared Difference (RMSD) of daily prediction ranging from 0.212 to 0.932 ℃ and the RMSD of monthly prediction ranging from 0.666 to 0.782 ℃. The EAM is superior to the deep learning models (ConvLSTM, 3D-CNN, and LSTM) and the numerical model (HYCOM); (2) Besides global prediction, the EAM also achieves good performance in a climate change sensitive area (Niño 3.4 sea area), with the lowest RMSD of 0.231 ℃. This study may not only benefit the research on climate events (El Niño and La Niña) but also serve to predict other marine disasters (such as heat waves and storms). In the future, the proposed attention module can also be adapted to other prediction scenarios such as meteorology.

A New Method to Minimize the Standard Deviation and Root Mean Square of the Prediction Error of Single-Optimized IOL Power Formulas.

The purpose of this study was to develop a simplified method to approximate constants minimizing the standard deviation (SD) and the root mean square (RMS) of the prediction error in single-optimized intraocular lens (IOL) power calculation formulas. The study introduces analytical formulas to determine the optimal constant value for minimizing SD and RMS in single-optimized IOL power calculation formulas. These formulas were tested against various datasets containing biometric measurements from cataractous populations and included 10,330 eyes and 4 different IOL models. The study evaluated the effectiveness of the proposed method by comparing the outcomes with those obtained using traditional reference methods. In optimizing IOL constants, minor differences between reference and estimated A-constants were found, with the maximum deviation at -0.086 (SD, SRK/T, and Vivinex) and -0.003 (RMS, PEARL DGS, and Vivinex). The largest discrepancy for third-generation formulas was -0.027mm (SD, Haigis, and Vivinex) and 0.002mm (RMS, Hoffer Q, and PCB00/SN60WF). Maximum RMS differences were -0.021 and +0.021, both involving Hoffer Q. Post-minimization, the largest mean prediction error was 0.726 diopters (D; SD) and 0.043 D (RMS), with the highest SD and RMS after adjustments at 0.529 D and 0.875 D, respectively, indicating effective minimization strategies. The study simplifies the process of minimizing SD and RMS in single-optimized IOL power predictions, offering a valuable tool for clinicians. However, it also underscores the complexity of achieving balanced optimization and suggests the need for further research in this area. The study presents a novel, clinically practical approach for optimizing IOL power calculations.

Dimensional Accuracy of Two-Step Impressions Relined with Extra-Light Addition Silicone Material.

Objectives: Some small defects may remain in the impression after making a two-step putty-light body impression. The aim of this study was to evaluate and compare the dimensional accuracy of 2-step and relined 2-step (3-step) putty-light body impressions. Materials and Methods: In this in vitro study, 30 impressions were made with putty, light body, and extra-light body addition silicone materials using the 2-step and 3-step impression techniques (N=15). An epoxy resin master model was made duplicating a maxillary typodont with left first premolar and first molar teeth prepared with a shoulder finish line and truncated pyramidal-shaped indices in the mid-palate and third molar sites. In addition to creating a reference digital model by scanning the master model, 30 master casts were scanned to produce digital models. The anteroposterior (AP) and cross-sectional (CS) dimensional accuracy of the models were compared with the master model using linear measurements. Moreover, tooth size measurements were made and compared using the root mean square (RMS). Two-sample t-test was applied to analyze the data (α=0.05). Results: The mean AP and RMS differences between the two study groups were not significant (P>0.05). However, the CS difference between the two groups was significant (P<0.001), and the 3-step impression technique showed smaller discrepancies in comparison to the master model. Conclusion: There was no significant difference in accuracy of the two techniques for single-unit and multiple-unit preparations. The 3-step impression technique had a higher CS dimensional accuracy.

