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At present, the majority of techniques for assessing image quality are limited to extracting features from an image in a single space. This paper proposes a new dual-space multi-feature fusion based method for full-reference image quality assessment. This method involves simultaneously extracting features from both the YIQ and L*a*b* color spaces. First, we extract the luminance, slope, chroma, and gradient features in the spatial domain of the image to describe the salient differences in the image. Second, based on contrast sensitivity characteristics, we extract spatial frequency features in the spatial domain of the image to represent frequency differences in the image. Next, merge the features extracted in the dual space to construct a quality perception feature vector. Finally, the feature vector is input into the Random Forest model for regression prediction to obtain the predicted score of the image. Many experiments have been carried out on the four public datasets, and contrasted with other methods. The experimental confirm that the proposed method predicts image quality more accurately. The MATLAB source code and dataset of this paper will be published on GitHub, and the corresponding author can be contacted if necessary. [ABSTRACT FROM AUTHOR]

To effectively reduce model bias and improve assimilation quality, we adopt a hybrid adaptive approach of ensemble adjustment Kalman filter (EAKF) and multigrid analysis (MGA), called EAKF-MGA, to implement parameter optimization as follows. For each assimilation cycle, observations are used to adjust the prior ensembles of both state variables and parameters using the EAKF without inflation. Then, the MGA is adaptively triggered to extract multiscale information from the observational residual to innovate the ensemble mean of the state once again. Results of biased twin experiments consisting of a barotropic spectral model and idealized observation systems show that the proposed EAKF-MGA is insensitive to state variance inflation and localization during the parameter optimization process, compared with the EAKF with adaptive inflation. We also find that computational efficiency is another important advantage of the EAKF-MGA for both state estimation and parameter estimation since extremely small ensemble size is allowed, while the EAKF with adaptive inflation does not work anymore. In essence, the EAKF-MGA is designed to estimate and correct systematic errors jointly with model's state variables. Through alleviating biases, including the model bias caused by the biased parameter and the analysis bias resulting from the sampling noise given the limited ensemble size, it can be guaranteed that the analysis in the EAKF-MGA will be proceeded onward with the standard assumption of the unbiased model background field in modern data assimilation theory to be met. [ABSTRACT FROM AUTHOR]

Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a "twin" experiment framework, the SST "observations" generated by a "truth" model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the "truth" values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions. [ABSTRACT FROM AUTHOR]

Probabilistic forecasts, which are usually initialized by an ensemble Kalman filter (EnKF), are known to be better than deterministic (or one member) forecasts for the El Niño-Southern Oscillation (ENSO) phenomenon. Because of sampling errors caused by a finite ensemble and the errors related to model biases associated with the physical parameterizations, dynamic core, model resolution, and so on, a state-of-the-art inflation method is commonly used in the standard EnKF to increase the prior variance so as to avoid filter divergence. However, the optimal inflation factor is almost prohibitive in reality because of vast computational cost. An adaptive EnKF and multigrid analysis hybrid approach without inflation is presented to compensate for the abovementioned limitations of the standard EnKF. In this study, the adaptive approach is applied to an intermediate coupled model for ENSO prediction. Gridded observations of daily-mean sea surface temperature (SST) anomalies from the Advanced Very High Resolution Radiometer (AVHRR) during January 1982-December 2012 are assimilated into the model to initialize a 2-yr ENSO hindcast. Results show that compared to the standard EnKF that uses multiplicative variance inflation, the adaptive method can reduce analysis errors by 63% for both the daily SST anomaly and the Niño-1+2 SST anomaly. The prediction skill of Niño-1+2 SST anomaly is consistently enhanced, especially for phase forecast. For SST anomaly forecasting, the advantage of the adaptive method mainly occurs in the eastern equatorial Pacific and the northern boundary of the intermediate coupled model. [ABSTRACT FROM AUTHOR]

A four-dimensional variational (4D-Var) data assimilation method is implemented in an improved intermediate coupled model (ICM) of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The 'observation' of the SST anomaly, which is sampled from a 'truth' model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction. [ABSTRACT FROM AUTHOR]

Extracting multiple-scale observational information is critical for accurately reconstructing the structure of mesoscale circulation systems such as typhoon. The Space and Time Mesoscale Analysis System (STMAS) with multigrid data assimilation developed in Earth System Research Laboratory (ESRL) in National Oceanic and Atmospheric Administration (NOAA) has addressed this issue. Previous studies have shown the capability of STMAS to retrieve multiscale information in 2-dimensional Doppler radar radial velocity observations. This study explores the application of 3-dimensional (3D) Doppler radar radial velocities with STMAS for reconstructing a 3D typhoon structure. As for the first step, here, we use an idealized simulation framework. A two-scale simulated “typhoon” field is constructed and referred to as “truth,” from which randomly distributed conventional wind data and 3D Doppler radar radial wind data are generated. These data are used to reconstruct the synthetic 3D “typhoon” structure by the STMAS and the traditional 3D variational (3D-Var) analysis. The degree by which the “truth” 3D typhoon structure is recovered is an assessment of the impact of the data type or analysis scheme being evaluated. We also examine the effects of weak constraint and strong constraint on STMAS analyses. Results show that while the STMAS is superior to the traditional 3D-Var for reconstructing the 3D typhoon structure, the strong constraint STMAS can produce better analyses on both horizontal and vertical velocities. [ABSTRACT FROM AUTHOR]

Model error is a major obstacle for enhancing the forecast skill of El Niño-Southern Oscillation (ENSO). Among three kinds of model error sources-dynamical core misfitting, physical scheme approximation and model parameter errors, the model parameter errors are treatable by observations. Based on the Zebiak-Cane model, an ensemble coupled data assimilation system is established to study the impact of parameter optimization (PO) on ENSO predictions within a biased twin experiment framework. 'Observations' of sea surface temperature anomalies drawn from the 'truth' model are assimilated into a biased prediction model in which model parameters are erroneously set from the 'truth' values. The degree by which the assimilation and prediction with or without PO recover the 'truth' is a measure of the impact of PO. Results show that PO improves ENSO predictability-enhancing the seasonal-interannual forecast skill by about 18 %, extending the valid lead time up to 33 % and ameliorating the spring predictability barrier. Although derived from idealized twin experiments, results here provide some insights when a coupled general circulation model is initialized from the observing system. [ABSTRACT FROM AUTHOR]

