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Effect of preprocessing on performances of machine learning-based mineral composition analysis on gas hydrate sediments, Ulleung Basin, East Sea

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Petroleum Science 2025年第1期22卷 151-162页

作者： Hongkeun Jin Ju Young Park Sun Young Park Byeong-Kook Son Baehyun Min Kyungbook Lee Department of Geoenvironmental Sciences Kongju National University Tunnel & Underground Facility Division ESCO Consultant & Engineers Company Yellow Sea Institute of Geoenvironmental Sciences Petroleum and Marine Research Division Korea Institute of Geoscience and Mineral Resources Department of climate and Energy Systems Engineering Ewha Womans University center for climate/Environment change Prediction Research Ewha Womans University

Gas hydrate(GH) is an unconventional resource estimated at 1000-120,000 trillion *** on GH is ongoing to determine its geological and flow characteristics for commercial production. After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin, the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD). Because the analysis is costly and dependent on experts, a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data. However, the model's performance was limited because of improper preprocessing of the intensity profile. Because preprocessing was applied to each feature, the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition. In this study, the profile was preprocessed for each sample using minmax scaling because relative intensity is critical for mineral analysis. For 49 test data among the 488data, the convolutional neural network(CNN) model improved the average absolute error and coefficient of determination by 41% and 46%, respectively, than those of CNN model with feature-based preprocessing. This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data. The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau. In addition, the overall procedure can be applied to any XRD data of sediments worldwide.

关键词： Sample-based preprocessing X-ray diffraction (XRD) Machine learning Mineral composition Gas hydrate (GH) Ulleung basin

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Interannual variability of boreal summer intraseasonal oscillation over the northwestern Pacific influenced by the Pacific Meridional Mode

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 6-11页

作者： Haoyu Zhou Pang-Chi Hsu Lin Chen Yitian Qian Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjingChina

During the boreal summer,intraseasonal oscillations exhibit significant interannual variations in intensity over two key regions:the central-western equatorial Pacific(5°S-5°N,150°E-150°W)and the subtropical Northwestern Pacific(10°-20°N,130°E-175°W).The former is well-documented and considered to be influenced by the ENSO,while the latter has received comparatively less attention and is likely influenced by the Pacific Meridional Mode(PMM),as suggested by partial correlation analysis *** elucidate the physical processes responsible for the enhanced(weakened)intraseasonal convection over the subtropical northwestern Pacific during warm(cold)PMM years,the authors employed a moisture budget *** findings reveal that during warm PMM years,there is an increase in summer-mean moisture over the subtropical northwestern *** increase interacts with intensified vertical motion perturbations in the region,leading to greater vertical moisture advection in the lower troposphere and consequently resulting in convective *** a process is pivotal in amplifying intraseasonal convection *** observational findings were further verified by model experiments forced by PMM-like sea surface temperature patterns.

关键词： Boreal summer intraseasonal oscillation Interannual variability Pacific Meridional Mode Moisture budget analysis

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Effect of Parameter Variation in the BMJ Scheme on the Simulation of MJO Propagation and Structure

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Advances in Atmospheric Sciences 2025年第2期42卷 397-409页

作者： Xiaoyu ZHU Zhong ZHONG Yimin ZHU Yunying LI Yijia HU Yao HA College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China Key Laboratory for Virtual Geographic Environment School of Geography ScienceNanjing Normal UniversityNanjing 210023China Jiangsu Collaborative Innovation center for climate change School of Atmospheric SciencesNanjing UniversityNanjing 210023China

