Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.

Experimental study of a neutralizer-free gridded ion thruster using radio-frequency self-bias effect

An experimental study on the quasi-neutral beam extracted by a neutralizer-free gridded ion thruster prototype was presented.The prototype was designed using an inductively coupled plasma source terminated by a double-grid accelerator.The beam characteristics were compared when the accelerator was radio-frequency(RF)biased and direct-current(DC)biased.An RF power supply was applied to the screen grid via a blocking capacitor for the RF acceleration,and a DC power supply was directly connected to the screen grid for the DC acceleration.Argon was used as the propellant gas.Furthermore,the characteristics of the plasma beam,such as the floating potential,the spatial distribution of ion flux,and the ion energy distribution function(IEDF)were measured by a four-grid retarding field energy analyzer.The floating potential results showed that the beam space charge is compensated in the case of RF acceleration without a neutralizer,which is similar to the case of classical DC acceleration with a neutralizer.The ion flux of RF acceleration is 1.17 times higher than that of DC acceleration under the same DC component voltage between the double-grid.Moreover,there are significant differences in the beam IEDFs for RF and DC acceleration.The IEDF of RF acceleration has a widened and multipeaked profile,and the main peak moves toward the high-energy region with increasing the DC self-bias voltage.In addition,by comparing the IEDFs with RF acceleration frequencies of3.9 and 7.8 MHz,it is found that the IEDF has a more centered main peak and a narrower energy spread at a higher frequency.

Development and simulation of a gridded thermionic cathode electron gun for a high-energy photon source

A gridded thermionic cathode electron gun was developed for the linear accelerator of the High Energy Photon Source(HEPS).An electron gun should provide a large maximum bunch charge with a wide adjustable range.To satisfy these requirements,the shape of the electrode was optimized using a multi-objective genetic algorithm.A large bunch charge with an adjustable range was achieved using the grid-limited gun,the flow of which was analyzed using 3-D simulations.The electron gun has been manufactured and tested,and the measured data of the grid-limited current and simulation results are compared and discussed in this study.

Simulation method for measurement of the cross-section of the^(14)N(n,a)^(11)B reaction using a gridded ionization chamber

A simulation method for measurement of the cross-section of the^(14)N(n,a)^(11)B reaction with gas and solid samples using a gridded ionization chamber(GIC)has been established.Using the simulation,the experimental spectra of both^(14)N(n,a)^(11)B events and background from other reactions can be predicted,and the experimental scheme can be optimized.According to the simulation results,the optimal experimental parameters,including the pressure of the working gas and the compositions of the working gas and the sample,can be determined.In addition,the simulation results can be used to determine the valid event area and calculate the detection efficiency for valid events.A measurement of the cross-sections of the^(14)N(n,a)^(11)B reaction at E_(n)=4.25,4.50,4.75,5.00,5.25,and 5.50 MeV,based on the 4.5-MV Van de Graff accelerator at Peking University(PKU)using a GIC as the detector for the outgoing a particles,has been performed.The good agreement of the spectra from the simulation and experiment demonstrated the universality of this simulation method,which can be used to accurately measure neutroninduced light-charged particle emission reactions.

Establishment and analysis of global gridded Tm-Ts relationship model

In ground-based GPS meteorology, Tm is a key parameter to calculate the conversion factor that can convert the zenith wet delay（ZWD） to precipitable water vapor（PWV）. It is generally acknowledged that Tm is in an approximate linear relationship with surface temperature Ts, and the relationship presents regional variation. This paper employed sliding average method to calculate correlation coefficients and linear regression coefficients between Tm and Ts at every 2°× 2.5° grid point using Ts data from European Centre for Medium-Range Weather Forecasts（ECMWF） and Tm data from ＂GGOS Atmosphere＂, yielding the grid and bilinear interpolation-based Tm Grid model. Tested by Tm and Ts grid data, Constellation Observation System of Meteorology, Ionosphere, and Climate（COSMIC） data and radiosonde data, the Tm Grid model shows a higher accuracy relative to the Bevis Tm-Ts relationship which is widely used nowadays. The Tm Grid model will be of certain practical value in high-precision PWV calculation.

