Intensity Errors Research Articles

Identification Method for Cone Yarn Based on the Improved Faster R-CNN Model

To solve the problems of high labor intensity, low efficiency, and frequent errors in the manual identification of cone yarn types, in this study five kinds of cone yarn were taken as the research objects, and an identification method for cone yarn based on the improved Faster R-CNN model was proposed. In total, 2750 images were collected of cone yarn samples in real of textile industry environments, then data enhancement was performed after marking the targets. The ResNet50 model with strong representation ability was used as the feature network to replace the VGG16 backbone network in the original Faster R-CNN model to extract the features of the cone yarn dataset. Training was performed with a stochastic gradient descent approach to obtain an optimally weighted file to predict the categories of cone yarn. Using the same training samples and environmental settings, we compared the method proposed in this paper with two mainstream target detection algorithms, YOLOv3 + DarkNet-53 and Faster R-CNN + VGG16. The results showed that the Faster R-CNN + ResNet50 algorithm had the highest mean average precision rate for the five types of cone yarn at 99.95%, as compared with the YOLOv3 + DarkNet-53 algorithm with a mean average precision rate that was 2.24% higher and the Faster R-CNN + VGG16 algorithm with a mean average precision that was 1.19% higher. Regarding cone yarn defects, shielding, and wear, the Faster R-CNN + ResNet50 algorithm can correctly identify these issues without misdetection occurring, with an average precision rate greater than 99.91%.

Experimental assessment of noise robustness of the forward-additive, symmetric-additive and the inverse-compositional Gauss-Newton algorithm in digital image correlation

Digital Image Correlation (DIC) is a well-known optical experimental method based on the assumption that pixel intensity does not change with motion. This assumption is never exactly satisfied because various sources of noise exist (read-, shot-, dark-noise of the Charge Coupled Device (CCD), thermal dilatation of lens, non-constant illumination,...); moreover, to accurately track displacements, pixel intensity has to be interpolated at non-integer locations, thus introducing both phase and intensity errors due to the use of theoretically incorrect interpolating kernels.DIC can be implemented using various formulations, the most used being the Forward-Additive Gauss-Newton (FA-GN) and the Inverse-Compositional Gauss-Newton formulation (IC-GN). Even though both formulations give the same results at the first order, their speed, converging characteristics, and noise robustness differ considerably in particular with regard to the noise bias. Indeed, Shao et al. [1], using a theoretical analysis of a mono-dimensional signal and a numerical simulation on FFT-shifted images, recently showed that the latter formulation is not affected by noise bias, providing the correct differential operator is used.In this work, a fully experimental assessment of the noise-bias sensitivity of both algorithms is performed, confirming the theoretical findings and the numerical simulations available in the literature.

Simulations of an extreme rainstorm event (1056.7 mm/day) along the South China coast: The effect of single- and double-moment cloud microphysics schemes on precipitation

An extreme rainstorm event (1056.7 mm/day) along the South China coast in summer 2018 is investigated using WRF model. We focused on the effects of single-moment WSM6/7 and double-moment WDM6/7 microphysics schemes on the extreme precipitation. (1) The precipitation area, rain band movements and changes in hourly rainfall intensity could be robustly simulated in the 4 experiments. The performance of each scheme varied across different grades of precipitation. WDM7 scheme simulated precipitation extremes and hourly rain intensity that were closest to observations and produced relatively higher scores for light rain; WSM6 scheme had relatively higher scores for moderate rain; and WSM7 scheme showed higher scores for heavy rain and rainstorm. (2) Compared with the single-moment scheme, the double-moment scheme showed higher cloud water and hail mixing ratios. There was no significant difference in cloud water and snow mixing ratios. (3) Schemes incorporating the hail particles showed a larger cloud water mixing ratio but a smaller graupel mixing ratio. Incorporating hail particles caused an increase in simulated precipitation intensity and smaller simulation errors. (4) The increase in graupel mixing ratios was correspondent to the three precipitation peaks. The temporal evolutions of graupel, hail and snow mixing ratios are similar to that of precipitation, with the peak of cloud water mixing ratio appearing earlier than that of precipitation and the peak of ice mixing ratio appearing later than that of precipitation.