To further explore enthalpy-based sea-ice assimilation, a one-dimensional (1D) enthalpy sea-ice model is implemented into a simple pycnocline prediction model. The 1D enthalpy sea-ice model includes the physical processes such as brine expulsion, flushing, and salt diffusion. After being coupled with the atmosphere and ocean components, the enthalpy sea-ice model can be integrated stably and serves as an important modulator of model variability. Results from a twin experiment show that the sea-ice data assimilation in the enthalpy space can produce smaller root-mean-square errors of model variables than the traditional scheme that assimilates the observations of ice concentration, especially for slow-varying states. This study provides some insights into the improvement of sea-ice data assimilation in a coupled general circulation model. [ABSTRACT FROM AUTHOR]

Although the fixed covariance localization in the ensemble Kalman filter (EnKF) can significantly increase the reliability of background error covariance, it has been demonstrated that extreme impact radii can cause the EnKF to lose some useful information. Tuning an optimal impact radius, on the other hand, is always difficult for a general circulation model. The EnKF multiscale analysis (MSA) approach was presented to make up for the above-mentioned drawback of the fixed localization. As a follow-up, this study presents an adaptive compensatory approach to further improve the performance of the EnKF-MSA. The new method adaptively triggers a multigrid analysis (MGA) to extract multiscale information from the observational residual after the EnKF without inflation is completed at each analysis step. Within a biased twin experiment framework consisting of a barotropic spectral model and an idealized observing system, the performance of the adaptive method is examined. Results show that the MGA reduces the computational cost of the MSA by 93%. On the assimilation quality, the adaptive method has an incremental improvement over the EnKF-MSA. That is, the adaptive EnKF-MGA reduces to the EnKF without inflation, which is better than the EnKF-MSA, for moderate impact radii. The proposed scheme works for a broader range of impact radii than the standard EnKF (i.e., the EnKF with inflation). For extreme impact radii, the adaptive EnKF-MGA can produce smaller assimilation errors than the standard EnKF and shorten the spinup period by 53%. In addition, the computational cost of the MGA is negligible relative to that of the standard EnKF. [ABSTRACT FROM AUTHOR]

Coupling parameter estimation (CPE) that uses observations to estimate the parameters in a coupled model through error covariance between variables residing in different media may increase the consistency of estimated parameters in an air-sea coupled system. However, it is very challenging to accurately evaluate the error covariance between such variables due to the different characteristic time scales at which flows vary in different media. With a simple Lorenz-atmosphere and slab ocean coupled system that characterizes the interaction of two-timescale media in a coupled “climate” system, this study explores feasibility of the CPE with four-dimensional variational analysis and ensemble Kalman filter within a perfect observing system simulation experiment framework. It is found that both algorithms can improve the representation of air-sea coupling processes through CPE compared to state estimation only. These simple model studies provide some insights when parameter estimation is implemented with a coupled general circulation model for improving climate estimation and prediction initialization. [ABSTRACT FROM AUTHOR]

While fixed covariance localization can greatly increase the reliability of the background error covariance in filtering by suppressing the long-distance spurious correlations evaluated by a finite ensemble, it may degrade the assimilation quality in an ensemble Kalman filter (EnKF) as a result of restricted longwave information. Tuning an optimal cutoff distance is usually very expensive and time consuming, especially for a general circulation model (GCM). Here the authors present an approach to compensate the demerit in fixed localization. At each analysis step, after the standard EnKF is done, a multiple-scale analysis technique is used to extract longwave information from the observational residual (referred to the EnKF ensemble mean). Within a biased twin-experiment framework consisting of a global barotropical spectral model and an idealized observing system, the performance of the new method is examined. Compared to a standard EnKF, the hybrid method is superior when an overly small/large cutoff distance is used, and it has less dependence on cutoff distance. The new scheme is also able to improve short-term weather forecasts, especially when an overly large cutoff distance is used. Sensitivity studies show that caution should be taken when the new scheme is applied to a dense observing system with an overly small cutoff distance in filtering. In addition, the new scheme has a nearly equivalent computational cost to the standard EnKF; thus, it is particularly suitable for GCM applications. [ABSTRACT FROM AUTHOR]

Coupled data assimilation uses a coupled model consisting of multiple time-scale media to extract information from observations that are available in one or more media. Because of the instantaneous exchanges of information among the coupled media, coupled data assimilation is expected to produce self-consistent and physically balanced coupled state estimates and optimal initialization for coupled model predictions. It is also expected that applying coupling error covariance between two media into observational adjustments in these media can provide direct observational impacts crossing the media and thereby improve the assimilation quality. However, because of the different time scales of variability in different media, accurately evaluating the error covariance between two variables residing in different media is usually very difficult. Using an ensemble filter together with a simple coupled model consisting of a Lorenz atmosphere and a pycnocline ocean model, which characterizes the interaction of multiple time-scale media in the climate system, the impact of the accuracy of coupling error covariance on the quality of coupled data assimilation is studied. Results show that it requires a large ensemble size to improve the assimilation quality by applying coupling error covariance in an ensemble coupled data assimilation system, and the poorly estimated coupling error covariance may otherwise degrade the assimilation quality. It is also found that a fast-varying medium has more difficulty being improved using observations in slow-varying media by applying coupling error covariance because the linear regression from the observational increment in slow-varying media has difficulty representing the high-frequency information of the fast-varying medium. [ABSTRACT FROM AUTHOR]