An evaluation of sea surface height assimilation using along-track and gridded products based on the Regional Ocean Modeling System(ROMS) and the four-dimensional variational data assimilation

Remote sensing products are significant in the data assimilation of an ocean model. Considering the resolution and space coverage of different remote sensing data, two types of sea surface height（SSH） product are employed in the assimilation, including the gridded products from AVISO and the original along-track observations used in the generation. To explore their impact on the assimilation results, an experiment focus on the South China Sea（SCS） is conducted based on the Regional Ocean Modeling System（ROMS） and the four-dimensional variational data assimilation（4 DVAR） technology. The comparison with EN4 data set and Argo profile indicates that, the along-track SSH assimilation result presents to be more accurate than the gridded SSH assimilation, because some noises may have been introduced in the merging process. Moreover, the mesoscale eddy detection capability of the assimilation results is analyzed by a vector geometry–based algorithm. It is verified that, the assimilation of the gridded SSH shows superiority in describing the eddy＇s characteristics, since the complete structure of the ocean surface has been reconstructed by the original data merging.

Testing gridded NWS 1-day observed precipitation analysis in a daily irrigation scheduler

One of the inputs required by daily decision support tools for scheduling irrigation is the amount of water supplied by rainfall. In-field measurements of daily precipitation are expensive or laborious, while measurements from gauges within a few kilometers are frequently not representative due to the high spatiotemporal variability of precipitation. Online radarbased precipitation analyses from NOAA’s National Weather Service (NWS) have obvious potential to provide the needed data, but are known to have varying degrees of accuracy with location and conditions. The NWS precipitation analysis is computed on a 4 km × 4 km grid, so differences should also be expected between the product and individual gauge measurements under each grid cell. In order to test the utility of the NWS precipitation analysis in a daily irrigation scheduler, daily data were gathered in July 2012 from 18 weather stations under 2 NWS precapitation analysis grid cells across instru-mented research and production fields in the Mississippi Delta. Differences between individual station measurements and the NWS precipitation analysis are examined, and root-zone daily soil water deficits computed using both station data and the NWS precipitation analysis. Sub-grid spatial variability between gauge locations, and differences in precipitation between gauges and the gridded NWS analysis, are found to be similar to those reported elsewhere. Differences between time series of soil water deficit computed using the two different precipitation data sources are noted, but prove to be of limited impact on the decision to irrigate or not to irrigate. It is also noted that profile-filling rainfalls limit the impact of accumulating error, resetting the modeled soil water to “full”. Given the Delta-local practice of irrigating to replace full evapotranspirational water used, use of the NWS daily precipitation analysis data as input for a daily irrigation scheduler is judged not only acceptable, but also preferable to other sources of daily precipitation data.

An Independent Assessment of the Monthly PRISM Gridded Precipitation Product in Central Oklahoma

Accurate, long-term records of precipitation are required for the development of climate-informed decision support tools for agriculture. But rain gauges are too sparsely located to meet this need, and radar-derived precipitation measurements are too recent in duration. Using all readily available station records, spatiotemporally continuous estimates of precipitation were created by the PRISM Climate Group to address this problem. As with all interpolated data, the validity of the gridded PRISM product requires validation, and given the extreme spatiotemporal variability of precipitation, such validation is essential. Previous work comparing the monthly precipitation product against contributing rain gauge data revealed inconsistencies that prompted the analysis reported herein. As a basis for checking the accuracy of the PRISM product, independent precipitation data gathered at a USDA research laboratory in central Oklahoma were quality controlled, including comparison to a co-located automated rain gauge operated by the Oklahoma Mesonet. Results indicate that the independent USDA gauge data are of sufficient quality to use in the evaluation of the PRISM product. The area average of the independent USDA data over a matching size area was then used to validate colocated gridded PRISM estimates. The validation results indicate that the monthly gridded PRISM precipitation estimates are close to the independent observed data in terms of means (smaller by 3% to 4.5%) and cumulative probability distributions (within ~4%), but with variances too small by 7% to 11%. From the point of view of agricultural decision support, these results indicate that PRISM estimates might be useful for probabilistic applications, such as downscaling climate forecasts or driving weather generators, assuming appropriate corrections to the higher-order statistics were applied. However, the number of months with potentially significant differences precludes the use of PRISM estimates for any retrospective month-by-month analyses of possible interactions between climate, crop management, and productivity.