A Simulation-Based Framework to Determine the Kinematic Compatibility of an Augmentative Exoskeleton during Walking

Augmentative exoskeletons (AEs) are wearable orthotic devices that, when coupled with a healthy individual, can significantly enhance endurance, speed, and strength. Exoskeletons are function-specific and individual-specific, with a multitude of possible configurations and joint mechanisms. This complexity presents a challenging scenario to quantitatively determine the optimal choice of the kinematic configuration of the exoskeleton for the intended activity. A comprehensive simulation-based framework for obtaining an optimal configuration of a passive augmentative exoskeleton for backpack load carriage during walking is the theme of this research paper. A musculoskeletal-based simulation approach on 16 possible kinematic configurations with different Degrees of Freedom (DoF) at the exoskeleton structure’s hip, knee, and ankle joints was performed, and a configuration with three DoF at the hip, one DoF at the knee, three DoF at the ankle was quantitatively chosen. The Root Mean Square of Deviations (RMSD) and Maximum Deviations (MaxDev) between the kinematically coupled human–exoskeleton system were used as criteria along with the Cumulative Weight Score (CWS). The chosen configuration from the simulation was designed, realised, and experimentally validated. The error of the joint angles between the simulation and experiments with the chosen configuration was less than 3° at the hip and ankle joints and less than 6° at the knee joints.

Effect of adding Schroth physiotherapeutic scoliosis specific exercises to standard care in adolescents with idiopathic scoliosis on posture assessed using surface topography: A secondary analysis of a Randomized Controlled Trial (RCT).

Adolescent idiopathic scoliosis (AIS) is a three-dimensional structural asymmetry of the spine and trunk affecting 2-4% of adolescents. Standard treatment is observation, bracing, and surgery for small, moderate, and large curves, respectively. Schroth exercises aim to correct posture and reduce curve progression. This study aimed to determine the effect of Schroth exercises added to the standard care compared to standard care alone on torso asymmetry in AIS. In a randomized controlled trial (NCT01610908), 124 participants with AIS (age: 10-18, Cobb: 10°-45°, Risser: ≤3) were randomly assigned to the control (Standard care only) or Schroth (Standard care + Schroth treatment) group. Schroth treatment consisted of 1-hour weekly supervised sessions and 30-45 minutes of daily home exercises for six months. The control group received Schroth exercises in the last six months of the 1-year monitoring period. Markerless 3D surface topography assessed torso asymmetry measured by maximum deviation (MaxDev) and root mean square (RMS). Intention to treat linear mixed effects model analysis was compared to the per protocol analysis. In the intention to treat analysis, the Schroth group (n = 63) had significantly larger decreased RMS (-1.2 mm, 95%CI [-1.5,-0.9]mm, p = 0.012) and MaxDev (-1.9mm, 95%CI [-2.4,-1.5]mm, p = 0.025) measurements compared to controls (n = 57) after six months of intervention. In the per protocol analysis (Schroth n = 39, control n = 36), the Schroth group also had a significantly larger decrease compared to the control in both the RMS (-1.0mm, 95%CI [-1.9, -0.2]mm, p = 0.013) and MaxDev measurements (-2.0mm, 95%CI [-3.3,-0.5]mm, p = 0.037). For the control group, both the intention to treat and per protocol analysis showed no difference in RMS and MaxDev in the last six months of Schroth intervention (p>0.5). Schroth Exercise treatment added to standard care (observation or bracing) reduced asymmetry measurements in AIS. As expected, a greater effect was observed for participants who followed the prescribed exercise treatment per protocol.

Dynamics of heart rhythm disorders in combatants with acute myocarditis during a 6-month follow-up