Long‐Term and Fine‐Scale Surface Urban Heat Island Dynamics Revealed by Landsat Data Since the 1980s: A Comparison of Four Megacities in China

AbstractLong‐term and fine‐scale monitoring of surface urban heat island (SUHI) is critical for the design of heat mitigation strategies. Landsat series offer long‐term (since the 1980s) and fine‐scale land surface temperature (LST) observations for such SUHI analysis. However, Landsat data are characterized by a long revisit period (16 days) and are seriously impacted by cloud contamination and stripe gaps, making the Landsat‐derived long‐term SUHI trends across cities incomparable. To address this issue, here we applied the Prophet model to reconstruct temporally consistent long‐term (∼1985–2019) clear‐sky Landsat LSTs over four megacities in China (Beijing, Chongqing, Shanghai, and Shenzhen). The results show that the mean absolute error of the SUHI intensity (SUHII) estimated using the reconstructed LSTs ranges from 0.2 to 0.6°C. The reliability of the reconstructed LSTs is further evidenced by the general consistency between the reconstructed Landsat and original Moderate Resolution Imaging Spectroradiometer LSTs. Our analysis further demonstrates that the overall SUHII has been increasing slowly over Beijing but has been increasing rapidly in the other three megacities since the 1980s. Local SUHII trends also varied between urban regions. Urban core is characterized by an initial increase and then a slight decrease in the local SUHII, while a continuously increasing trend is observed for the urban fringe. For the new urban development, the interannual SUHII trend lies somewhere between these trends. Overall, our study provides a practical approach to reconstruct long‐term clear‐sky Landsat LSTs, and delivers a better understanding of long‐term and fine‐scale SUHI variations across cities.

Tropical cyclone track and intensity prediction skill of GFS model over NIO during 2019 & 2020

The Tropical Cyclone (TC) track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors, which is beneficial for giving the model guidance-based real-time cyclone warning advisories. This study has attempted to verify the Global Forecast System (GFS) model forecasted tropical cyclone track and intensity over the North Indian Ocean (NIO) for the years 2019 and 2020. GFS is one of the operational models in the India Meteorological Department (IMD), which provides the medium-range weather forecast up to 10 days. The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory (GFDL). A total of 13 tropical cyclones formed over the North Indian Ocean, eight during 2019 and five in 2020 have been considered in this study. The accuracy of the model predicted tracks and intensity is verified for five days forecasts (120 h) at 6-h intervals; the track errors are verified in terms of Direct Position Error (DPE), Along Track Error (ATE) and Cross-Track Error (CTE). The annual mean DPE over NIO during 2019 (51–331 km) is lower than 2020 (82–359 km), and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020. The positive ATE (76–332 km) indicates the predicted track movement is faster than the observed track during both years. The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years. The cyclone Intensity forecast for the maximum sustained wind speed (MaxWS) and central mean sea level pressure (MSLP) are verified in terms of mean error (ME) and root mean square error (RMSE). The errors are lead time independent. However, most of the time model under-predicted the cyclone intensity during both years. Finally, there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.

Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data.

In the arid grasslands of northern China, unreasonable grazing methods can reduce the water content and species numbers of grassland vegetation. This project uses solar-powered GPS collars to obtain track data for sheep grazing. In order to eliminate the trajectory data of the rest area and the drinking area, the kernel density analysis method was used to cluster the trajectory point data. At the same time, the vegetation index of the experimental area, including elevation, slope and aspect data, was obtained through satellite remote sensing images. Therefore, using trajectory data and remote sensing image data to establish a neural network model of grazing intensity of sheep, the accuracy of the model could be high. The results showed that the best input parameters of the model were the combination of vegetation index, sheep weight, duration, moving distance and ambient temperature, where the coefficient of determination , and the mean square error MSE = 0.73. The error of grazing intensity obtained by the model is the smallest, and the spatial-temporal distribution of grazing intensity can reflect the actual situation of grazing intensity in different locations. Monitoring the grazing behavior of sheep in real time and obtaining the spatial-temporal distribution of their grazing intensity can provide a basis for scientific grazing.

