Integrating remote sensing and 3-PG model to simulate the biomass and carbon stock of Larix olgensis plantation

Accurate estimations of biomass and its temporal dynamics are crucial for monitoring the carbon cycle in forest ecosystems and assessing forest carbon sequestration potentials.Recent studies have shown that integrating process-based models(PBMs)with remote sensing data can enhance simulations from stand to regional scales,significantly improving the ability to simulate forest growth and carbon stock dynamics.However,the utilization of PBMs for large-scale simulation of larch carbon storage distribution is still limited.In this study,we applied the parameterized 3-PG(Physiological Principles Predicting Growth)model across the Mengjiagang Forest Farm(MFF)to make broad-scale predictions of the biomass and carbon stocks of Larix olgensis plantation.The model was used to simulate average diameter at breast height(DBH)and total biomass,which were later validated with a wide range of observation data including sample plot data,forest management inventory data,and airborne laser scanning data.The results showed that the 3-PG model had relatively high accuracy for predicting both DBH and total biomass at stand and regional scale,with determination coefficients ranging from 0.78 to 0.88.Based on the estimation of total biomass,we successfully produced a carbon stock map of the Larix olgensis plantation in MFF with a spatial resolution of 20 m,which helps with relevant management advice.These findings indicate that the integration of 3-PG model and remote sensing data can well predict the biomass and carbon stock at regional and even larger scales.In addition,this integration facilitates the evaluation of forest carbon sequestration capacity and the development of forest management plans.

Species-specific and generalized allometric biomass models for eight Fagaceae species in the understory of evergreen broadleaved forests in subtropical China

Quantifying the biomass of saplings in the regeneration component is critical for understanding biogeochemical processes of forest ecosystems.However,accurate allometric equations have yet to be developed in sufficient detail.To develop species-specific and generalized allometric equations,154 saplings of eight Fagaceae tree species in subtropical China’s evergreen broadleaved forests were collected.Three dendrometric variables,root collar diameter(d),height(h),and crown area(ca)were applied in the model by the weighted nonlinear seemingly unrelated regression method.Using only d as an input variable,the species-specific and generalized allometric equations estimated the aboveground biomass reasonably,with R _(adj)^(2) values generally>0.85.Adding h and/or ca improved the fitting of some biomass components to a certain extent.Generalized equations showed a relatively large coefficient of variation but comparable bias to species-specific equations.Only in the absence of species-specific equations at a given location are generalized equations for mixed species recommended.The developed regression equations can be used to accurately calculate the aboveground biomass of understory Fagaceae regeneration trees in China’s subtropical evergreen broadleaved forests.

Coupled bio-chemo-hydro-mechanical modeling of microbially induced calcite precipitation process considering biomass encapsulation using a micro-scale relationship

Geomaterials with inferior hydraulic and strength characteristics often need improvement to enhance their engineering behaviors.Traditional ground improvement techniques require enormous mechanical effort or synthetic chemicals.Sustainable stabilization technique such as microbially induced calcite precipitation(MICP)utilizes bacterial metabolic processes to precipitate cementitious calcium carbonate.The reactive transport of biochemical species in the soil mass initiates the precipitation of biocement during the MICP process.The precipitated biocement alters the hydro-mechanical performance of the soil mass.Usually,the flow,deformation,and transport phenomena regulate the biocementation technique via coupled bio-chemo-hydro-mechanical(BCHM)processes.Among all,one crucial phenomenon controlling the precipitation mechanism is the encapsulation of biomass by calcium carbonate.Biomass encapsulation can potentially reduce the biochemical reaction rate and decelerate biocementation.Laboratory examination of the encapsulation process demands a thorough analysis of associated coupled effects.Despite this,a numerical model can assist in capturing the coupled processes influencing encapsulation during the MICP treatment.However,most numerical models did not consider biochemical reaction rate kinetics accounting for the influence of bacterial encapsulation.Given this,the current study developed a coupled BCHM model to evaluate the effect of encapsulation on the precipitated calcite content using a micro-scale semiempirical relationship.Firstly,the developed BCHM model was verified and validated using numerical and experimental observations of soil column tests.Later,the encapsulation phenomenon was investigated in the soil columns of variable maximum calcite crystal sizes.The results depict altered reaction rates due to the encapsulation phenomenon and an observable change in the precipitated calcite content for each maximum crystal size.Furthermore,the permeability and deformation of the soil mass were affected by the simultaneous precipitation of calcium carbonate.Overall,the present study comprehended the influence of the encapsulation of bacteria on cement morphology-induced permeability,biocement-induced stresses and displacements.

