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Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 model performance and Independence

Advances in Atmospheric Sciences 2024年第8期41卷 1465-1482页

作者： Jiazhen ZHAO Shengping HE Ke FAN Huijun WANG Fei LI Key Laboratory of Meteorological Disaster Ministry of Education/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 Geophysical Institute University of Bergen and Bjerknes Centre for Climate ResearchBergen 5007Norway School of Atmospheric Science Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research Bergen 5007Norway Nansen-Zhu International Research Center Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519082China

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 ***, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

关键词： wintertime newly formed Arctic sea ice model democracy model weighting scheme model performance model independence

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Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

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Journal of Forestry Research 2024年第3期35卷 111-130页

作者： Xin Chen Yujun Sun State Forestry&Grassland Administration Key Laboratory of Forest Resources&Environmental Management Beijing Forestry UniversityBeijing 100083People's Republic of China

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable ***,the potential of GEE for forest resource management has not been fully *** extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast *** and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant *** results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source ***,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was *** terrain factors and removing spatial correlation from NFRI sample points further improved the recognition *** optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,*** high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant *** the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

关键词： Google Earth Engine Sentinel Forest resource inventory data Dominant woody plant species Subtropics model performance

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performance Improvement of Artificial Neural Network model in Short-term Forecasting of Wind Farm Power Output

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Journal of Modern Power Systems and Clean Energy 2020年第3期8卷 484-490页

作者： Sergio Velázquez Medina Ulises Portero Ajenjo Department of Electronics and Automatics Engineering Universidad de Las Palmas de Gran CanariaCampus de Tafira s/n35017 Las Palmas de Gran CanariaCanary IslandsSpain School of Industrial and Civil Engineering University of Las Palmas de Gran CanariaCampus de Tafira s/n35017 Las Palmas de Gran CanariaCanary IslandsSpain

Due to the low dispatchability of wind power,the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible.A study is conducted in the present paper of potential improvements to the performance of artificial neural network(ANN)models in terms of efficiency and ***,current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station,in addition to selecting a fixed number of time periods prior to the *** this respect,new ANN models are proposed in this paper,which are developed by:varying the number of prior 1-h periods(periods prior to the forecasting hour)chosen for the input layer parameters;and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference *** has been found that the model performance is always improved when data from a second weather station are *** mean absolute relative error(MARE)of the new models is reduced by up to 7.5%.Furthermore,the longer the forecasting horizon,the greater the degree of improvement.

关键词： Artificial neural networks(ANN) wind power forecasting model performance wind power output

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The performance of Atmospheric Component model R42L9 of GOALS/LASG

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Advances in Atmospheric Sciences 2003年第5期20卷 726-742页

作者：吴统文刘平王在志刘屹岷宇如聪吴国雄 LASG Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029

This paper examines the performance of an atmospheric general circulation model (AGCM) developed at the State Key Laboratory of Numerical modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics (LASG/IAP). It is a spectral model truncated at R42(2.8125°long×1.66°lat) resolution and with nine vertical levels, and referred to as R42L9/LASG hereafter. It is also the new version of atmospheric component model R15L9 of the global ocean-atmosphere-land system (GOALS/LASG). A 40-year simulation in which the model is forced with the climatological monthly mean sea surface temperature is compared with the 40-year (1958-97) U.S. National Center for Environmental Prediction (NGEP) global reanalysis and the 22-year (1979-2000) Xie-Arkin monthly precipitation climatology. The mean DJF and JJA geographical distributions of precipitation, sea level pressure, 500-hPa geopotential height, 850-hPa and 200-hPa zonal wind, and other fields averaged for the last 30-year integration of the R42L9 model are analyzed. Results show that the model reproduces well the observed basic patterns, particularly precipitation over the East Asian region. Comparing the new model with R15L9/LASG, the old version with coarse resolution (nearly 7.5°long×4.5°lat), shows an obvious improvement in the simulation of regional climate, especially precipitation. The weaknesses in simulation and future improvements of the model are also discussed.

关键词： Global ocean-atmosphere-land system (GOALS) model development general atmospheric model (R42L9) model performance

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Effect of calibration data series length on performance and optimal parameters of hydrological model

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Water Science and Engineering 2010年第4期3卷 378-393页

作者： Chuan-zhe LI Hao WANG Jia LIU Deng-hua YAN Fu-liang YU Lu ZHANG Department of Water Resources China Institute of Water Resources and Hydropower Research Beijing 100038 P. R. China Water and Environmental Management Research Centre Department of Civil Engineering University of Bristol Bristol BS8 1TR UK CSIRO Land and Water Canberra ACT 2601 Australia

In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

关键词： calibration data series length model performance optimal parameter hydrological model data-limited catchment

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Harnessing AI-human synergy for deep learning research analysis in ophthalmology with large language models assisting humans

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Eye Science 2024年第1期1卷 7-25页

作者：罗明杰张玮星张哲铭庞健宇林桢哲赵兰琴林铎儒林浩添 State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Center for Precision Medicine and Department of Genetics and Biomedical Informatics Zhongshan School of MedicineSun Yat-sen UniversityGuangzhou 510060China Hainan Eye Hospital and Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityHaikou 570311China