Robust Registration of Medical Images in the Presence of Spatially-Varying Noise

Spatially-varying intensity noise is a common source of distortion in medical images and is often associated with reduced accuracy in medical image registration. In this paper, we propose two multi-resolution image registration algorithms based on Empirical Mode Decomposition (EMD) that are robust against additive spatially-varying noise. EMD is a multi-resolution tool that decomposes a signal into several principle patterns and residual components. Our first proposed algorithm (LR-EMD) is based on the registration of EMD feature maps from both floating and reference images in various resolutions. In the second algorithm (AFR-EMD), we first extract a single average feature map based on EMD and then use a simple hierarchical multi-resolution algorithm to register the average feature maps. We then showcase the superior performance of both algorithms in the registration of brain MRIs as well as retina images. For the registration of brain MR images, using mutual information as the similarity measure, both AFR-EMD and LR-EMD achieve a lower error rate in intensity (42% and 32%, respectively) and lower error rate in transformation (52% and 41%, respectively) compared to intensity-based hierarchical registration. Our results suggest that the two proposed algorithms offer robust registration solutions in the presence of spatially-varying noise.

The Use of Composite GOES-R Satellite Imagery to Evaluate a TC Intensity and Vortex Structure Forecast by an FV3GFS-Based Hurricane Forecast Model

The HAFS model is an effort under the NGGPS and UFS initiatives to create the next generation of hurricane prediction and analysis system based on FV3-GFS. It has been validated extensively using traditional verification indicators such as tracker error and biases, intensity error and biases, and the radii of gale, damaging and hurricane strength winds. While satellite images have been used to verify hurricane model forecasts, they have not been used on HAFS. The community radiative transfer model CRTM is used to generate model synthetic satellite images from HAFS model forecast state variables. The 24 forecast snapshots in the mature stage of hurricane Dorian in 2019 are used to generate a composite model synthetic GOES-R infrared brightness image. The composite synthetic image is compared to the corresponding composite image generated from the observed GOES-R data, to evaluate the model forecast TC vortex intensity, size, and asymmetric structure. Results show that the HAFS forecast TC Dorian agrees reasonably well with the observation, but the forecast intensity is weaker, its overall vortex size smaller, and the radii of its eye and maximum winds larger than the observed. The evaluation results can be used to further improve the model. While these results are consistent with those obtained by traditional verification methods, evaluations based on composite satellite images provide an additional benefit with richer information because they have near-real-times spatially and temporally continuous high-resolution data with global coverage. Composite satellite infrared images could be used routinely to supplement traditional verification methods in the HAFS and other hurricane model evaluations. Note since this study only evaluated one hurricane, the above conclusions are only applicable to the model behavior of the mature stage of hurricane Dorian in 2019, and caution is needed to extend these conclusions to expect model biases in predicting other TCs. Nevertheless, the consistency between the evaluation using composite satellite images and the traditional metrics, of hurricane Dorian, shows that this method has the potential to be applied to other storms in future studies.

Offshore Surface Evaporation Duct Joint Inversion Algorithm Using Measured Dual-Frequency Sea Clutter

In this paper, high-precision joint inversion of evaporative duct based on dual frequency radar sea clutter data is analyzed to study the abnormal duct environment phenomenon that occurs over offshore surfaces. As the information of duct environment retrieved by radars with different frequencies is inconsistent, a joint optimization model with dynamic penalty factor is proposed, which can improve the degree of conformity between the measured clutter power and the modeled clutter power. Then, a parallel crossover quantum particle swarm optimization algorithm is used to jointly invert the objective function, which adaptively processes the inputs involved in the crossover, and effectively improves the convergence of the inversion. Compared with the single-frequency model commonly used in engineering, the average relative error of the duct height of the dual frequency joint optimization model is reduced by 3.13%, and the average relative error of the duct intensity is reduced by 6.34%, verifying the effectiveness of this method.