Observational information has a strong geographic dependence that may directly influence the quality of parameter estimation in a coupled climate system. Using an intermediate atmosphere-ocean-land coupled model, the impact of geographic dependent observing system on parameter estimation is explored within a 'twin' experiment framework. The 'observations' produced by a 'truth' model are assimilated into an assimilation model in which the most sensitive model parameter has a different geographic structure from the 'truth', for retrieving the 'truth' geographic structure of the parameter. To examine the influence of data-sparse areas on parameter estimation, the twin experiment is also performed with an observing system in which the observations in some area are removed. Results show that traditional single-valued parameter estimation (SPE) attains a global mean of the 'truth', while geographic dependent parameter optimization (GPO) can retrieve the 'truth' structure of the parameter and therefore significantly improves estimated states and model predictability. This is especially true when an observing system with data-void areas is applied, where the error of state estimate is reduced by 31 % and the corresponding forecast skill is doubled by GPO compared with SPE. [ABSTRACT FROM AUTHOR]

Because of the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere-ocean-land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin-experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation, and a simple land model. The assimilation model is biased by erroneously setting the values of all model parameters. The four most sensitive parameters identified by sensitivity studies are used to perform traditional single-value parameter estimation and new geographic-dependent parameter optimization. Results show that the new parameter optimization significantly improves the quality of state estimates compared to the traditional scheme, with reductions of root-mean-square errors as 41%, 23%, 62%, and 59% for the atmospheric streamfunction, the oceanic streamfunction, sea surface temperature, and land surface temperature, respectively. Consistently, the new parameter optimization greatly improves the model predictability as a result of the improvement of initial conditions and the enhancement of observational signals in optimized parameters. These results suggest that the proposed geographic-dependent parameter optimization scheme may provide a new perspective when a coupled general circulation model is used for climate estimation and prediction. [ABSTRACT FROM AUTHOR]

In recent years, high-resolution global ocean circulation models have begun to include astronomical tidal forcing to concurrently simulate ocean circulation, barotropic and internal tides. The inclusion of tides is of great significance for understanding three-dimensional (3-D) ocean mixing and developing internal tide prediction. However, due to uncertainty in topography and damping terms, improper representation of the self-attraction and loading term, barotropic tidal estimate cannot match that in the data-constrained barotropic tidal model. In this study, the TPXO8 data are assimilated to optimize the tides embedded in a global 9-km 3-D general circulation model of MITgcm as follows. For each time step of the MITgcm, the previous 25-hour-averaged sea surface height (SSH) is subtracted from the instantaneous SSH to obtain the MITgcm surface tidal elevation. Then, the difference of surface tidal elevation between the MITgcm and TPXO8 together with a damping coefficient acts as an additional term for the right-hand side of the SSH equation in order to correct the SSH field. Results show that the global M 2 (K 1) error of the corrected surface tidal elevation relative to the TPXO8 reaches 2.12 cm (0.89 cm). Compared with tide gauge data, the accuracy of surface tidal elevation after using the correction scheme is almost the same as that of TPXO8 for all areas except for some continental shelf and coastal areas. The internal tidal signals are subsequently improved compared to the along-track altimetry and TAO observations. Compared to previous tidal assimilation scheme, the implementation of the correction scheme used in this study is simple and more suitable for realistic applications. • Tide is embodied in the high resolution general circulation model MITgcm. • The model can reproduce barotropic and internal tides, eddies, general circulation. • The tidal assimilation scheme is investigated and used in the MITgcm. • The accuracy of barotropic and internal tides is significantly improved. [ABSTRACT FROM AUTHOR]

The accumulated remote sensing data of altimeters and scatterometers have provided new opportunities for ocean state forecasting and have improved our knowledge of ocean–atmosphere exchanges. Studies on multivariate, multistep, spatiotemporal sequence forecasts of sea level anomalies (SLA) for different modalities, however, remain problematic. In this paper, we present a novel hybrid and multivariate deep neural network, named HMnet3, which can be used for SLA forecasting in the South China Sea (SCS). First, a spatiotemporal sequence forecasting network is trained by an improved convolutional long short-term memory (ConvLSTM) network using a channelwise attention mechanism and multivariate data from 1993 to 2015. Then a time series forecasting network is trained by an improved long short-term memory (LSTM) network, which is realized by ensemble empirical mode decomposition (EEMD). Finally, the two networks are combined by a successive correction method to produce SLA forecasts for lead times of up to 15 days, with a special focus on the open sea and coastal regions of the SCS. During the testing period of 2016–18, the performance of HMnet3 with sea surface temperature anomaly (SSTA), wind speed anomaly (SPDA), and SLA data is much better than those of state-of-the-art dynamic and statistical (ConvLSTM, persistence, and climatology) forecast models. Stricter testbeds for trial simulation experiments with real-time datasets are investigated, where the eddy classification metrics of HMnet3 are favorable for all properties, especially for those of small-scale eddies. [ABSTRACT FROM AUTHOR]

Accurate wind speed forecasting is of great significance for the utilization of Arctic wind energy resources. The traditional single model is difficult to fully depict the nonlinearity of wind speed and its wide range of variations. In this paper, a hybrid model is proposed for multi-step wind speed forecasting, which combined complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), convolutional neural network (CNN) and long-short term memory neural network (LSTM). The wind speed series is firstly decomposed into several intrinsic mode functions (IMF) by CEEMDAN to provide more stable data. Secondly, four-step-ahead forecasts are realized using well-tuned CNN-LSTM model for each IMF. Finally, the forecasted wind speed is obtained by reconstruction. The effectiveness and feasibility of the proposed method is validated based on thorough evaluation and step-by-step analysis. The RMSE of the proposed model is 0.4046 m/s, which are reduced by 58 % compared with 7 benchmark models. Furthermore, the average prediction interval of the proposed model is also reduced by 20 %, 16 % and 7 % compared to CEEMDAN-FCNN, CEEMDAN-CNN and CEEMDAN-LSTM respectively. The results prove that all three parts of the proposed model contribute to a better performance in wind speed forecasting. [Display omitted] • CEEMDAN can reduce the nonlinearity of wind speed data and unstable IMFs have a large impact on the wind speed forecasting. • Combining LSTM with CNN can enhance feature extraction and improve the accuracy of multi-step wind speed forecasting. • The proposed model achieves more accurate forecasting for catastrophe points which provides more critical information. [ABSTRACT FROM AUTHOR]