In this study,the Betts-Miller-Janjic(BMJ)convective adjustment scheme in the Weather Research and Forecasting(WRF)model version 4.0 was used to investigate the effect of itsα-parameter,which influences the first-guess potential temperature reference profile on the Madden-Julian oscillation(MJO)propagation and *** study diagnosed the MJO active phase composites of the MJO-filtered outgoing longwave radiation(OLR)during the December-to-February(DJF)period of 2006-2016 over the Indian Ocean(IO),Maritime Continent(MC),and western Pacific(WP).The results show that the MJO-filtered OLR intensity,propagation pattern,and MJO classification(standing,jumping,and propagating clusters)are sensitive to theα-value,but the phase speeds of propagating MJOs are ***,with an increasingα-value,the simulated MJO-filtered OLR intensity increases,and the simulated propagation pattern is *** also show that the intensity and propagation pattern of an eastward-propagating MJO are associated with MJO circulation structures and thermodynamic ***αincreases,the front Walker cell and the low-level easterly anomaly are enhanced,which premoistens the lower troposphere and triggers more active shallow and congestus *** enhanced shallow and congestus convection preconditions the lower to middle troposphere,accelerating the transition from congestus to deep convection,thereby facilitating eastward propagation of the ***,the simulated MJO tends to transfer from standing to eastward propagating asα*** summary,increasing theα-value is a possible way to improve the simulation of the structure and propagation of the MJO.

关键词： MJO WRF model BMJ scheme low-level easterly anomaly

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Pods Filling in Natural Pollination of 10 Spontaneous Cocoa Trees from French Guiana

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Agricultural Sciences 2025年第1期16卷 193-205页

作者： Inago Caudou Trebissou Gnion Mathias Tahi Kacou Antoine Alban M’bo Cacao Breeding Laboratory National Center for Agronomic Research (CNRA) Research Station of Divo Divo Cte dIvoire African center of Excellence on climate change Biodiversity and Sustainable Agriculture (CEA-CCBAD/WASCAL) Flix Houphout-Boigny University (UFHB) Abidjan Cte dIvoire

The study focuses on the filling of pods from ten cacao clones originating from French Guiana in Côte d’Ivoire. Two reference clones (IFC5 and NA32) were used as controls. The parameters studied include the number of ovules per ovary, the average number of beans per pod, the fresh bean weight, and the pod index. The aim was to evaluate the genetic variability and agronomic performance of these clones under natural pollination conditions. The results show significant genetic variability between the clones for all traits studied. The IFC5 clone, known for its excellent pod filling, exhibited the highest apparent fertility (0.91), while the GU183/A clone had the lowest filling (0.47). Three types of distributions of the number of beans per pod were identified: a right-skewed unimodal distribution, characteristic of well-filled clones;a left-skewed unimodal distribution, associated with poorly filled clones;and an intermediate bimodal distribution. These differences could be related to pollination compatibility and the availability of compatible pollen. Regarding the fresh bean weight and pod index, the GU183/A clone stood out with the heaviest beans (3.27 g) but had a high pod index (49.58), indicating relatively low productivity. None of the Guyanese clones achieved the filling level of the IFC5 clone, although some surpassed the NA32 control. This study highlights the importance of apparent fertility and the number of beans per pod as essential criteria for cacao genetic improvement programs. The authors recommend extending research to a larger number of Guyanese clones and exploring complementary traits, such as the influence of pollination type and intergroup compatibility.

关键词： Theobroma cacao Phytophthora Pod Index Apparent Fertility

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A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

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Advances in Atmospheric Sciences 2025年第1期42卷 36-52页

作者： Bofei ZHANG Haipeng YU Zeyong HU Ping YUE Zunye TANG Hongyu LUO Guantian WANG Shanling CHENG Key Laboratory of Cryospheric Science and Frozen Soil Engineering Nagqu Plateau Climate and Environment Observation and Research Station of Tibet Autonomous RegionNorthwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China University of the Chinese Academy of Sciences Beijing 100049China Key Open Laboratory of Arid climate change and Disaster Reduction of CMA Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu ProvinceInstitute of Arid MeteorologyChina Meteorological AdministrationLanzhou 730000China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China

Seasonal precipitation has always been a key focus of climate *** a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal ***,solving a nonlinear problem through linear regression is significantly *** study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National climate center climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in *** model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached *** PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,*** observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.