Optical field propagation model of ring-down cavity light field based on angular spectrum propagation theory and evaluation criterion of cavity tuning

In cavity ring-down technique, cavity maladjustment has an essential effect on the measurement of intracavity loss. Several adjustment criterions have been adopted to achieve the optimal cavity state. However, experimental study shows that these criterions may correspond to different cavity states, which means that there is discrepancy between different criterions. In view of this problem, a model of intracavity propagation of Gaussian beam is established based on the angular spectrum propagation theory. This model is tested by numerical simulation and experimental research together. In the simulation, the true value of intracavity loss can be known beforehand. The two-dimensional angular scanning is carried out for certain cavity mirror. The two-dimensional distributions of the measure value of intracavity loss and the transmission light intensity are obtained simultaneously. These distributions are both nonlinear and multi-extremum, which will doubtlessly increase the difficulty in realizing the cavity adjustment. By comparing the distributions , we do find the discrepancy between the largest transmission light intensity and the least measured intracavity loss. Meanwhile both of these two states may be not corresponding to the true value in fact. After statistical studies, the relative error of the least measured intracavity loss is (–37.01±11.79) ppm, whereas the relative error of the largest transmission intensity is (–2.70±0.89) ppm. The criterion of the largest transmission intensity shows better stability and repeatability. This model is further tested in a folded cavity ring-down setup. The similar scanning procedure is carried out. A major problem in the experiment is that the true value of intracavity loss cannot be known. So only the repeatability precision of the measured intracavity loss can be analyzed. The statistical results of the largest light intensity and the least measured intracavity loss are ±29.32 ppm and ±70.71 ppm, respectively. The criterion of the largest transmission intensity has better repeatability, which is basically consistent with the simulation result. In this way the rationality of this model can be verified to some degree. In this paper, the criterion of the largest transmission intensity is recommended in the cavity ring-down technique. Furthermore, this model can be a reference for the research of intracavity optical field response, intracavity optical field transmission, unstable resonator alignment, etc.

An Optimizing Diffusion Kernel-Based Binary Encoding Strategy With Genetic Algorithm for Fringe Projection Profilometry

The binary encoding fringe projection profilometry has the potential of achieving high-speed 3-D measurement. Error diffusion is one of the most widely investigated binary encoding techniques. However, the existing error diffusion usually utilizes the same diffusion kernel to encode sinusoidal (S) fringe patterns without considering the fringe pitch-widths, leading to inevitable measurement error due to the demodulated phase error. In this article, a fringe binary encoding strategy based on genetic algorithm is proposed to reduce the phase error recovered by the temporal phase unwrapping (TPU) method. We design an optimization equation that combines phase error and intensity error to calculate the encoding error, which ensures that the optimized diffusion kernel is robust to defocusing changes. Both simulation and experiments have proved the effectiveness and accurateness of the proposed method. Comparative experiments indicate that the extracted phase error is greatly reduced for two types of extensively adopted TPU methods at different defocusing levels. Especially for multi wavelength method, the phase error can be reduced by up to 68%.

Sensitivity Criterion and Law on a Navigation Receiver under Single Frequency Electromagnetic Radiation

To objectively assess the anti-electromagnetic interference ability of the navigation receiver, the sensitivity criterion of a certain type of navigation receiver is tested under single-frequency continuous wave electromagnetic radiation with an optimized testing method. The experimental results show that it is difficult to guarantee the accuracy of the measurement value of the sensitivity level by using the carrier-to-noise density ratio (C/N0) as the sensitivity criterion, but the initial C/N0 of each satellite can be used as the basis for identifying whether the navigation system has recovered from the interference. The experimental error is dramatically decreased when the sensitivity criterion of “the sensitive phenomenon appears within the first 4 s, and the loss of positioning lasts for 30 s” is employed, the variable interference power step size is adopted and all of the satellites C/N0 are required to recover to the initial value after an interference. The critical interference field intensity error can be controlled within 1 dB by using all these measures. The sensitivity law of the navigation receiver is the same under different working signal intensities. It is significantly sensitive in the working frequency bandwidth. It is also quite sensitive in −11.5 MHz~55.5 MHz of the frequency offset range. The positive sensitive bandwidth is about 5 times that of the negative sensitive bandwidth.

