Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.

Adaptive semi-empirical model for non-contact atomic force microscopy

Non-contact atomic force microscope is a powerful tool to investigate the surface topography with atomic resolution.Here we propose a new approach to estimate the interaction between its tips and samples,which combines a semi-empirical model with density functional theory(DFT)calculations.The generated frequency shift images are consistent with the experiment for mapping organic molecules using CuCO,Cu,CuCl,and CuO_(x)tips.This approach achieves accuracy close to DFT calculation with much lower computational cost.

Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.

Semi-empirical modeling of volumetric efficiency in engines equipped with variable valve timing system

Volumetric efficiency and air charge estimation is one of the most demanding tasks in control of today's internal combustion engines.Specifically,using three-way catalytic converter involves strict control of the air/fuel ratio around the stoichiometric point and hence requires an accurate model for air charge estimation.However,high degrees of complexity and nonlinearity of the gas flow in the internal combustion engine make air charge estimation a challenging task.This is more obvious in engines with variable valve timing systems in which gas flow is more complex and depends on more functional variables.This results in models that are either quite empirical(such as look-up tables),not having interpretability and extrapolation capability,or physically based models which are not appropriate for onboard applications.Solving these problems,a novel semi-empirical model was proposed in this work which only needed engine speed,load,and valves timings for volumetric efficiency prediction.The accuracy and generalizability of the model is shown by its test on numerical and experimental data from three distinct engines.Normalized test errors are 0.0316,0.0152 and 0.24 for the three engines,respectively.Also the performance and complexity of the model were compared with neural networks as typical black box models.While the complexity of the model is less than half of the complexity of neural networks,and its computational cost is approximately 0.12 of that of neural networks and its prediction capability in the considered case studies is usually more.These results show the superiority of the proposed model over conventional black box models such as neural networks in terms of accuracy,generalizability and computational cost.

Kinetics of zinc sulfide concentrate direct leaching in pilot plant scale and development of semi-empirical model

The direct leaching kinetics of an iron-poor zinc sulfide concentrate in the tubular reactor was examined.All tests werecarried out in the pilot plant.To allow the execution of hydrostatic pressure condition,the slurry with ferrous sulfate and sulfuric acidsolution was filled into a vertical tube(9m in height)and air was blown from the bottom of the reactor.The effects of initial acidconcentration,temperature,particle size,initial zinc sulfate concentration,pulp density and the concentration of Fe on the leachingkinetics were investigated.Results of the kinetic analysis indicate that direct leaching of zinc sulfide concentrate follows shrinkingcore model(SCM).This process was controlled by a chemical reaction with the apparent activation energy of49.7kJ/mol.Furthermore,a semi-empirical equation is obtained,showing that the order of the iron,sulfuric acid and zinc sulfate concentrationsand particle radius are0.982,0.189,-0.097and-0.992,respectively.Analysis of the unreacted and reacted sulfide particles bySEM-EDS shows that insensitive agitation in the reactor causes detachment of the sulfur layer from the particles surface in lowerthan60%Zn conversion and lixiviant in the face with sphalerite particles.

Salt-Affected Soil Mapping in an Arid Environment Using Semi-Empirical Model and Landsat-OLI Data

The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the OLI data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca2+, Mg2+, Na+, K+, Cl- and SO42-), pH and the electrical conductivity (EC-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC-Lab, which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from OLI data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline. The laboratory chemical analyses corroborate these results. Furthermore, the semi-empirical predictive model provides good global results in comparison to the ground truth and laboratory analysis (EC-Lab), with correlation coefficient (R2) of 0.97, an index of agreement (D) of 0.84 (p < 0.05), and low overall root mean square error (RMSE) of 11%. Moreover, we found that topographic attributes have a substantial impact on the spatial distribution of salinity. The areas at a relatively high altitude and with hard bedrock are less susceptible to salinity, while areas at a low altitude and slope (≤2%) composed of Quaternary soil are prone to it. In these low areas, the water table is very close to the surface (≤1 m), and the absence of an adequate drainage network contributes significantly to waterlogging. Consequently, the intrusion and emergence of seawater at the surface, coupled with high temperature and high evaporation rates, contribute extensively to the soil salinity in the study area.

Description of martensitic transformation kinetics in Fe-C-X(X = Ni,Cr,Mn,Si) system by a modified model

Controlling the content of athermal martensite and retained austenite is important to improving the mechanical properties of high-strength steels,but a mechanism for the accurate description of martensitic transformation during the cooling process must be addressed.At present,frequently used semi-empirical kinetics models suffer from huge errors at the beginning of transformation,and most of them fail to exhibit the sigmoidal shape characteristic of transformation curves.To describe the martensitic transformation process accurately,based on the Magee model,we introduced the changes in the nucleation activation energy of martensite with temperature,which led to the varying nucleation rates of this model during martensitic transformation.According to the calculation results,the relative error of the modified model for the martensitic transformation kinetics curves of Fe-C-X(X = Ni,Cr,Mn,Si) alloys reached 9.5% compared with those measured via the thermal expansion method.The relative error was approximately reduced by two-thirds compared with that of the Magee model.The incorporation of nucleation activation energy into the kinetics model contributes to the improvement of its precision.