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。

Are allometric model parameters of aboveground biomass for trees phylogenetically constrained?

Knowledge of which biological and functional traits have,or lack,phylogenetic signal in a particular group of organisms is important to understanding the formation and functioning of biological communities.Allometric biomass models reflecting tree growth characteristics are commonly used to predict forest biomass.However,few studies have examined whether model parameters are constrained by phylogeny.Here,we use a comprehensive database(including 276 tree species) compiled from 894 allometric biomass models published in 302 articles to examine whether parameters a and b of the model W=aD~b(where W stands for aboveground biomass,D is diameter at breast height) exhibit phylogenetic signal for all tree species as a whole and for different groups of tree species.For either model parameter,we relate difference in model parameter between different tree species to phylogenetic distance and to environmental distance between pairwise sites.Our study shows that neither model parameter exhibits phylogenetic signals(Pagel's λ and Blomberg's K both approach zero).This is the case regardless of whether all tree species in our data set were analyzed as a whole or tree species in different taxonomic groups(gymnosperm and angiosperm),leaf duration groups(evergreen and deciduous),or ecological groups(tropical,temperate and boreal) were analyzed separately.Our study also shows that difference in each parameter of the allometric biomass model is not significantly related to phylogenetic and environmental distances between tree species in different sites.

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。

Fixed-Bed Column Adsorption Modeling of MnO4- Ions from Acidic Aqueous Solutions on Activated Carbons Prepared with the Biomass

Activated carbons calcined at 400˚C and 600˚C (AC-400 and AC-600), prepared using palm nuts, collected in the town of Franceville in Gabon, were used to study the dynamic adsorption of MnO4- ions in acidic media on fixed bed column and on the kinetic modeling of experimental data of breakthrough curves of MnO4- ions obtained. Results on the adsorption of MnO4- ions in fixed-bed dynamics obtained on AC-400 and AC-600 adsorbents beds indicated that the AC-400 bed appears to be the most efficient in removing MnO4- ions in acidic media. Indeed, the adsorbed amounts, the adsorbed capacities at saturation and the elimination percentage of MnO4- ions obtained with AC-400 (31.24 mg;52.06 mg·g-1 and 41.65% respectively) were higher compared to those obtained with AC-600 (9.87 mg;16.45 mg·g-1 and 17.79% respectively). The breakthrough curves kinetic modeling revealed that the Thomas model and the pseudo-first-order kinetic model were the most suitable models to describe the adsorption of MnO4- ions on adsorbents studied in our experimental conditions. The results of the intraparticle diffusion model showed that intraparticle diffusion was involved in the adsorption mechanism of MnO4- ions on investigated adsorbents and was not the limiting step and the only process controlling MnO4- ions adsorption. In contrast to AC-400, the intraparticle diffusion on AC-600 bed plays an important role in the adsorption mechanism of MnO4- ions.

Towards carbon neutrality of calcium carbide-based acetylene production with sustainable biomass resources