3D Fourier ghost imaging via semi-calibrated photometric stereo.

We achieved three-dimensional (3D) computational ghost imaging with multiple photoresistors serving as single-pixel detectors using the semi-calibrated lighting approach. We performed imaging in the spatial frequency domain by having each photoresistor obtain the Fourier spectrum of the scene at a low spectral coverage ratio of 5%. To retrieve a depth map of a scene, we inverted, apodized, and applied semi-calibrated photometric stereo (SCPS) to the spectra. At least 93.5% accuracy was achieved for the 3D results of the apodized set of images applied with SCPS in comparison with the ground truth. Furthermore, intensity error map statistics obtained at least 97.0% accuracy for the estimated surface normals using our method. Our system does not need special calibration objects or any additional optical components to perform accurate 3D imaging, making it easily adaptable. Our method can be applied in current imaging systems where multiple detectors operating at any wavelength are used for two-dimensional (2D) imaging, such as imaging cosmological objects. Employing the idea of changing light patterns to illuminate a target scene and having stored information about these patterns, the data retrieved by one detector will give the 2D information while the multiple-detector system can be used to get a 3D profile.

Optimization of physical schemes in WRF model on cyclone simulations over Bay of Bengal using one-way ANOVA and Tukey\u2019s test

Evaluation of appropriate physics parameterization schemes for the Weather Research and Forecasting (WRF) model is vital for accurately forecasting tropical cyclones. Three cyclones Nargis, Titli and Fani have been chosen to investigate the combination of five cloud microphysics (MP), three cumulus convection (CC), and two planetary boundary layer (PBL) schemes of the WRF model (ver. 4.0) with ARW core with respect to track and intensity to determine an optimal combination of these physical schemes. The initial and boundary conditions for sensitivity experiments are drawn from the National Centers for Environmental Prediction (NCEP) global forecasting system (GFS) data. Simulated track and intensity of three cyclonic cases are compared with the India Meteorological Department (IMD) observations. One-way analysis of variance (ANOVA) is applied to check the significance of the data obtained from the model. Further, Tukey’s test is applied for post-hoc analysis in order to identify the cluster of treatments close to IMD observations for all three cyclones. Results are obtained through the statistical analysis; average root means square error (RMSE) of intensity throughout the cyclone period and time error at landfall with the step-by-step elimination method. Through the elimination method, the optimal scheme combination is obtained. The YSU planetary boundary layer with Kain–Fritsch cumulus convection and Ferrier microphysics scheme combination is identified as an optimal combination in this study for the forecasting of tropical cyclones over the Bay of Bengal.

Prior image-guided cone-beam computed tomography augmentation from under-sampled projections using a convolutional neural network.

Acquiring sparse-view cone-beam computed tomography (CBCT) is an effective way to reduce the imaging dose. However, images reconstructed by the conventional filtered back-projection method suffer from severe streak artifacts due to the projection under-sampling. Existing deep learning models have demonstrated feasibilities in restoring volumetric structures from the highly under-sampled images. However, because of the inter-patient variabilities, they failed to restore the patient-specific details with the common restoring pattern learned from the group data. Although the patient-specific models have been developed by training models using the intra-patient data and have shown effectiveness in restoring the patient-specific details, the models have to be retrained to be exclusive for each patient. It is highly desirable to develop a generalized model that can utilize the patient-specific information for the under-sampled image augmentation. In this study, we proposed a merging-encoder convolutional neural network (MeCNN) to realize the prior image-guided under-sampled CBCT augmentation. Instead of learning the patient-specific structures, the proposed model learns a generalized pattern of utilizing the patient-specific information in the prior images to facilitate the under-sampled image enhancement. Specifically, the MeCNN consists of a merging-encoder and a decoder. The merging-encoder extracts image features from both the prior CT images and the under-sampled CBCT images, and merges the features at multi-scale levels via deep convolutions. The merged features are then connected to the decoders via shortcuts to yield high-quality CBCT images. The proposed model was tested on both the simulated CBCTs and the clinical CBCTs. The predicted CBCT images were evaluated qualitatively and quantitatively in terms of image quality and tumor localization accuracy. Mann-Whitney U test was conducted for the statistical analysis. P<0.05 was considered statistically significant. The proposed model yields CT-like high-quality CBCT images from only 36 half-fan projections. Compared to other methods, CBCT images augmented by the proposed model have significantly lower intensity errors, significantly higher peak signal-to-noise ratio, and significantly higher structural similarity with respect to the ground truth images. Besides, the proposed method significantly reduced the 3D distance of the CBCT-based tumor localization errors. In addition, the CBCT augmentation is nearly real-time. With the prior-image guidance, the proposed method is effective in reconstructing high-quality CBCT images from the highly under-sampled projections, considerably reducing the imaging dose and improving the clinical utility of the CBCT.