Key clinical message: Here we reported a particular case of MUTYH‐associated polyposis (MAP) that had only one rare heterozygous variant, but some particular clinical manifestations contributed to occur in this male patient by only one defective MUTYH allele were worth of further investigation. We reported a case of MAP. It is about a 33‐year‐old man with chief complaints of hematochezia who had multiple polyps that were found in his colon via colonoscopy. He followed his doctor's advice and performed a genetic analysis examination. Germline test was positive for a major heterozygous variant: chr1:45800165 on the MUTYH gene. MUTYH gene sequence analysis confirmed the following heterozygous variant: c.55CT (p.R19X) in exon 2 (ClinVar NM_001128425). Unfortunately, his mother and daughter have the ILK variant according to genetic analysis. However, this variant at the site was not detected in his father. Various types of polyps were found on repeated colonoscopy, which tended to become latent cancerous in the future. This case indicated that awareness of the risk of carcinogenesis of polyps in carriers of monoallelic variants might accordingly increase, and our understanding of the type of genetically related disease will be enhanced by us. [ABSTRACT FROM AUTHOR]

Due to the relatively brief domestication history of sugar beet (Beta vulgaris ssp. vulgaris), our understanding of the genomic diversity and functional genes in its cultivars is limited, resulting in slow breeding progress. To address this issue, a total of 306 germplasm materials of major cultivars and breeding lines from China, the USA, and Europe were selected for genome resequencing. We investigated population structure and genetic diversity and performed selective scanning of genomic regions, identifying six novel genes associated with important agronomic traits: the candidate genes DFAX2 and P5CS for skin roughness; the candidate genes FRO5, GL24, and PPR91 for root yield and sugar yield, and the pleiotropic candidate gene POLX for flourishing growth vigour, plant height, crown size, flesh coarseness, and sugar yield. In addition, we constructed a protein–protein interaction network map and a phenotype-gene network map, which provide valuable information for identifying and characterizing functional genes affecting agronomic traits in sugar beet. Overall, our study sheds light on the future improvement of sugar beet agronomic traits at the molecular level. [ABSTRACT FROM AUTHOR]

This article discusses a case study of a 43-year-old female patient who presented with multiple gastric signet-ring cell carcinomas. The patient was diagnosed with both Sotos syndrome and Li-Fraumeni syndrome, which is rare as these syndromes are typically diagnosed in pediatric patients and rarely coexist in the same individual. Whole exon gene sequencing revealed abnormalities in the TP53 and NSD1 genes, which are associated with Li-Fraumeni and Sotos syndromes, respectively. The article highlights the unusual nature of this case and provides information about the typical manifestations of Li-Fraumeni syndrome. [Extracted from the article]

Due to the relatively brief domestication history of sugar beet (Beta vulgaris ssp. vulgaris), our understanding of the genomic diversity and functional genes in its cultivars is limited, resulting in slow breeding progress. To address this issue, a total of 306 germplasm materials of major cultivars and breeding lines from China, the USA, and Europe were selected for genome resequencing. We investigated population structure and genetic diversity and performed selective scanning of genomic regions, identifying six novel genes associated with important agronomic traits: the candidate genes DFAX2 and P5CS for skin roughness; the candidate genes FRO5, GL24, and PPR91 for root yield and sugar yield, and the pleiotropic candidate gene POLX for flourishing growth vigour, plant height, crown size, flesh coarseness, and sugar yield. In addition, we constructed a protein–protein interaction network map and a phenotype-gene network map, which provide valuable information for identifying and characterizing functional genes affecting agronomic traits in sugar beet. Overall, our study sheds light on the future improvement of sugar beet agronomic traits at the molecular level. [ABSTRACT FROM AUTHOR]

Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. With the development of artificial intelligence, research on ocean temperature prediction has made some progress. However, existing methods are mostly limited to temperature prediction of isolated points on ocean surface, with less vertical studies. And existing graph neural network-based methods typically use predefined graphs, which cannot adaptively capture unknown associations between data. In this paper, we propose a new adaptive spatiotemporal dynamic graph convolution network to predict three-dimensional sea water temperature. Combined with adaptive graph learning and k nearest neighbor clustering methods, the network can automatically mine unknown dependencies between sequences based on raw data without any prior knowledge. Temporal and spatial dependencies in time series are further captured using temporal convolution and graph convolution. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework for the three-dimensional seawater temperature prediction task. In this paper, the prediction experiment is carried out using the high-resolution three-dimensional temperature and salt datasets from the Copernicus Global Ocean Physics Reanalysis. The results showed that our method achieved the best predictive performance on all prediction scales compared to current mainstream methods, with MAE increasing by an average of 21.7% and RMSE of 23.3%. [ABSTRACT FROM AUTHOR]

Multivariate time series data with multiple points is a kind of spatio-temporal data, and in recent years, spatio-temporal graph neural networks (STGNNs) have become one of the most effective methods for spatio-temporal data prediction. By leveraging graph neural networks and sequence models, STGNNs simultaneously capture the hidden temporal and spatial patterns in spatio-temporal data. However, there exist two key limitations: (1) Most existing STGNNs whether based on static graphs or dynamic graphs, fail to introduce time periodicity into graph structure learning and ignore the importance of multi-relational graph structure learning. (2) Most multivariate time series prediction models with multiple points ignore the relation between multiple attributes. To this end, we propose a novel framework called the Dynamic Multi-fusion Spatio-temporal Graph Neural Network (DMF-STNet), where multi-fusion embodies two meanings: spatio-temporal fusion and multi-attribute fusion, as well as multi-graph fusion and multi-layer fusion. The dynamic periodic graph is proposed to mine time periodicity that is hidden in graph structures, and we further offer multi-relational graph structure learning methods to capture static and dynamic relations. Moreover, we introduce a dual-stage feature aggregation method to aggregate hidden features from both primary and auxiliary attributes. Extensive experiments on three real-world datasets demonstrate the efficacy of our proposed DMF-STNet, and the visualization study proves the interpretability of learned weights. • Modeling multivariate time series with multi-point. • Design a graph structure learning method with multi-relation. • Aggregating features through the dual-stage feature aggregation method. • Experiment results demonstrate the effectiveness of the proposed DMF-STNet. • Visualization study further proves the interpretability of learned weights. [ABSTRACT FROM AUTHOR]