The main factors of an unfavorable prognosis, are life-threatening heart rhythm disorders which determine the severity of the clinical course of myocarditis, in addition to impaired contractility and dilatation of the left ventricle (LV). The aim of the study was to assess the frequency and nature of arrhythmias and establish predictors of their long-term persistence, assess the severity of anxiety and depression, changes in heart rate variability in combatants on the background of changes in the structural and functional state of the left ventricle during a 6-month follow-up. The study included 52 male servicemen (combatants) with acute myocarditis (AM) with an average age of 33.4±2.5 years. All patients were assessed by Questionnaire according to the Hospital Anxiety and Depression Scale (HADS) and underwent 24 hour ECG monitoring, transthoracic echocardiography and cardiac magnetic resonance (CMR) with late enhancement using gadovist. At the onset of AM frequent supraventricular (30.7% of cases) and frequent ventricular (42.3% of cases) extrasystolic arrhythmia, as well as paroxysms of non-sustained ventricular tachycardia (26.9% of cases) on the background of heart rhythm variability disorders were recorded. After 6 months, there was an improvement in the structural and functional state of the heart, which was characterized by a decrease in dilatation and an improvement of LV systolic function, a decrease in the volume of the inflammatory lesion and the total number of affected LV segments revealed on cardiac MRI, which was associated with a decrease in the number of ventricular rhythm disorders and reduction of clinical manifestations of anxiety according to the HADS scale. The obtained data indicate that the persistence of supraventricular rhythm disorders is associated with the long-term presence of anxiety and low values of standard deviation of RR intervals (SDNN) and root mean square of successive differences between normal heartbeats (RMSSD). Also predictors of persistence of non-sustained ventricular tachycardia paroxysms were established – SDNN value ≤80.0 ms, LV end-diastolic volume index ≥95 ml/m2, total number of affected LV segments ≥6 segments, presence of delayed contrasting in ≥3 LV segments, pre­sence of ≥12 points on the HADS scale and predictors of atrial fibrillation – SDNN value ≤80.0 ms, RMSSD value ≤12.0 ms, total number of affected segments of LV≥6 seg. and ≥12 points on the HADS anxiety scale, determined at the onset of AM.

Application of surface energy balance Algorithm for land (SEBAL) for mapping spatiotemporal evapotranspiration over various climatic zones of Pakistan

To address the emerging issues of water scarcity in a hydrological realm, estimation of evapotranspiration (ET) at spatiotemporal scale is essential. It is crucial for understanding climate change, particularly for Pakistan's ongoing water resource management policy under the United Nations' Sustainable Development Goals (SDGs), where water is given top priority. In situ techniques can quantify ET, but with a small spatial footprint, it cannot accurately depict its regional distribution. Therefore, geospatial techniques and data, especially high resolution satellite imagery, estimate ET for large heterogeneous surfaces. The Surface Energy Balance Algorithm for Land (SEBAL) was employed in this study to estimate the spatiotemporal distribution of ET in various climatic zones of Pakistan using Landsat 8 images. A total of 48 satellite images were processed for six study sites (Skardu, Kalam, Peshawar, D.I. Khan, Quetta and Tando Jam) for the summer months from 2013 to 2020. Accuracy of SEBAL-based ET was evaluated with ground-based ET. The model performs well with daily rate of ET under southern desert hot climate of Tando Jam with highest pearson’s correlation coefficient (r) of 0.89, lowest root mean squared difference (RMSD) and mean bias error (MBE) of 0.42 mm/day and 0.26 mm/day, respectively. The steppe-hot and extremely humid climate of Peshawar follow Tando Jam, with r value of 0.82, RMSD value of 0.76 mm/day, and MBE value of 0.40 mm/day. Central desert hot climate of Dera Ismail Khan(D.I. Khan) has the lowest (though still better) r value at 0.74, while the other two indices (RMSD and MBE) do not differ significantly from other sites. A high RMSD (0.84 mm/day) for the daily ET rate is recorded in Skardu's northern, cold, snowy climate; the rest of the indices are comparable to D.I. Khan region. In Kalam, the ET rate for various land uses was calculated and was found highest for snow cover (4.66 to 6.45 mm/day), followed by forest cover (3.69 to 5.79 mm/day), and open water body (3.25 to 5.25 mm/day). The SEBAL model exhibitedtremendous potential to be applied under a range of climatic conditionsin this study. Evapotranspiration can provide vital information for monitoring hydrological extremes (droughts and floods), environmental protection, planning climate-resilient agriculture, combating climate change, and sustainable management of water resources.