Evaluation of CHIRPS Satellite Gridded Dataset as an Alternative Rainfall Estimate for Localized Modelling over Uganda

The Ugandan economy is largely dependent on rural-based and rain-fed agriculture. This creates a critical need to understand the rainfall dynamics at the local scale. However, the country has a sternly sparse and unreliable rain gauge network. This research, therefore, sets out to evaluate the use of the CHIRPS satellite gridded dataset as an alternative rainfall estimate for local modelling of rainfall in Uganda. Complete, continuous and reliable in situ station observations for the period between 2012 and 2020 were used for the comparison with CHIRPS satellite data models in the same epoch. Rainfall values within the minimum 5 km and maximum 20 km radii from the in situ stations were extracted at a 5 km interval from the interpolated in situ station surface and the CHIRPS satellite data model for comparison. Results of the 5 km radius were adopted for the evaluation as it’s closer to the optimal rain gauge coverage of 25 km2. They show the R2 = 0.91, NSE = 0.88, PBias = -0.24 and RSR = 0.35. This attests that the CHIRPS satellite gridded datasets provide a good approximation and simulation of in situ station data with high collinearity and minimum deviation. This tallies with related studies in other regions that have found CHIRPS datasets superior to interpolation surfaces and sparse rain gauge data in the comprehensive estimation of rainfall. With a 0.05° * 0.05° (Latitude, longitude) spatial resolution, CHIRPS satellite gridded rainfall estimates are therefore able to provide a comprehensive rainfall estimation at a local scale. Essentially these results reward research science in regions like Uganda that have sparse rain gauges networks characterized by incomplete, inconsistent and unreliable data with an empirically researched alternative source of rainfall estimation data. It further provides a platform to scientifically interrogate the rainfall dynamics at a local scale in order to infuse local policy with evidence-based formulation and application.

Variational Gridded Graph Convolution Network for Node Classification

The existing graph convolution methods usually suffer high computational burdens,large memory requirements,and intractable batch-processing.In this paper,we propose a high-efficient variational gridded graph convolution network(VG-GCN)to encode non-regular graph data,which overcomes all these aforementioned problems.To capture graph topology structures efficiently,in the proposed framework,we propose a hierarchically-coarsened random walk(hcr-walk)by taking advantage of the classic random walk and node/edge encapsulation.The hcr-walk greatly mitigates the problem of exponentially explosive sampling times which occur in the classic version,while preserving graph structures well.To efficiently encode local hcr-walk around one reference node,we project hcrwalk into an ordered space to form image-like grid data,which favors those conventional convolution networks.Instead of the direct 2-D convolution filtering,a variational convolution block(VCB)is designed to model the distribution of the randomsampling hcr-walk inspired by the well-formulated variational inference.We experimentally validate the efficiency and effectiveness of our proposed VG-GCN,which has high computation speed,and the comparable or even better performance when compared with baseline GCNs.

Hybrid global gridded snow products and conceptual simulations of distributed snow budget:evaluation of different scenarios in a mountainous watershed