The type VI protein secretion system ( T6SS) is essential for the virulence of several Gram-negative bacteria. In this study, we identified a T6SS gene cluster in Acidovorax citrulli, a plant-pathogenic bacterium that causes bacterial fruit blotch ( BFB) of cucurbits. One T6SS cluster, of approximately 25 kb in length and comprising 17 genes, was found in the A. citrulli AAC00-1 genome. Seventeen A. citrulli mutants were generated, each with a deletion of a single T6SS core gene. There were significant differences in BFB seed-to-seedling transmission between wild-type A. citrulli strain, xjl12, and Δ vas D, Δ imp K, Δ imp J and Δ imp F mutants (71.71%, 9.83%, 8.41%, 7.15% and 5.99% BFB disease index, respectively). In addition, we observed that these four mutants were reduced in melon seed colonization and biofilm formation; however, they were not affected in virulence when infiltrated into melon seedling tissues. There were no significant differences in BFB seed-to-seedling transmission, melon tissue colonization and biofilm formation between xjl12 and the other 13 T6SS mutants. Overall, our results indicate that T6SS plays a role in seed-to-seedling transmission of BFB on melon. [ABSTRACT FROM AUTHOR]

A coupled ocean–wave–sea spray model system is used to investigate the impacts of sea spray and sea surface roughness on the response of the upper ocean to the passage of the Super Typhoon Haitang. Sea spray–mediated heat and momentum fluxes are derived from an improved version of Fairall's heat fluxes formulation and Andreas's sea spray–mediated momentum flux models. For winds ranging from low to extremely high speeds, a new parameterization scheme for the sea surface roughness is developed, in which the effects of wave state and sea spray are introduced. In this formulation, the drag coefficient has minimal values over the right quadrant of the typhoon track, along which the typhoon-generated waves are longer, smoother, and older, compared to other quadrants. Using traditional interfacial air–sea turbulent (sensible, latent, and momentum) fluxes, the sea surface cooling response to Typhoon Haitang is overestimated by 1°C, which can be compensated by the effects of sea spray and ocean waves on the right side of the storm. Inclusion of sea spray–mediated turbulent fluxes and sea surface roughness, modulated by ocean waves, gives enhanced cooling along the left edges of the cooling area by 0.2°C, consistent with the upper ocean temperature observations. [ABSTRACT FROM AUTHOR]

A hybrid architecture model of multi-branch aware CNN and efficient transformer (MAET) is proposed and implemented for lightweight image super-resolution (SR). In the model, the multi-branch aware block (MAB) removes the redundant branches when their local spatial features are captured by other branches, while the efficient transformer block (ETB) applies the scaled cosine attention (SCA) to scale up the model capacity by generating mild attentional values from the pixel pairs. By removing the redundant branches and applying SCA to scale up the model capacity, we believe that the model is able to improve the performance while maintaining a low computation complexity. Specifically, MAET consists of a multi-branch aware CNN module (MACM) and an efficient transformer module (ETM). MACM is a lightweight CNN module composed of a serious of MABs to extract hierarchical local features. ETM is composed of ETBs to fully exploit global information by modeling long-term image dependencies to refine the texture details. ETB adopts the feature split strategy, residual post-normalization, and SCA for efficient multi-head attention. Extensive experiments demonstrate that the proposed MAET achieves better accuracy and visual improvements against the state-of-the-art lightweight image SR methods in terms of quantitative and qualitative evaluations. [ABSTRACT FROM AUTHOR]

Objective: To explore and construct a platform for the transformation from general pharmacists to information pharmacists using quality control circle (QCC ) activities. Methods: The QCC activities were applied to the whole training process of information pharmacists. Training started through the approach of having the pharmacists learn programming sentences, and program writing was finally completed after continuous exploration. Results: Through QCC activities, 3 information pharmacists were initially trained,the writing of 2 programs were completed and a set of standard training profiles was summarized. Conclusion: The QCC activities could be used for pharmacy management and personnel training. It is particularly the case when more difficult targets are set for the work. Compared with the general team,the team implementing the QCC is more resolute and clearer in work goals and more liable to complete the project. [ABSTRACT FROM AUTHOR]

Recent studies have started to explore coupled data assimilation (CDA) in coupled ocean–atmosphere models because of the great potential of CDA to improve climate analysis and seamless weather–climate prediction on weekly-to-decadal time scales in advanced high-resolution coupled models. In this review article, we briefly introduce the concept of CDA before outlining its potential for producing balanced and coherent weather–climate reanalysis and minimizing initial coupling shocks. We then describe approaches to the implementation of CDA and review progress in the development of various CDA methods, notably weakly and strongly coupled data assimilation. We introduce the method of coupled model parameter estimation (PE) within the CDA framework and summarize recent progress. After summarizing the current status of the research and applications of CDA-PE, we discuss the challenges and opportunities in high-resolution CDA-PE and nonlinear CDA-PE methods. Finally, potential solutions are laid out. [ABSTRACT FROM AUTHOR]

This paper investigates the energy optimization for high dynamic heterogeneous UAV swarm network. Due to the high dynamism of the UAV swarm’s topology, the approaches with high complexity, long computing time, or low converging speed would be inappropriate to efficiently form stable communication network. We propose an UAV relay selection coalition game model and the distributed fast UAV relay coalition formation algorithm to optimize the energy efficiency of the UAV swarm. First, we formulate the UAV relay selection problem as a coalition game model. Then we design a distributed fast UAV relay coalition formation algorithm which could converge to the stable state rapidly and optimize the swarm’s energy efficiency. The proposed approach could help UAV swarm form high energy efficient network in the high dynamism situation. [ABSTRACT FROM AUTHOR]