Effect of transcutaneous electrical acupoint stimulation based on the theory of "qi ascending and descending movement" on autonomic nervous system and gastrointestinal function in patients after general anesthesia laparoscopic cholecystectomy.

To observe the therapeutic effect of transcutaneous electrical acupoint stimulation (TEAS) based on the theory of "qi ascending and descending movement" in patients after general anesthesia laparoscopic cholecystectomy, so as to explore the impact of TEAS on the autonomic nervous system and gastrointestinal function of patients. A total of 204 patients scheduled to undergo general anesthesia laparoscopic cholecystectomy were selected and randomly divided into control, double acupoints and multiple acupoints groups, with 68 cases in each group. For patients in the multiple acupoints group, TEAS was applied at Zusanli (ST36), Tiantu (CV22), Danzhong (CV17), Zhongwan (CV12), Taichong (LR3), and Neiguan (PC6) 30 min before anesthesia induction until the end of the surgery. In the double acupoints group, TEAS was applied only at ST36 and PC6. No electrical stimulation was applied in the control group. The postoperative bloating, bowel sound recovery time, first farting time, first defecation time, length of hospital stay, nausea and vomiting were compared among the three groups. Heart rate variability was monitored by twelve-lead electrocardiogram to evaluate the autonomic nervous function of the patients, including the low frequency power/high frequency power ratio (LF/HF), the standard deviation of all sinus RR intervals (SDNN), and the root mean square of difference between successive normal RR intervals (RMSSD). At 6 h and 24 h after surgery, the symptoms of bloating, nausea and vomiting in the multiple acupoints group and double acupoints group were significantly improved compared to the control group (P<0.05), and the multiple acupoints group was superior to the double acupoints group (P<0.05). Compared with the control group, the bowel sound recovery time, first farting time, first defecation time, and length of hospital stay were significantly shorter (P<0.05) in the multiple acupoints group and double acupoints group, and the multiple acupoints group was superior to the double acupoints group (P<0.05). At 1 d and 2 d after surgery, compared with the control group, LF/HF was decreased (P<0.05) while SDNN and RMSSD were increased (P<0.05) in the multiple acupoints group and double acupoints group, and there was a significant difference between the two groups (P<0.05). TEAS treatment based on the theory of "qi ascending and descending movement" can relieve gastrointestinal dysfunction, reduce early postoperative sympathetic nerve excitement and maintain parasympathetic nerve tension in patients after general anesthesia laparoscopic cholecystectomy, thereby promoting gastrointestinal function recovery.

Modified time domain randomization technique for multi-shaker non-stationary non-Gaussian random vibration control

Traditional random vibration environmental tests simulate stationary Gaussian random vibration environments. In this work, the generation and closed-loop control of multi-shaker non-stationary non-Gaussian random vibration signals with the modified time domain randomization technique are studied. The amplitude modulation function is introduced to control the non-stationary and non-Gaussian characteristics that the root mean square value of the generated signal changes with time. The kurtosis relationship between the one frame pseudo-random signal and consecutive true random signal is derived theoretically. It is found that the kurtosis of the consecutive non-stationary random signal is determined by the amplitude modulation function and kurtosis of the pseudo-random signal. The standard deviation of the time-varying root mean square reflects the non-stationary characteristics of the generated signal. A control strategy for multi-shaker non-stationary non-Gaussian random vibration environmental test is proposed. The non-stationary and non-Gaussian characteristics are extracted from the measured response signals so that the power spectral densities, kurtoses and non-stationary characteristics can be controlled simultaneously. The results from the simulation examples and multi-shaker vibration experiments prove the feasibility and practicability of the proposed non-stationary non-Gaussian random signal generation method and control strategy.

Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols

Abstract. From a passive satellite remote sensing point of view, the richest set of information on aerosol properties can be obtained from instruments that measure both intensity and polarization of backscattered sunlight at multiple wavelengths and multiple viewing angles for one ground pixel. However, it is challenging to exploit this information at a global scale because complex algorithms are needed with many fit parameters (aerosol and land/ocean reflection), based on online radiative transfer models. So far, two such algorithms have demonstrated this capability at a global scale: the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm and the Remote sensing of Trace gas and Aerosol Products (RemoTAP) algorithm. In this paper, we present a detailed comparison of the most recent versions of RemoTAP and GRASP. We evaluate both algorithms for synthetic observations, for real PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) observations against AERONET (Aerosol Robotic Network) for common pixels, and for global PARASOL retrievals for the year 2008. For the aerosol optical depth (AOD) over land, both algorithms show a root mean square error (RMSE) of 0.10 (at 550 nm). For single scattering albedo (SSA), both algorithms show a good performance in terms of RMSE (0.04), but RemoTAP has a smaller bias (0.002) compared to GRASP (0.021). For the Ångström exponent (AE), GRASP has a smaller RMSE (0.367) than RemoTAP (0.387), mainly caused by a small overestimate of AE at low values (large particles). Over ocean both algorithms perform very well. For AOD, RemoTAP has an RMSE of 0.057 and GRASP an even smaller RMSE of 0.047. For AE, the RMSEs of RemoTAP and GRASP are 0.285 and 0.224, respectively. Based on the AERONET comparison, we conclude that both algorithms show very similar overall performance, where both algorithms have stronger and weaker points. For the global data products, we find a root mean square difference (RMSD) between RemoTAP and GRASP AOD of 0.12 and 0.038 over land and ocean, respectively. The largest differences occur over the biomass burning region in equatorial Africa. The global mean values are virtually unbiased with respect to each other. For AE the RMSD between RemoTAP and GRASP is 0.33 over land and 0.23 over ocean. For SSA, we find much better agreement over land (bias = −0.01, RMSD = 0.043 for retrievals with AOD &gt; 0.2) than over ocean (bias = 0.053, RMSD = 0.074). As expected, the differences increase towards low AOD, over both land and ocean. We also compared the GRASP and RemoTAP AOD and AE products against MODIS. For AOD over land, the agreement of either GRASP or RemoTAP with MODIS is worse than the agreement between the two PARASOL algorithms themselves. Over ocean, the agreement is very similar among the three products for AOD. For AE, the agreement between GRASP and RemoTAP is much better than the agreement of both products with MODIS. The agreement of the latest product versions with each other and with AERONET improved significantly compared to the previous version of the global products of GRASP and RemoTAP. The results demonstrate that the dedicated effort in algorithm development for multi-angle polarimetric (MAP) aerosol retrievals still leads to substantial improvement of the resulting aerosol products, and this is still an ongoing process.

A novel land surface temperature reconstruction method and its application for downscaling surface soil moisture with machine learning

Downscaling of soil moisture data is important for high resolution hydrological modeling. Most downscaling studies in the literature have used spatially discontinuous land surface temperature (LST) maps as the main auxiliary parameter, which limits the creation of continuous soil moisture maps. The number of studies on soil moisture downscaling with machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, which are then used to produce high resolution surface soil moisture (SSM) datasets. The proposed method is shown to have high mean performance with R2 and RMSE values of 0.94 and 1.84°K, respectively, for the period between 2019 and 2022. The developed reconstructed LST maps were then used to downscale original 9 km spatial resolution soil moisture datasets of SMAP L3 and SMAP L4 with Random Forest (RF) machine learning algorithm. The RF model were run with four different rainfall datasets, and the MSWEP rainfall dataset was found to produce the best results. The use of antecedent rainfall values as input variables in machine learning models has been shown to improve the performance of the models R2 0.76 to 0.93. The accuracy of the downscaled data was later evaluated for Western Anatolia Basins (WAB) in Türkiye with 31 in-situ stations. The downscaled SMAP L4 had good average statistical indicators R (0.815 ± 0.1), RMSE (0.09 ± 0.047 cm3/cm3), and ubRMSE (0.058 ± 0.025 cm3/cm3). Downscaled SMAP L3 was also validated with in-situ observations with satisfactory R (0.79 ± 0.074), RMSE (0.09 ± 0.043 cm3/cm3), and ubRMSE (0.06 ± 0.026 cm3/cm3) statistics. Furthermore, the performance of the downscaled SMAP L3 was also cross validated with SMAP + Sentinel 1 (L2) dataset between 2019 and 2022. The mean statistics of R (0.761 ± 0.11) and Root Mean Squared Difference (RMSD) (0.05 ± 0.014 cm3/cm3) between downscaled SMAP L3 and L2 data revealed that the new reconstruction method of LST used in the RF model for downscaling of soil moisture performed well to obtain high resolution soil moisture datasets. The proposed technique also overcame the difficulties associated with coastal regions where data was masked for quality considerations, by not only enhancing overall spatial resolution but also filling these data gaps and giving a complete SSM coverage.