Acetylene is produced from the reaction between calcium carbide(CaC_(2))and water,while the production of CaC_(2) generates significant amount of carbon dioxide not only because it is an energy-intensive process but also the raw material for CaC_(2) synthesis is from coal.Here,a comprehensive biomass-to-acetylene process was constructed that integrated several units including biomass pyrolysis,oxygen-thermal CaC_(2) fabrication and calcium looping.For comparison,a coal-to-acetylene process was also established by using coal as feedstock.The carbon efficiency,energy efficiency and environmental impacts of the bio-based calcium carbide acetylene(BCCA)and coal-based calcium carbide acetylene(CCCA)processes were systematically analyzed.Moreover,the environmental impacts were further evaluated by applying thermal integration at system level and energy substitution in CaC_(2) furnace.Even though the BCCA process showed lower carbon efficiency and energy efficiency than that of the CCCA process,life cycle assessment demonstrated the BCCA(1.873 kgCO_(2eq) kg-prod^(-1))a lower carbon footprint process which is 0.366 kgCO_(2eq) kg-prod^(-1) lower compared to the CCCA process.With sustainable energy(biomass power)substitution in CaC_(2) furnace,an even lower GWP value of 1.377 kgCO_(2eq) kg-prod^(-1) can be achieved in BCCA process.This work performed a systematic analysis on integrating biomass into industrial acetylene production,and revealed the positive role of biomass as raw material(carbon)and energy supplier.

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).

Biomass-enhanced Janus sponge-like hydrogel with salt resistance and highstrength for efficient solar desalination

Interfacial solar-driven evaporation technology shows great potential in the field of industrial seawater desalination, and the development ofefficient and low-cost evaporation materials is key to achieving large-scale applications. Hydrogels are considered to be promising candidates;however, conventional hydrogel-based interfacial solar evaporators have difficulty in simultaneously meeting multiple requirements, including ahigh evaporation rate, salt resistance, and good mechanical properties. In this study, a Janus sponge-like hydrogel solar evaporator (CPAS) withexcellent comprehensive performance was successfully constructed. The introduction of biomass agar (AG) into the polyvinyl alcohol (PVA)hydrogel backbone reduced the enthalpy of water evaporation, optimized the pore structure, and improved the mechanical properties. Meanwhile, by introducing hydrophobic fumed nano-silica aerogel (SA) and a synergistic foaming-crosslinking process, the hydrogel spontaneouslyformed a Janus structure with a hydrophobic surface and hydrophilic bottom properties. Based on the reduction of the evaporation enthalpy andthe modulation of the pore structure, the CPAS evaporation rate reached 3.56 kg m^(-2) h^(-1) under one sun illumination. Most importantly, owingto the hydrophobic top surface and 3D-interconnected porous channels, the evaporator could work stably in high concentrations of salt-water(25 wt% NaCl), showing strong salt resistance. Efficient water evaporation, excellent salt resistance, scalable preparation processes, and low-costraw materials make CPAS extremely promising for practical applications.

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.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

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.

No effect of invasive tree species on aboveground biomass increments of oaks and pines in temperate forests

Prunus serotina and Robinia pseudoacacia are the most widespread invasive trees in Central Europe.In addition,according to climate models,decreased growth of many economically and ecologically important native trees will likely be observed in the future.We aimed to assess the impact of these two neophytes,which differ in the biomass range and nitrogen-fixing abilities observed in Central European conditions,on the relative aboveground biomass increments of native oaks Qucrcus robur and Q.petraea and Scots pine Pinus sylvestris.We aimed to increase our understanding of the relationship between facilitation and competition between woody alien species and overstory native trees.We established 72 circular plots(0.05 ha)in two different forest habitat types and stands varying in age in western Poland.We chose plots with different abundances of the studied neophytes to determine how effects scaled along the quantitative invasion gradient.Furthermore,we collected growth cores of the studied native species,and we calculated aboveground biomass increments at the tree and stand levels.Then,we used generalized linear mixed-effects models to assess the impact of invasive species abundances on relative aboveground biomass increments of native tree species.We did not find a biologically or statistically significant impact of invasive R.pseudoacacia or P.serotina on the relative aboveground,biomass increments of native oaks and pines along the quantitative gradient of invader biomass or on the proportion of total stand biomass accounted for by invaders.The neophytes did not act as native tree growth stimulators but also did not compete with them for resources,which would escalate the negative impact of climate change on pines and oaks.The neophytes should not significantly modify the carbon sequestration capacity of the native species.Our work combines elements of the per capita effect of invasion with research on mixed forest management.

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.