Abstract: Parametric uncertainty in convection parameterization is one major source of model errors that cause model climate drift. Convection parameter tuning has been widely studied in atmospheric models to help mitigate the problem. However, in a fully coupled general circulation model (CGCM), convection parameters which impact the ocean as well as the climate simulation may have different optimal values. This study explores the possibility of estimating convection parameters with an ensemble coupled data assimilation method in a CGCM. Impacts of the convection parameter estimation on climate analysis and forecast are analyzed. In a twin experiment framework, five convection parameters in the GFDL coupled model CM2.1 are estimated individually and simultaneously under both perfect and imperfect model regimes. Results show that the ensemble data assimilation method can help reduce the bias in convection parameters. With estimated convection parameters, the analyses and forecasts for both the atmosphere and the ocean are generally improved. It is also found that information in low latitudes is relatively more important for estimating convection parameters. This study further suggests that when important parameters in appropriate physical parameterizations are identified, incorporating their estimation into traditional ensemble data assimilation procedure could improve the final analysis and climate prediction. [ABSTRACT FROM AUTHOR]

This study addresses how to maintain oceanic mixing along potential density surface in ocean data assimilation (ODA). It is well known that the oceanic mixing across the potential density surface is much weaker than that along the potential density surface. However, traditional ODA schemes allow the mixing across the potential density surface and thus may result in extra assimilation errors. Here, a new ODA scheme that uses potential density gradient information of the model background to rescale observational adjustment is designed to improve the quality of assimilation. The new scheme has been tested using a regional ocean model within a multiscale 3-dimensional variational framework. Results show that the new scheme effectively prevents the excessive unphysical projection of observational information in the direction across potential density surface and thus improves assimilation quality greatly. Forecast experiments also show that the new scheme significantly improves the model forecast skills through providing more dynamically consistent initial conditions [ABSTRACT FROM AUTHOR]

Imperfect physical parameterization schemes are an important source of model bias in a coupled model and adversely impact the performance of model simulation. With a coupled ocean-atmosphere-land model of intermediate complexity, the impact of imperfect parameter estimation on model simulation with biased physics has been studied. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation and 'truth' models. To mitigate model bias, the parameters employed in the biased longwave radiation scheme are optimized using three different methods: least-squares parameter fitting (LSPF), single-valued parameter estimation and geography-dependent parameter optimization (GPO), the last two of which belong to the coupled model parameter estimation (CMPE) method. While the traditional LSPF method is able to improve the performance of coupled model simulations, the optimized parameter values from the CMPE, which uses the coupled model dynamics to project observational information onto the parameters, further reduce the bias of the simulated climate arising from biased physics. Further, parameters estimated by the GPO method can properly capture the climate-scale signal to improve the simulation of climate variability. These results suggest that the physical parameter estimation via the CMPE scheme is an effective approach to restrain the model climate drift during decadal climate predictions using coupled general circulation models. [ABSTRACT FROM AUTHOR]

A modified Gauss-Markov model with weighted constraints was constructed by combining satellite altimeter and tide gauge records. Vertical motion rates of nine tide gauge stations around the Bohai Sea and Yellow Sea are estimated. This is the first time systematic estimates have been derived in this region. Downward trends were seen at the six tide gauge stations located at Tanggu, Longkou, Laohutan, Bayuquan, Xiaochangshan, and Yantai; with vertical motion rates of −1.82±0.50, −1.65±0.46, −0.88±0.42, −0.58±0.62, −0.13±0.43, and −0.01±0.43 mm/yr, respectively. Upward trends were seen at the three tide gauge stations located at Qinhuangdao, Huludao and Chengshantou; with vertical motion rates of 1.12±0.46, 0.55±0.49 and 0.26±0.44 mm/yr, respectively. There was significant subsidence in Tanggu and Longkou, and a rising trend in Qinhuangdao. According to our results, the rate of sea level rise calculated from these tide gauge records can be improved using a more accurate measurement of the land elevation accounting for lifting or subsidence. The model derived can be used to estimate vertical motions of tide gauge stations, and can be widely applied to revise the benchmark levels of tide gauges. [ABSTRACT FROM AUTHOR]

Upper ocean heat content is a factor critical to the intensity change of tropical cyclones (TCs). Because of the inhomogeneity of in situ observations in the North Indian Ocean, gridded temperature/salinity ( T/ S) profiles were derived from satellite data for 1993-2012 using a linear regression method. The satellite derived T/ S dataset covered the region of 10°S-32°N, 25°-100°E with daily temporal resolution, 0.25°×0.25° spatial resolution, and 26 vertical layers from the sea surface to a depth of 1 000 m at standard layers. Independent Global Temperature Salinity Profile Project data were used to validate the satellite derived T/ S fields. The analysis confirmed that the satellite derived temperature field represented the characteristics and vertical structure of the temperature field well. The results demonstrated that the vertically averaged root mean square error of the temperature was 0.83 in the upper 1 000 m and the corresponding correlation coefficient was 0.87, which accounted for 76% of the observed variance. After verification of the satellite derived T/ S dataset, the TC heat potential (TCHP) was verified. The results show that the satellite derived values were coherent with observed TCHP data with a correlation coefficient of 0.86 and statistical significance at the 99% confidence level. The intensity change of TC Gonu during a period of rapid intensification was studied using satellite derived TCHP data. A delayed effect of the TCHP was found in relation to the intensity change of Gonu, suggesting a lag feature in the response of the inner core of the TC to the ocean. [ABSTRACT FROM AUTHOR]

The climate modulation on the sea surface height (SSH) in China seas is investigated using a China Ocean Reanalysis (CORA) dataset from 1958-2008. The dataset is constructed by assimilating the temperature/salinity profiles derived from the satellite altimetry data and historical observational temperature/salinity profiles. Based on the Empirical Orthogonal Function (EOF), the CORA sea surface height anomaly (SSHa) is decomposed, and the interannual and decadal variability of the first three leading modes are analyzed. On the interannual timescale, the first principal component (PC1) is significant positively correlated with the El Niño/Southern Oscillation (ENSO). On the decadal timescale, North Pacific Gyre Oscillation (NPGO) has significant negative correlation with PC1 whereas Pacific Decadal Oscillation (PDO) is in phase with PC3. Analysis shows that the decadal variability of SSH is mainly modulated by the wind stress curl variability related to the NPGO and PDO. In addition, the effect of net heat flux associated to the NPGO and PDO on SSH is also investigated, with net heat flux variability in the Luzon strait and tropic Pacific found to influence the decadal variability of SSH. [ABSTRACT FROM AUTHOR]