Experimental and numerical study to optimize building integrated photovoltaic (BIPV) roof structure

The study addresses the issue of heat dissipation in Building Integrated Photovoltaic (BIPV) roofs by proposing a novel PV module and establishing its testbed in Beijing, China, which is extended to more structures by the validated numerical model. The validation reveals MBD (mean bias difference）values of 10.24% for electricity production and 9.32% for temperature, accompanied by corresponding RMSD (root mean square difference) values of 23.97% and 10.46%, respectively. Taguchi method and Computational Fluid Dynamics (CFD) numerical simulation were employed to design and simulate orthogonal schemes, optimizing BIPV roof structure for the first time by considering three factors: air gap thickness, PV panel spacing and heating power. The results indicate that the air gap thickness impacts most on PV panel temperature, followed by meteorological parameters and PV panel spacing. The optimal BIPV roof structure, featured an air gap of 68 mm and a PV panel spacing of 30 mm, exhibits a 25.35% reduction in PV panel temperature, an 8.78% increase in signal-to-noise (S/N) ratio and a 2.49% growth in electricity production compared to the performance of the intermediate level scheme.

Validating GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN) Product over CONUS

Abstract Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) input data. GREMLIN is useful for observing severe weather and initializing convection for short-term forecasts, especially over regions without ground-based radars. This study expands the evaluation of GREMLIN’s accuracy against the Multi-Radar Multi-Sensor (MRMS) System to the entire contiguous United States (CONUS) for the entire annual cycle. Regional and temporal variation of validation metrics are examined over CONUS by season, day of year, and time of day. Since GREMLIN was trained with data in spring and summer, root-mean-square difference (RMSD) and bias are lowest in the order of summer, spring, fall, and winter. In summer, diurnal patterns of RMSD follow those of precipitation occurrence. Winter has the highest RMSD because of cold surfaces mistaken as precipitating clouds, but some of these errors can be removed by applying the ABI clear-sky mask product and correcting biases using a lookup table. In GREMLIN, strong echoes are closely related to the existence of lightning and corresponding low brightness temperatures, which result in different error distributions over different regions of CONUS. This leads to negative biases in cold seasons over Washington State, lower 30-dBZ critical success index caused by high misses over the Northeast, and higher false alarms over Florida that are due to higher frequency of lightning.

Markerless motion capture provides accurate predictions of ground reaction forces across a range of movement tasks

Measuring or estimating the forces acting on the human body during movement is critical for determining the biomechanical aspects relating to injury, disease and healthy ageing. In this study we examined whether quantifying whole-body motion (segmental accelerations) using a commercial markerless motion capture system could accurately predict three-dimensional ground reaction force during a diverse range of human movements: walking, running, jumping and cutting. We synchronously recorded 3D ground reaction forces (force instrumented treadmill or in-ground plates) with high-resolution video from eight cameras that were spatially calibrated relative to a common coordinate system. We used a commercially available software to reconstruct whole body motion, along with a geometric skeletal model to calculate the acceleration of each segment and hence the whole-body centre of mass and ground reaction force across each movement task. The average root mean square difference (RMSD) across all three dimensions and all tasks was 0.75 N/kg, with the maximum average RMSD being 1.85 N/kg for running vertical force (7.89 % of maximum). There was very strong agreement between peak forces across tasks, with R2 values indicating that the markerless prediction algorithm was able to predict approximately 95–99 % of the variance in peak force across all axes and movements. The results were comparable to previous reports using whole-body marker-based approaches and hence this provides strong proof-of-principle evidence that markerless motion capture can be used to predict ground reaction forces and therefore potentially assess movement kinetics with limited requirements for participant preparation.