关键词： observational constraint LightGBM seasonal prediction summer precipitation machine learning

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Predictability Study of Weather and climate Events Related to Artificial Intelligence Models

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Advances in Atmospheric Sciences 2025年第1期42卷 1-8页

作者： Mu MU Bo QIN Guokun DAI Department of Atmospheric and Oceanic Sciences&Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China Shanghai Key Laboratory of Ocean-land-atmosphere Boundary Dynamics and climate change Fudan UniversityShanghai 200438China Shanghai Frontiers Science center of Atmosphere-Ocean Interaction Fudan UniversityShanghai 200438China CMA-FDU Joint Laboratory of Marine Meteorology Shanghai 200438China Key Laboratory of Polar Atmosphere-ocean-ice System for Weather and climate Ministry of EducationShanghai 200438China

Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate *** the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant ***,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful *** this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization *** this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability *** models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven ***,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific *** addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model *** predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.

关键词： predictability artificial intelligence models simulation and forecasting nonlinear optimization cognition–observation–model paradigm

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Impact of Skin Temperature Control Variable on the Assimilation of Microwave Temperature-sounding Channels in Regional Numerical Weather Prediction

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Advances in Atmospheric Sciences 2025年第3期42卷 564-578页

作者： Yaodeng CHEN Qihang YANG Luyao QIN Yuanbing WANG Deming MENG Xusheng YAN Key Laboratory of Meteorological Disaster Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing 210044China College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China Key Laboratory of Mesoscale Severe Weather Ministry of EducationSchool of Atmospheric SciencesNanjing UniversityNanjing 210023China

Accurate skin temperature is one of the critical factors in successfully assimilating satellite radiance data over ***,model-simulated skin temperature may not be accurate *** address this issue,an extended skin temperature control variable(TSCV) approach is proposed in a variational assimilation framework,which also considers the background error correlation between skin temperature and atmospheric variables.A series of single observation tests and a 10-day cycling assimilation experiment were conducted to evaluate the impact of the TSCV approach on the assimilation of AMSU-A and ATMS(Advanced Technology Microwave Sounder) microwave temperature-sounding channels over *** results of the single observation tests show that by applying the TSCV approach,not only the direct analysis of skin temperature is realized,but also the interaction between skin temperature and atmospheric variables can be achieved during the assimilation *** results of the cycling experiment demonstrate that the TSCV approach improves the skin temperature analysis,which in turn reduces the RMSE of the surface variables and low-level air temperature *** TSCV approach also reduces the difference between the observed and simulated brightness temperatures of both microwave and infrared window channels over land,suggesting that the approach can facilitate the radiance simulation of these channels,thus contributing to the assimilation of window channels.

关键词： radiance assimilation skin temperature control variable microwave temperature-sounding numerical weather prediction

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Biochar as a climate-Smart Agricultural Practice: Reducing Greenhouse Gas Emissions and Promoting Sustainable Farming

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Phyton-International Journal of Experimental Botany 2025年第1期94卷 65-99页

作者： Muhammad Nazim Abdul Ghafoor Abida Hussain Mehwish Tabassum Aamir Nawaz Muhammad Ahmad Murad Muhammad Muqarrab Ali State Key Laboratory of Desert and Oasis Ecology Key Laboratory of Ecological Safety and Sustainable Development in Arid LandsXinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi830011China University of Chinese Academy of Sciences Beijing100049China center for Water and Environmental Studies College of Agricultural and Food SciencesKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of climate change Faculty of Agriculture and Environmental SciencesMuhammad Nawaz Shareef University of AgricultureMultan66000Pakistan Department of Botany Division of Science and Technology University of EducationLahore54000Pakistan Institute of Chemistry Faculty of Chemical and Biological SciencesThe Islamia University of BahawalpurBahawalpur63100Pakistan Department of Agronomy University of Agriculture FaisalabadFaisalabad38040Pakistan

