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.

Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

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.

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.

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.

Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces

The spatial resolution of source data, the impact factor selection on the grid model and the size of the grid might be the main limitations of global land datasets applied on a regional scale. Quantitative studies of the impacts of rasterization on data accuracy can help improve data resolution and regional data accuracy. Through a case study of cropland data for Jiangsu and Anhui provinces in China, this research compared data accuracy with different data sources, rasterization methods, and grid sizes. First, we investigated the influence of different data sources on gridded data accuracy. The temporal trends of the History Database of the Global Environment （HYDE）, Chinese Historical Cropland Data （CHCD）, and Suwan Cropland Data （SWCD） datasets were more similar. However, differ- ent spatial resolutions of cropland source data in the CHCD and SWCD datasets revealed an average difference of 16.61% when provin- cial and county data were downscaled to a 10 x 10 km2 grid for comparison. Second, the influence of selection of the potential arable land reclamation rate and temperature factors, as well as the different processing methods for water factors, on accuracy of gridded datasets was investigated. Applying the reclamation rate of potential cropland to grid-processing increased the diversity of spatial distri- bution but resulted in only a slightly greater standard deviation, which increased by 4.05. Temperature factors only produced relative disparities within 10% and absolute disparities within 2 km2 over more than 90% of grid cells. For the different processing methods for water factors, the HYDE dataset distributed 70% more cropland in grid cells along riverbanks, at the abandoned Yellow River Estuary （located in Binhai County, Yancheng City, Jiangsu Province）, and around Hongze Lake, than did the SWCD dataset. Finally, we ex- plored the influence of different grid sizes. Absolute accuracy disparities by unit area for the year 2000 were within 0.1 km2 at a 1 km2 grid size, a 25% improvement over the 10 km2 grid size. Compared to the outcomes of other similar studies, this demonstrates that some model hypotheses and grid-processing methods in international land datasets are truly incongruent with actual land reclamation proc- esses, at least in China. Combining the model-based methods with historical empirical data may be a better way to improve the accuracy of regional scale datasets. Exploring methods for the above aspects improved the accuracy of historical crop/and gridded datasets for finer regional scales.

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.

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.

Estimation of areal precipitation in the Qilian Mountains based on a gridded dataset since 1961

Based on a 0.5°×0.5° daily gridded precipitation dataset and observations in mete- orological stations released by the National Meteorological Information Center, the interan- nual variation of areal precipitation in the Qilian Mountains during 1961-2012 is investigated using principal component analysis （PCA） and regression analysis, and the relationship be- tween areal precipitation and drought accumulation intensity is also analyzed. The results indicate that the spatial distribution of precipitation in the Qilian Mountains can be well re- flected by the gridded dataset. The gridded data-based precipitation in mountainous region is generally larger than that in plain region, and the eastern section of the mountain range usu- ally has more precipitation than the western section. The annual mean areal precipitation in the Qilian Mountains is 724.9×108 m3, and the seasonal means in spring, summer, autumn and winter are 118.9×108 m3, 469.4×108 m3, 122.5×108 m3 and 14.1×108 m3, respectively. Summer is a season with the largest areal precipitation among the four seasons, and the proportion in summer is approximately 64.76%. The areal precipitation in summer, autumn and winter shows increasing trends, but a decreasing trend is seen in spring. Among the four seasons, summer have the largest trend magnitude of 1.7×108 m3-a-1. The correlation be- tween areal precipitation in the mountainous region and dry-wet conditions in the mountains and the surroundings can be well exhibited. There is a negative correlation between drought accumulation intensity and the larger areal precipitation is consistent with the weaker drought intensity for this region.

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.

Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique

Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

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.

A Ceph-based storage strategy for big gridded remote sensing data

When using distributed storage systems to store gridded remote sensing data in large,distributed clusters,most solutions utilize big table index storage strategies.However,in practice,the performance of big table index storage strategies degrades as scenarios become more complex,and the reasons for this phenomenon are analyzed in this paper.To improve the read and write performance of distributed gridded data storage,this paper proposes a storage strategy based on Ceph software.The strategy encapsulates remote sensing images in the form of objects through a metadata management strategy to achieve the spatiotemporal retrieval of gridded data,finding the cluster location of gridded data through hash-like calculations.The method can effectively achieve spatial operation support in the clustered database and at the same time enable fast random read and write of the gridded data.Random write and spatial query experiments proved the feasibility,effectiveness,and stability of this strategy.The experiments prove that the method has higher stability than,and that the average query time is 38%lower than that for,the large table index storage strategy,which greatly improves the storage and query efficiency of gridded images.