Structural optimization of acceptor molecules guided by a semi-empirical model for organic solar cells with efficiency over 15%

Despite much progress in organic solar cells(OSCs),higher efficiency is still the most desirable goal and can indeed be obtained through rational design of active layer materials and device optimization according to the theoretical prediction.Herein,under the guidance of a semi-empirical model,two new non-fullerene small molecule acceptors(NFSMAs)with an acceptor-donor-acceptor(A-D-A)architecture,namely,6 T-OFIC and 5 T-OFIC,have been designed and synthesized.6 T-OFIC exhibits wider absorption spectrum and a red-shifted absorption onset(λ_(onset))of 946 nm due to its extended conjugation central unit.In contrast,5 T-OFIC with five-thiophene-fused backbone has an absorption with theλ_(onset)of 927 nm,which is closer to the predicted absorption range for the best single junction cells based on the semiempirical model.Consequently,the device based on 5 T-OFIC yields a higher power conversion efficiency(PCE)of 13.43%compared with 12.35%of the 6 T-OFIC-based device.Furthermore,an impressive PCE of 15.45%was achieved for the5 T-OFIC-based device when using F-2 Cl as the third component.5 T-OFIC offers one of a few acceptor cases with efficiencies over 15%other than Y6 derivatives.

Vapor intrusion in buildings: Development of semi-empirical models including lateral separation between the building and the pollution source

Future constructions in the context of the industrial wastelands reuse may be exposed to Vapor Intrusion(VI).VI can be evaluated by combining in-situ measures and analytical models to evaluate exposure risk in future indoor environments.However,the assumptions in the existing models may reduce their accuracy when they do not meet the characteristics of real situations.Wrong estimations of indoor concentration levels may lead to inappropriate solutions against VI.In this context,new semi-empirical models(SEM)are proposed in order to better specify pollution scenarios and thus increase the accuracy of VI estimations.This development is based on a parametric study(numerical CFD)and a dimensionless analysis combined to existing VI models that consider a continuous source distribution in the soil.These expressions allow to better take into account the source position in the soil(i.e.depth and lateral source/building separation),soil properties(air permeability,diffusion coefficient of the pollutant,…)and building features(building foundation,indoor pressure,air exchange rate,…)in the estimation of indoor concentration levels.The obtained results with the proposed SEM were compared with a numerical CFD model and available experimental data,showing good accuracy in the estimation of VI.Given the advantages of these new models,they can provide better precision in the health risk assessments associated with VI.Furthermore,these expressions can be easily integrated into building ventilation codes allowing to consider air exchange rate and indoor pressure variations over time.

采用STAMP-24Model的多组织事故分析

安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

24Model与LCM原因因素定义对比研究

为探究损失致因模型(LCM)原因因素定义与事故致因“2-4”模型(24Model)存在的异同和优缺点,梳理2个模型各层面原因和结果的定义,对比定义内容及其对事故原因分析等安全实务的指导作用,并以一起瓦斯爆炸事故为例加以实证分析,获得二者分析结果之间的差异。研究结果表明:LCM是首个将管理因素纳入事故致因分析的一维事件序列模型,可明确各层面原因因素的定义和因素间的逻辑关系,但部分定义存在交叉重复的问题,并没有揭示安全工作指导思想等深层次事故致因因素;24Model作为系统性事故致因模型,对各类因素的定义均以组织为主体,描述事件、事故、安全的概念内涵,划分个体安全动作、安全能力和组织安全管理体系的类别并给出含义解析,探究组织安全文化层面的问题并以32个元素体现;2个模型的事故原因分析方法均建立在对各层级原因因素定义的基础上,并适用于模型理论体系本身。

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

Anisotropic time-dependent behaviors of shale under direct shearing and associated empirical creep models

Understanding the anisotropic creep behaviors of shale under direct shearing is a challenging issue.In this context,we conducted shear-creep and steady-creep tests on shale with five bedding orientations (i.e.0°,30°,45°,60°,and 90°),under multiple levels of direct shearing for the first time.The results show that the anisotropic creep of shale exhibits a significant stress-dependent behavior.Under a low shear stress,the creep compliance of shale increases linearly with the logarithm of time at all bedding orientations,and the increase depends on the bedding orientation and creep time.Under high shear stress conditions,the creep compliance of shale is minimal when the bedding orientation is 0°,and the steady-creep rate of shale increases significantly with increasing bedding orientations of 30°,45°,60°,and 90°.The stress-strain values corresponding to the inception of the accelerated creep stage show an increasing and then decreasing trend with the bedding orientation.A semilogarithmic model that could reflect the stress dependence of the steady-creep rate while considering the hardening and damage process is proposed.The model minimizes the deviation of the calculated steady-state creep rate from the observed value and reveals the behavior of the bedding orientation's influence on the steady-creep rate.The applicability of the five classical empirical creep models is quantitatively evaluated.It shows that the logarithmic model can well explain the experimental creep strain and creep rate,and it can accurately predict long-term shear creep deformation.Based on an improved logarithmic model,the variations in creep parameters with shear stress and bedding orientations are discussed.With abovementioned findings,a mathematical method for constructing an anisotropic shear creep model of shale is proposed,which can characterize the nonlinear dependence of the anisotropic shear creep behavior of shale on the bedding orientation.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.

Geostatistical seismic inversion and 3D modelling of metric flow units,porosity and permeability in Brazilian presalt reservoir

Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow.

耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

Toward a Learnable Climate Model in the Artificial Intelligence Era

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.