In recent years, the world has faced rising global temperatures, accumulative pollution, and energy crises, stimulating scientists worldwide to strive for eco-friendly and cost-effective solutions. Biochar has materialized as a favorable tool for environmental remediation, indicating efficacy as an efficient sorbent substance for both inorganic and organic pollutants in environmental field. These unique properties exclude improved surface functionality, porous morphology, large specific surface area (SSA), cation exchange capacity (CEC), robust adsorption capabilities, environmental stability, and embedded micronutrients. Biochar exhibited potential characteristics for environmental oversight, greenhouse gas (GHG) emission reduction, and soil fertility improvement. This review explores the impact of fundamental factors such as retention time, pyrolysis temperature, gas flow rate, and reactor design on biochar yield and properties. Collected data revealed the various applications of biochar, ranging from waste management and construction materials to the adsorptive removal of hydrocarbon lubricants from aqueous media, contaminant immobilization, and carbon sequestration. It has played mostly a significant share in climate change mitigation and an important role in soil amendments. Biochar improves soil improvement by increasing water retention (10%–30%), carbon sequestration, soil surface functionality, and providing high surface area with chemical stability. The assessment also reports the prospects and contests associated with biochar application uses in various agriculture cropping ecosystems. Inclusive, this review highlights the multifaceted characteristics of biochar as an adjustable on top of a sustainable solution addressing greenhouse gas emission, carbon sequestration, and environmental stresses. However, further research is needed to understand its long-term impacts and optimal applications fully.

关键词： Agro-ecology biochar carbon storage greenhouse gas reduction sustainable agriculture

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Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 18-23页

作者： Jiarui Cai Bo Sun Huijun Wang Yi Zheng Siyu Zhou Huixin Li Yanyan Huang Peishu Zong Collaborative Innovation center on forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjingChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiChina China Meteorological Administration Key Laboratory of Transportation MeteorologyNanjingChina Jiangsu Meteorological Observatory NanjingChina

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical *** learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many *** this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,*** two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction *** addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

关键词： Groundwater depth Multi-head attention Improved dung beetle optimizer CNN-LSTM CNN-GRU Ningxia

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Performance of CRA-40 reanalysis in representing the Antarctic winter stratospheric circulation: a comparison with ERA-5 and MERRA-2

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Earth and Planetary Physics 2025年第02期9卷 452-459页

作者： ZiXu Wang JingGao Hu Jing-Jia Luo Jian Rao ZongZe Li LiuChi Yan Key Laboratory of Meteorological Disaster Ministry of Education (KLME) Joint International Research Laboratory of Climate and Environment Change (ILCEC) Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) Nanjing University of Information Science & Technology Institute for climate and Application Research (ICAR) Nanjing University of Information Science and Technology State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) Institute of Atmospheric Physics Chinese Academy of Sciences University of Chinese Academy of Sciences

This paper evaluates a representation of winter stratospheric circulation in the Antarctic that is based on CRA-40, a 40-year global reanalysis dataset released by the China Meteorological Administration, and compares it with representations based on two other state-of-the-art reanalysis datasets: the fifth-generation atmospheric reanalysis provided by the European Centre for Medium-Range Weather Forecasts(ERA-5) and the Modern-Era Retrospective analysis for Research and Applications, version 2(MERRA-2). In terms of climatology, we find that CRA-40 portrays a stronger and colder polar vortex in the middle and lower stratosphere than ERA-5, but a weaker and warmer one than MERRA-2. However, disagreement among the three reanalyses is confined mainly to the period before1999, and is largely reduced after that time. On the interannual timescale, portrayals of the intensity and area of the 10-hPa polar vortex are quite consistent among the three reanalyses, with correlation coefficients greater than 0.9 between each pair of reanalyses. In addition, the central dates of most sudden stratospheric deceleration(SSD) events at 10 hPa in the three reanalyses differ by less than one day, indicating that CRA-40 is also highly consistent with the other two reanalysis datasets regarding daily evolution. Our analyses suggest that CRA-40 performs comparably to ERA-5 and MERRA-2 in characterizing winter circulation in the Antarctic middle and lower stratosphere.

关键词： CRA-40 ERA-5 MERRA-2 Antarctic stratosphere reanalysis intercomparison

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