Agreement between a markerless and a marker-based motion capture systems for balance related quantities

Balance studies usually focus on quantities describing the global body motion. Assessing such quantities using classical marker-based approach can be tedious and modify the participant’s behaviour. The recent development of markerless motion capture methods could bypass the issues related to the use of markers. This work compared dynamic balance related quantities obtained with markers and videos. Sixteen young healthy participants performed four different motor tasks: walking at self-selected speed, balance loss, walking on a narrow beam and countermovement jumps. Their movements were recorded simultaneously by marker-based and markerless motion capture systems. Videos were processed using a commercial markerless pose estimation software, Theia3D. The centre of mass position (CoM) was computed, and the associated extrapolated centre of mass position (XCoM) and whole-body angular momentum (WBAM) were derived. Bland-Altman analysis was performed and root mean square difference (RMSD) and coefficient of correlation were computed to compare the results obtained with marker-based and markerless methods. Bias remained of the magnitude of a few mm for CoM and XCoM positions, and RMSD of CoM and XCoM was around 1 cm. RMSD of the WBAM was less than 10 % of the total amplitude in any direction, and bias was less than 1 %. Results suggest that outcomes of balance studies will be similar whether marker-based or markerless motion capture system are used. Nevertheless, one should be careful when assessing dynamic movements such as jumping, as they displayed the biggest differences (both bias and RMSD), although it is unclear whether these differences are due to errors in markerless or marker-based motion capture system.

Sagittal plane knee kinematics can be measured during activities of daily living following total knee arthroplasty with two IMU.

Knee function is rarely measured objectively during functional tasks following total knee arthroplasty. Inertial measurement units (IMU) can measure knee kinematics and range of motion (ROM) during dynamic activities and offer an easy-to-use system for knee function assessment post total knee arthroplasty. However, IMU must be validated against gold standard three-dimensional optical motion capture systems (OMC) across a range of tasks if they are to see widespread uptake. We computed knee rotations and ROM from commercial IMU sensor measurements during walking, squatting, sit-to-stand, stair ascent, and stair descent in 21 patients one-year post total knee arthroplasty using two methods: direct computation using segment orientations (r_IMU), and an IMU-driven iCloud-based interactive lower limb model (m_IMU). This cross-sectional study compared computed knee angles and ROM to a gold-standard OMC and inverse kinematics method using Pearson's correlation coefficient (R) and root-mean-square-differences (RMSD). The r_IMU and m_IMU methods estimated sagittal plane knee angles with excellent correlation (>0.95) compared to OMC for walking, squatting, sit-to-stand, and stair-ascent, and very good correlation (>0.90) for stair descent. For squatting, sit-to-stand, and walking, the mean RMSD for r_IMU and m_IMU compared to OMC were <4 degrees, < 5 degrees, and <6 degrees, respectively but higher for stair ascent and descent (~12 degrees). Frontal and transverse plane knee kinematics estimated using r_IMU and m_IMU showed poor to moderate correlation compared to OMC. There were no differences in ROM measurements during squatting, sit-to-stand, and walking across the two methods. Thus, IMUs can measure sagittal plane knee angles and ROM with high accuracy for a variety of tasks and may be a useful in-clinic tool for objective assessment of knee function following total knee arthroplasty.