At the interface between the lower atmosphere and sea surface, sea spray might significantly influence air-sea heat fluxes and subsequently, modulate upper ocean temperature during a typhoon passage. The effects of sea spray were introduced into the parameterization of sea surface roughness in a 1-D turbulent model, to investigate the effects of sea spray on upper ocean temperature in the Kuroshio Extension area, for the cases of two real typhoons from 2006, Yagi and Soulik. Model output was compared with data from the Kuroshio Extension Observatory (KEO), and Reynolds and AMSRE satellite remote sensing sea surface temperatures. The results indicate drag coefficients that include the spray effect are closer to observations than those without, and that sea spray can enhance the heat fluxes (especially latent heat flux) considerably during a typhoon passage. Consequently, the model results with heat fluxes enhanced by sea spray simulate better the cooling process of the SST and upper-layer temperature profiles. Additionally, results from the simulation of the passage of typhoon Soulik (that passed KEO quickly), which included the sea spray effect, were better than for the simulated passage of typhoon Yagi (that crossed KEO slowly). These promising 1-D results could provide insight into the application of sea spray in general circulation models for typhoon studies. [ABSTRACT FROM AUTHOR]

A variational method is used to estimate wave-affected parameters in a two-equation turbulence model with assimilation of temperature data into an ocean boundary layer model. Enhancement of turbulent kinetic energy dissipation due to breaking waves is considered. The Mellor-Yamada level 2.5 turbulence closure scheme (MY2.5) with the two uncertain wave-affected parameters (wave energy factor α and Charnock coefficient β) is selected as the two-equation turbulence model for this study. Two types of experiments are conducted. First, within an identical synthetic experiment framework, the upper-layer temperature 'observations' in summer generated by a 'truth' model are assimilated into a biased simulation model to investigate if ( α, β) can be successfully estimated using the variational method. Second, real temperature profiles from Ocean Weather Station Papa are assimilated into the biased simulation model to obtain the optimal wave-affected parameters. With the optimally estimated parameters, the upper-layer temperature can be well predicted. Furthermore, the horizontal distribution of the wave-affected parameters employed in a high-order turbulence closure scheme can be estimated optimally by using the four-dimensional variational method that assimilates the upper-layer available temperature data into an ocean general circulation model. [ABSTRACT FROM AUTHOR]

Imperfect physical parameterization schemes in a coupled climate model are an important source of model biases that adversely impact climate prediction. However, how observational information should be used to optimize physical parameterizations through parameter estimation has not been fully studied. Using an intermediate coupled ocean-atmosphere model, the authors investigate parameter optimization when the assimilation model contains biased physics within a biased assimilation experiment framework. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation model and the 'truth' model that is used to generate simulated observations. While the stochastic physics, implemented by initially perturbing the physical parameters, can significantly enhance the ensemble spread and improve the representation of the model ensemble, the parameter estimation is able to mitigate the model biases induced by the biased physics. Furthermore, better results for climate estimation and prediction can be obtained when only the most influential physical parameters are optimized and allowed to vary geographically. In addition, the parameter optimization with the biased model physics improves the performance of the climate estimation and prediction in the deep ocean significantly, even if there is no direct observational constraint on the low-frequency component of the state variables. These results provide some insight into decadal predictions in a coupled ocean-atmosphere general circulation model that includes imperfect physical schemes that are initialized from the climate observing system. [ABSTRACT FROM AUTHOR]

Abstract: The primary objective of this paper is to investigate the seismic behaviour of concrete-filled steel tubular (CFST) arches using incremental dynamic analysis (IDA). A nonlinear elastic–plastic finite element model is developed using OpenSees software and is verified with a shaking table test. Single-record IDA studies indicate that a CFST arch undergoes global dynamic instability when subjected to ground motions of increasing intensity levels. During this process, either dynamic elastic buckling or dynamic elastic–plastic buckling may occur. Dynamic strength, which is defined as the capacity for preventing global dynamic instabilities of CFST arches, is determined with a series of multi-record IDA calculations. A lower bound equation that takes into account the effect of slenderness ratio, axial compression ratio, and included angle is proposed for the prediction of the dynamic strength of CFST arches. [Copyright &y& Elsevier]

The first version of a global ocean reanalysis over multiple decades (1979-2008) has been completed by the National Marine Data and Information Service within the China Ocean Reanalysis (CORA) project. The global ocean model employed is based upon the ocean general circulation model of the Massachusetts Institute of Technology. A sequential data assimilation scheme within the framework of 3D variational (3DVar) analysis, called multi-grid 3DVar, is implemented in 3D space for retrieving multiple-scale observational information. Assimilated oceanic observations include sea level anomalies (SLAs) from multi-altimeters, sea surface temperatures (SSTs) from remote sensing satellites, and in-situ temperature/salinity profiles. Evaluation showed that compared to the model simulation, the annual mean heat content of the global reanalysis is significantly approaching that of World Ocean Atlas 2009 (WOA09) data. The quality of the global temperature climatology was found to be comparable with the product of Simple Ocean Data Assimilation (SODA), and the major ENSO events were reconstructed. The global and Atlantic meridional overturning circulations showed some similarity as SODA, although significant differences were found to exist. The analysis of temperature and salinity in the current version has relatively larger errors at high latitudes and improvements are ongoing in an updated version. CORA was found to provide a simulation of the subsurface current in the equatorial Pacific with a correlation coefficient beyond about 0.6 compared with the Tropical Atmosphere Ocean (TAO) mooring data. The mean difference of SLAs between altimetry data and CORA was less than 0.1 m in most years. [ABSTRACT FROM AUTHOR]