Considering snowmelt in mountainous areas as the important source of streamflow,the snow accumulation/melting processes are vital for accurate simulation of the hydrological regimes.The lack of snow-related data and its uncertainties/conceptual ambiguity in snowpack modeling are the different challenges of developing hydroclimatological models.To tackle these challenges,Global Gridded Snow Products(GGSPs)are introduced,which effectively simplify the identification of the spatial characteristics of snow hydrological variables.This research aims to investigate the performance of multisource GGSPs using multi-stage calibration strategies in hydrological modeling.The used GGSPs were Snow-Covered Area(SCA)and Snow Water Equivalent(SWE),implemented individually or jointly to calibrate an appropriate water balance model.The study area was a mountainous watershed located in Western Iran with a considerable contribution of snowmelt to the generated streamflow.The results showed that using GGSPs as complementary information in the calibration process,besides streamflow time series,could improve the modeling accuracy compared to the conventional calibration,which is only based on streamflow data.The SCA with NSE,KGE,and RMSE values varying within the ranges of 0.47–0.57,0.54–0.65,and 4–6.88,respectively,outperformed the SWE with the corresponding metrics of 0.36–0.59,0.47–0.60,and 5.22–7.46,respectively,in simulating the total streamflow of the watershed.In addition to the superiority of the SCA over SWE,the twostage calibration strategy reduced the number of optimized parameters in each stage and the dependency of internal processes on the streamflow and improved the accuracy of the results compared with the conventional calibration strategy.On the other hand,the consistent contribution of snowmelt to the total generated streamflow(ranging from 0.9 to 1.47)and the ratio of snow melting to snowfall(ranging from 0.925 to 1.041)in different calibration strategies and models resulted in a reliable simulation of the model.

Calibration of Gridded Wind Speed Forecasts Based on Deep Learning

The challenges of applying deep learning(DL) to correct deterministic numerical weather prediction(NWP) biases with non-Gaussian distributions are discussed in this paper.It is known that the DL UNet model is incapable of correcting the bias of strong winds with the traditional loss functions such as the MSE(mean square error),MAE(mean absolute error),and WMAE(weighted mean absolute error).To solve this,a new loss function embedded with a physical constraint called MAE_MR(miss ratio) is proposed.The performance of the UNet model with MAE_MR is compared to UNet traditional loss functions,and statistical post-processing methods like Kalman filter(KF) and the machine learning methods like random forest(RF) in correcting wind speed biases in gridded forecasts from the ECMWF high-resolution model(HRES) in East China for lead times of 1–7 days.In addition to MAE for full wind speed,wind force scales based on the Beaufort scale are derived and evaluated.Compared to raw HRES winds,the MAE of winds corrected by UNet(MAE_MR) improves by 22.8% on average at 24–168 h,while UNet(MAE),UNet(WMAE),UNet(MSE),RF,and KF improve by 18.9%,18.9%,17.9%,13.8%,and 4.3%,respectively.UNet with MSE,MAE,and WMAE shows good correction for wind forces 1–3 and 4,but negative correction for 6 or higher.UNet(MAE_MR) overcomes this,improving accuracy for forces 1–3,4,5,and 6 or higher by 11.7%,16.9%,11.6%,and 6.4% over HRES.A case study of a strong wind event further shows UNet(MAE_MR) outperforms traditional post-processing in correcting strong wind biases.

基于卷积神经网络的燃气表信息自动识别方法研究

提出了一种基于改进的LeNet-5卷积神经网络的识别方法。通过加装摄像头和通信线路的方式,实时采集图像信息,并对图像进行部分预处理。引入Gabor滤波器、ReLU-Softplus函数、SVM分类器等优化传统LeNet-5模型,并根据图像数据的不均衡性,运用Grid Loss函数优化CNN网络,由此,实现燃气表自动化识别方法的构建。在Caffe深度学习框架下进行实验测评,结果表明该方法整体的识别准确性高达99.60%、整个样本集及单幅字码的训练总时间均优于其他识别方法,且对于不完整表码字的识别准确率也达到了99.21%。

Sculpturing solid polymer spheres into internal gridded hollow carbon spheres under controlled pyrolysis micro-environment

Porous carbon spheres with an internal gridded hollow structure and microporous shell have always been attractive as carbon hosts for electrochemical energy storage. Such carbon hosts can limit active species loss and enhance electronic conductivity throughout the entire framework. Herein, a synthesis approach of internal gridded hollow carbon spheres is developed from solid polymer spheres rather than originally gridded polymer spheres under a controlled pyrolysis micro-environment. The crucial point of this approach is the fabrication of a silica fence around solid polymer spheres, under which the free escaping of the pyrolysis gas will be partly impeded, thus offering a reconstitution opportunity for an internal structure of solid polymer spheres. As a result, the interior of carbon spheres is sculptured into a gridded hollow structure with microporous skin. Furthermore, the size and density of carbon-bridge grids can be modulated by altering the crosslinking degree of polymer spheres and varying pyrolysis conditions. Such gridded hollow carbon spheres show good performance as sulfur hosts for Li-S battery.