Soil moisture mapping from SMOS: evaluating the accuracy of the operational product in a Mediterranean setting

ABSTRACT The present study provides a thorough evaluation of the SMOS surface soil moisture (SSM) product in a typical Mediterranean setting in Greece. For this purpose, a total of 4 agricultural sites were used for which co-orbital in-situ measurements from ground SSM sensors were available for year 2020. In this context, the effect of topographical and geomorphological features, land use/cover and the satellite orbit type and the Radio Frequency Interference (RFI) were also examined. A series of statistical metrics were computed, which allowed evaluating the agreement between the 2 datasets. In overall, results showed a reasonable agreement in specific land use/cover types between the SMOS product and the corresponding in-situ measurements obtained from the 0–5 cm soil moisture layer. In most cases, Root Mean Square Difference (RMSD) was close to 0.15 m3 m−3 (minimum 0.126 m3 m−3). Tomato and vineyard showed a satisfactory agreement compared to walnut and cotton crops. The autumn period had the highest agreement for tomato crop. The effect of RFI was also quite noticeable, as after the exclusion of pixels with high RFI, statistical agreement was noticeably improved. This study is, to our knowledge, one of the few that investigates in a Greek setting the accuracy of the SMOS product. The study results can contribute to the understanding of the practical value of the SMOS product in agricultural and arid/semi-arid Mediterranean environments while support efforts ongoing globally aiming at improving the SMOS SSM product accuracy.

Feasibility of monitoring floodplain on-farm water storages by integrating airborne and satellite LiDAR altimetry with optical remote sensing

Small water storages (≤ 500 ha surface area) enhance water supply for agricultural, human and livestock consumption. Their oftentimes large numbers and wide geographic spread, plus inaccurate or absent in situ metering, make their inclusion in water resource management difficult. This research assessed the capabilities of satellite optical remote sensing and LiDAR altimetry to characterise small water storages used for irrigation, so they can be included in water resources assessments. Landsat and Sentinel-2 water maps were integrated with airborne and satellite LiDAR altimetry to assess: (i) locations; (ii) elevation-area-volume relations; (iii) dates of construction/decommissioning; (iv) actual evaporation losses; and (v) storage volume changes of floodplain on-farm water storages (FoFWS) used primarily for cotton irrigation in the semi-arid Barwon-Darling region (142,173 km2), Australia. These remotely sensed characteristics would enable the representation of FoFWS in river system models and in regional water management plans, thus enhancing socioeconomic and environmental outcomes. Results from Landsat water maps showcased the growth in FoFWS surface area. In January 1988 there were 17 FoFWS with a total maximum surface area of 101.89 ha. By December 2021, the then 105 FoFWS maximum surface area was 5587.68 ha; a 55-times increase. For the first time in a regional context, mean annual (2000/01–2020/21) standing water evaporation losses were estimated for all FoFWS. These accounted to 52.47 MCM/year, which is a substantial ∼27% of the estimated long-term mean regional irrigation diversion limit (189 MCM/year). Elevation measurements from satellite LiDAR altimetry data, specifically GEDI and ICESat-2, were evaluated to assess if water volumes in FoFWS can be estimated through elevation-area-volume relations from a 1 m digital elevation model derived from airborne LiDAR data. Although GEDI's spatially variable footprint and ICESat-2's 91-day repeat cycle render them insufficient to monitor changes in water volumes in FoFWS, both satellite LiDAR altimeters provided accurate water level observations, with root mean squared differences (RMSD) of 0.38 m for GEDI and 0.12 m for ICESat-2, when compared to in situ water level measurements. ICESat-2's 0.7 m along-track sampling interval also detected changes of FoFWS structural features, such as increases in wall heights in recent years. This study demonstrated the value of integrating high-resolution airborne LiDAR data with satellite altimetry (LiDAR and/or radar interferometry), along with satellite optical water maps and rates of actual evaporation, to monitor FoFWS and other similar individually small yet collectively large reservoirs. Where similar data are available globally, then accurate information about small and dispersed water storages across poorly metered irrigated agricultural landscapes can be retrieved. Doing so will improve water resource management in the Barwon-Darling region, and in similar semi-arid and arid areas globally where irrigated agriculture is present.