A salinity sensor drift in the Array for Real-time Geostrophic Oceanography (Argo) float has been identified. In the present study the salinity calibration results for global Argo floats indicate that an upper 900dbar averaged-positive salinity drift appears during the floats' operating period from January 2001 to December 2010. These offsets are shown to cause an uncertainty in the estimate of the steric height anomaly of up to 17±5%. Based on a multi-grid, three-dimensional, variational method we construct monthly gridded salinity fields using post-calibration Argo salinity profiles for the period 2003–2010. Monthly time series of halosteric height (HSH) for both the global and regional oceans are calculated using gridded salinity fields over the entire 8years. It is shown that there is a global HSH pattern with a 0.05±0.03mm/year trend of increasing HSH anomaly relative to the World Ocean Atlas (WOA) 2001 in the upper 900dbar. Most regions of the North Atlantic Ocean show predominately inter-annual and decadal fluctuations in the HSH anomaly over the observational period. These fluctuations are associated with the Atlantic Oscillation (AO) and/or the Atlantic Multi-decadal Oscillation (AMO). For the Pacific Ocean, inter-annual variability of the HSH anomaly in the Equatorial Pacific Ocean is modulated mainly by the El Niño/Southern Oscillation (ENSO) phenomenon while in the subtropical Northern Pacific Ocean, the wet, cold phase of the Pacific Decadal Oscillation (PDO) modulates the variability of the HSH anomaly, contributing to a positive anomaly observed over the 8years. For the Indian Ocean, both ENSO and the Indian Ocean Dipole (IOD) influence the inter-annual variability of the HSH anomaly in the Equatorial Indian Ocean. In addition, HSH trends of sub-basin regions are also investigated. We find the most pronounced positive trend occurred in the tropical and subtropical regions of the Southern Indian Ocean with HSH increasing by 0.55±0.08mm/year, while the most remarkable negative trend occurred in the tropical and subtropical regions of the North Atlantic with HSH decreasing by −0.44±0.04mm/year. [ABSTRACT FROM AUTHOR]

Based on the statistics of all surface drifting buoys of 1978-2011 and Lagrangian tracers simulated from high quality ocean reanalysis currents, the impact times and strength of Fukushima nuclear pollution to the east coast of China and the west coast of America have been estimated. Under the circumstances of the radioactive pollutants drifting in the ocean surface, preliminary research results show that while the tracers took about 4 years to reach the west coast of USA, there are two types of tracers to carry out Fukushima nuclear pollutants to reach the east coast of China, corresponding to 1.5-year recirculation gyre transport and 3.5-year subtropical circulation transport. The distributions of the impact strength at these time scales are given according to the variation of relative number concentration with time combined with the decaying rate of radioactive matter. For example, starting from 1% at 1.5-year, of the initial level at the originating area of Fukushima nuclear pollution, the impact strength of Cesium-137 in the South China Sea continuously increases up to 3% by 4 years, while the impact strength of Cesium-137 in the west coast of America is as high as 4% due to the role of strong Kuroshio-extension currents as a major transport mechanism of nuclear pollutants for that area. [ABSTRACT FROM AUTHOR]

A new regional ocean reanalysis over multiple decades (1958-2008) for the coastal waters of China and adjacent seas has been completed by the National Marine Data and Information Service (NMDIS) under the CORA (China Ocean ReAnalysis) project. Evaluations were performed on three aspects: (1) the improvement of general reanalysis quality; (2) eddy structures; and (3) decadal variability of sea surface height anomalies (SSHAs). Results showed that the quality of the new reanalysis has been enhanced beyond ∼40% (39% for temperature, 44% for salinity) in terms of the reduction of root mean squared errors (RMSEs) for which the reanalysis values were compared to observed values in the observational space. Compared to the trial version released to public in 2009, the new reanalysis is able to reproduce more detailed eddy structures as seen in satellite and in situ observations. EOF analysis of the reanalysis SSHAs showed that the new reanalysis reconstructs the leading modes of SSHAs much better than the old version. These evaluations suggest that the new CORA regional reanalysis represents a much more useful dataset for the community of the coastal waters of China and adjacent seas. [ABSTRACT FROM AUTHOR]

Currents and throughflow transport in the Taiwan Strait (TS) are systematically analyzed using a new regional ocean reanalysis dataset (China Ocean ReAnalysis, CORA in brief), recently released by National Marine Data and Information Service (NMDIS) in 2009, covering China's coastal waters and adjacent seas. The total throughflow volume transport through TS presents a seasonal variability with the annual-mean of 1.49Sv. It reaches maximum in summer with mean value of 2.33Sv, while in winter the mean transport falls to 0.78Sv on average. The estimated throughflow transport is in good agreement with the observational results. The comparisons of the results with historical observations and numerical simulations also show that CORA performs well on reproducing the currents and temperature field in TS region. In addition, CORA not only successfully reproduced the known oceanic phenomena in TS such as temperature front, up-wind flow in fall, winter northward flow blocking in Penghu Channel, and the winter anticyclonic eddy located at north of Changyun Rise (CYR), but it also found a new cyclonic eddy in north TS in winter. [ABSTRACT FROM AUTHOR]

regional ocean reanalysis system for the coastal waters of China and adjacent seas has been developed by the National Marine Data and Information Service (NMDIS). It produces a dataset package called CORA (China ocean reanalysis). The regional ocean model used is based on the Princeton Ocean Model with a generalized coordinate system (POMgcs). The model is parallelized by NMDIS with the addition of the wave breaking and tidal mixing processes into model parameterizations. Data assimilation is a sequential three-dimensional variational (3D-Var) scheme implemented within a multigrid framework. Observations include satellite remote sensing sea surface temperature (SST), altimetry sea level anomaly (SLA), and temperature/salinity profiles. The reanalysis fields of sea surface height, temperature, salinity, and currents begin with January 1986 and are currently updated every year. Error statistics and error distributions of temperature, salinity and currents are presented as a primary evaluation of the reanalysis fields using sea level data from tidal gauges, temperature profiles, as well as the trajectories of Argo floats. Some case studies offer the opportunity to verify the evolution of certain local circulations. These evaluations show that the reanalysis data produced provide a good representation of the ocean processes and phenomena in the coastal waters of China and adjacent seas. [ABSTRACT FROM AUTHOR]