Application of a Novel Ant Algorithm Termed Continuous Gridded in Aidded Drug Design

Aimed at solving continuous optimum parameter problems effectively in added drug design, this paper develops a novel ant algorithm termed continuous gridded ant colony （CGAC）, where the spy ants are utilized to search the latent optimum grid in the domain completely and effectively. In order to test the effect, the CGAC algorithm was success in finding the best values of C and y, when the support vector machine （SVM） was used to fit the nonlinear relationship between the numerical representation of the chemical structure and IC50. The genetic algorithm （GA） was also used to obtain the appropriate feature subset simultaneously, because feature subset selection influences the appropriate kernel parameters and vice versa. The obtained results illustrate that GA-CGAC-SVM models have satisfactory prediction accuracy. The best quantitative modeling results in thirteen-descriptors model based on GA-CGAC-SVMr with mean-square errors 0.397, a predicted correlation coefficient （R2） 0.842, and a cross-validated correlation coefficient （Q^2） 0.756. The best classification result was found using SVM： the percentage （%） of correct prediction based on 7-fold cross-validation was 90.6%. The results demonstrate that the proposed CGAC algorithm provides a new and effective method to find the optimum parameters when the SVM tool is used.

The study of the transmission of GHz waves in the oceanic low altitude waveguide in the PEM-based gridded method

A parabolic equation method （PEM）-based discrete algorithm is proposed and is used to obtain the field distribution in the evaporation duct space. This method not only improves the computing speed, but also provides the flexibility to adjust the simulation accuracy. Numerical simulation of the wave propagation in the oceanic waveguide structure is done. In addition, the initial field distribution and progressive steps are determined. The loss model in the waveguide is solved through the numerical solution. By comparing the characteristics of the radio wave propagation in the duct and in the normal atmospheric structure, we analyses the radio transmission over the horizon detection in the oceanic waveguide.

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

基于STAR-CCM+的船体周围流体流动数值分析及验证结果

In this paper,numerical analyses of fluid flow around the ship hulls such as Series 60,the Kriso Container Ship(KCS),and catamaran advancing in calm water,are presented.A commercial computational fluid dynamic(CFD)code,STAR-CCM+is used to analyze total resistance,sinkage,trim,wave profile,and wave pattern for a range of Froude numbers.The governing RANS equations of fluid flow are discretized using the finite volume method(FVM),and the pressure-velocity coupling equations are solved using the SIMPLE(semi-implicit method for pressure linked equations)algorithm.Volume of fluid(VOF)method is employed to capture the interface between air and water phases.A fine discretization is performed in between these two phases to get a higher mesh resolution.The fluid-structure interaction(FSI)is modeled with the dynamic fluid-body interaction(DFBI)module within the STAR-CCM+.The numerical results are verified using the results available in the literatures.Grid convergence studies are also carried out to determine the dependence of results on the grid quality.In comparison to previous findings,the current CFD analysis shows the satisfactory results.

Global dust density in two-dimensional complex plasma

The driven-dissipative Langevin dynamics simulation is used to produce a two-dimensional(2D) dense cloud, which is composed of charged dust particles trapped in a quadratic potential. A 2D mesh grid is built to analyze the center-to-wall dust density. It is found that the local dust density in the outer region relative to that of the inner region is more nonuniform,being consistent with the feature of quadratic potential. The dependences of the global dust density on equilibrium temperature, particle size, confinement strength, and confinement shape are investigated. It is found that the particle size, the confinement strength, and the confinement shape strongly affect the global dust density, while the equilibrium temperature plays a minor effect on it. In the direction where there is a stronger confinement, the dust density gradient is bigger.

An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.