Physics-based learning with channel attention for Fourier ptychographic microscopy.

Fourier ptychographic microscopy (FPM) is a computational imaging technology for large field-of-view, high resolution and quantitative phase imaging. In FPM, low-resolution intensity images captured with angle-varying illumination are synthesized in Fourier space with phase retrieval approaches. However, system errors such as pupil aberration and light-emitting diode (LED) intensity error seriously affect the reconstruction performance. In this article, we propose a physics-based neural network with channel attention for FPM reconstruction. With the channel attention module, which is introduced into physics-based neural networks for the first time, the spatial distribution of LED intensity can be adaptively corrected. Besides, the channel attention module is used to synthesize different Zernike modes and recover the pupil function. Detailed simulations and experiments are carried out to validate the effectiveness and robustness of the proposed method. Theresults demonstrate that our method achieves better performance in high-resolution complex field reconstruction, LED intensity correction and pupil function recovery compared with the state-of-art methods. The combination with deep neural network structures like channel attention modules significantly enhance the performance of physics-based neural networks and will promote the application of FPM in practical use.

The implementation of genetic algorithm for the identification of DNAPL sources

Contamination caused by dense non-aqueous phase liquids (DNAPLs) is a serious peril to the groundwater supply quality. The DNAPLs are largely undetected and yet are likely to be a significant factor limiting site remediation. It is necessary to identify the sources of pollution and control pollution in the early stage. The primary objective of this research was to investigate the pollution source and intensity of migrating DNAPLs. This objective was accomplished through the combination of genetic algorithm (GA) and T2VOC numerical experimental simulation. This study considers two synthetic cases of numerical modeling based on the T2VOC from the TOUGH2 code family to assess and evaluate the efficacity of the algorithm. The first case identified concurrently the intensity as well as the location of the one unknown contaminated water source. The second case distinguished various contaminated water sources. For the single source pollution identification, the estimation of intensity error was 0.08%, and the error of GA at second and third true value calculation was 0.05% and 0.03%, respectively, achieving the goal of pollution identification accurately. While for the multiple sources, the deviation of intensity was ranging between 1.02 and 5.65%, the results suggested that source No.2 was mainly responsible for the leakage, and source No.3 adopted the secondary responsibility among others. These two cases suggested that GA established by optimal search strategy could be adopted for effective identification of DNAPL pollution sources. This study provides information that could facilitate the development and implementation of innovative remediation strategies.

Spatial and Time Warping for Gauge Adjustment of Rainfall Estimates

Many satellite-based estimates use gauge information for bias correction. In general, bias-correction methods are focused on the intensity error and do not explicitly correct possible position or timing errors. However, position and timing errors in rainfall estimates can also lead to errors in the rainfall occurrence or the intensity. This is especially true for localized rainfall events such as the convective rainstorms occurring during the rainy season in sub-Saharan Africa. We investigated the use of warping to correct such errors. The goal was to gauge-adjust satellite-based estimates with respect to the position and the timing of the rain event, instead of its intensity. Warping is a field-deformation method that transforms an image into another one. We compared two methods, spatial warping focusing on the position errors and time warping for the timing errors. They were evaluated on two case studies: a synthetic rainfall event represented by an ellipse and a rain event in southern Ghana during the monsoon season. In both cases, the two warping methods reduced significantly the respective targeted (position or timing) errors. In the southern Ghana case, the average position error was decreased by about 45 km by the spatial warping and the average timing error was decreased from more than 1 h to 0.2 h by the time warping. Both warping methods also improved the continuous statistics on the intensity: the correlation went from 0.18 to at least 0.62 after warping in the southern Ghana case. The spatial warping seems more interesting because of its positive impact on both position and timing errors.

Relationships between macroseismic intensity and peak ground acceleration and velocity for the Vrancea (Romania) subcrustal earthquakes

The goal of this paper is to develop a new empirical relationship between observed macroseismic intensity and strong ground motion parameters such as peak ground acceleration (PGA) and velocity (PGV) for the Vrancea subcrustal earthquakes. The recent subcrustal earthquakes provide valuable data to examine these relationships for Vrancea seismogenic region. This region is one of the most active seismic zones in Europe and it is well-known for the strong subcrustal earthquakes. We examine the correlation between the strong ground-motion records and the observed intensities for major and moderate earthquakes with Mw ≥ 5.4 and epicentral intensity in the range VI to IX MSK degrees that occurred in Vrancea zone in the period 1977-2009. The empirical relationships between maximum intensity and ground parameters obtained and published by various authors have shown that these parameters do not always show a one-to-one correspondence, and the errors associated with the intensity estimation from PGA/PGV are sometimes +/-2 MSK degree. In the present study, the relation between macroseismic intensity and PGA/PGV will be given both as a mathematical equation, but also as corresponding ground motion intervals. Because of the intensity data spreading and errors related to mathematical approximations, it is necessary to systematically monitor not only the acceleration and velocity but also all the other ground motion parameters. The mathematical relation between these parameters might be used for the rapid assessment of ground shaking severity and potential damages in the areas affected by the Vrancea earthquakes.

Comparative performance of HWRF model coupled with POM and HYCOM for tropical cyclones over North Indian Ocean

The demonstration of air-sea interaction in the high-resolution numerical weather prediction (NWP) model is the necessary condition to predict the extreme event like tropical cyclone. It is already established that the coupled mesoscale models are capable of forecasting the track and intensity of tropical cyclones with reasonable skill over North Indian Ocean (NIO) basin and over other ocean basins as well. The Hurricane Weather Research and Forecasting (HWRF) model is consequently operational by India Meteorological Department (IMD) for the forecasting tropical cyclones like many other National Meteorological and Hydrological Services. Two different coupled versions of HWRF modeling systems with feature based Princeton Ocean Model (POM) and eddy-resolving Hybrid Coordinate Ocean Model (HYCOM) are employed for the real-time forecasting of tropical cyclones over NIO. In this study, the comparison of forecast skills between two coupled variants in predicting track and intensity for all eight cyclones during 2019 has been carried out. The results show that the track prediction of all cyclones by HWRF model coupled with HYCOM is comparatively better than that with POM. But the intensity prediction skills of both coupled models do not portray any obvious superiority for all forecast hours. The absolute intensity errors of HWRF model with POM coupling are large compared to HYCOM during initial phase up to 60 hours of forecast except for Vayu cyclone. But, the errors in intensity exhibited by POM are a bit smaller compared to HYCOM for longer lead period. However, the standard deviation is very large for both models and numbers of forecasts verified are also less. In case of two cyclones of 2020, the ability of coupled variant with HYCOM is superior to POM in forecasting the tracks of cyclones. Considering the skills of both the models for intensity prediction, it is clear that the use of coupled HCOM model for short-lived weaker storms is advantageous but the performance of the model is not consistently maintained with longer lead time for intense and long-lived cyclone. A brief assessment of surface fluxes and Sea Surface Temperature reveals that HYCOM portray more realistic ocean condition in turn surface fluxes compared to POM and therefore the coupled variant of HWRF with HYCOM is superior to the variant with POM while forecasting cyclones over deep Ocean.