Background:Research innovations inocular disease screening,diagnosis,and management have been boosted by deep learning(DL)in the last *** assess historical research trends and current advances,we conducted an artificial intelligence(AI)-human hybrid analysis of publications on DL in ***:All DL-related articles in ophthalmology,which were published between 2012 and 2022 from Web of Science,were included.500 high-impact articles annotated with key research information were used to fine-tune a large language models(LLM)for reviewing medical literature and extracting *** verifying the LLM's accuracy in extracting diseases and imaging modalities,we analyzed trend of DL in ophthalmology with 2535 ***:Researchers using LLM for literature analysis were 70%(P=0.0001)faster than those who did not,while achieving comparable accuracy(97%versus 98%,P=0.7681).The field of DL in ophthalmology has grown 116%annually,paralleling trends of the broader DL *** publications focused mainly on diabetic retinopathy(P=0.0003),glaucoma(P=0.0011),and age-related macular diseases(P=0.0001)using retinal fundus photographs(FP,P=0.0015)and optical coherence tomography(OCT,P=0.0001).DL studies utilizing multimodal images have been growing,with FP and OCT combined being the most *** the 500 high-impact articles,laboratory studies constituted the majority at 65.3%.Notably,a discernible decline in model accuracy was observed when categorizing by study design,notwithstanding its statistical ***,43 publicly available ocular image datasets were ***:This study has characterized the landscape of publications on DL in ophthalmology,by identifying the trends and breakthroughs among research topics and the fast-growing *** study provides an efficient framework for combined AI-human analysis to comprehensively assess the current status and future trends in the field.

关键词： large language model AI-human collaboration research trends ophthalmology model performance

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Parameter regionalization of the FLEX-Global hydrological model

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Science China Earth Sciences 2021年第4期64卷 571-588页

作者： Jingjing WANG Hongkai GAO Min LIU Yongjian DING Yan WANG Fang ZHAO Jun XIA Key Laboratory of Geographic Information Science(Ministry of Education of China) Shanghai 200241China School of Geographical Sciences East China Normal UniversityShanghai 200241China State Key Laboratory^of Cryospheric Science Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China Journal Editorial Department of Anhui Agricultural University Hefei 230036China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan UniversityWuhan 430072China

Global hydrological models(GHMs) are important tools for addressing worldwide change-related water resource problems from a global perspective. However, the development of these models has long been hindered by their low accuracy. In order to improve the streamflow simulation accuracy of GHMs, we developed a GHM—the FLEX-Global—based on the regionalization of hydrological model parameters. The FLEX-Global model is primarily based on the framework of the FLEX hydrological model coupled with the HAND-based Storage Capacity curve(HSC) runoff generation module to calculate net rainfall, and uses the global river-routing Ca Ma-Flood model to calculate river network routing. This new model allows for streamflow simulation at a spatial resolution of 0.5°×0.5° and a temporal resolution of 1 day in global catchments. To validate FLEX-Global accuracy, the FLEX-Global-simulated streamflow of 26 major rivers distributed in five different climate zones was compared with the observed data from the Global Runoff Data Center(GRDC). Next, the model performance of FLEXGlobal was further verified by comparing it with that of seven existing GHMs with varying accuracy in the five climate ***-metric evaluation indicated that the streamflow simulation accuracy was improved by the FLEX-Global model with regionalized parameters, especially in the tropical and dry climate zones. This newly-developed GHM with regionalized parameters can provide scientific support for the assessment of climate change impact, optimization of global water resource mangement, simulation of Earth's multi-sphere coupling, and implementation of the Inter-Sectoral Impact model Intercomparison Project(ISIMIP).

关键词： Global hydrological model model performance Parameter regionalization

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Application of the SCS-CN model to Runoff Estimation in a Small Watershed with High Spatial Heterogeneity

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Pedosphere 2011年第6期21卷 738-749页

作者： XIAO Bo WANG Qing-Hai FAN Jun HAN Feng-Peng DAI Quan-Hou Beijing Research ＆ Development Center for Grass and Environment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 （China） State Key Laboratory of Soil Erosion and Dryland Farmning on the Loess Plateau Institute of Soil and Water Conservation Chinese Academy of Sciences Yangling 712100 （China） Colleqe of Forest Guizhou University Guiyang 550025 （China）

For reasons of simplicity, the most commonly used hydrological models are based on the Soil Conservation Service Curve Number （SCS-CN） model, which is probably a good choice for the estimation of runoff on the Loess Plateau of China; however, the high spatial heterogeneity, mainly caused by a fragmented landform and variations in soil type, may limit its applicability to this region. Therefore, applicability of the SCS-CN model to a small watershed, Liudaogou on the plateau, was evaluated and the most appropriate initial abstraction ratio （I~/S） value in the model was quantified by the inverse method. The results showed that the standard SCS-CN model was applicable to the estimation of runoff in the Liudaogou watershed and the model performance was acceptable according to the values of relative error and Nash-Sutcliffe efficiency. The most appropriate Ia/S value for the watershed was 0.22 because with this modified Ia/S value, the model performance was slightly improved. The model performance was not sensitive to the modification of the Ia/S value when one heavy rainfall event （50.1 mm） was not considered, which implied that the model, using a standard Ia/S value, can be recommended for the Liudaogou watershed because single rainfall events exceeding 50 mm seldom occurred in that region. The runoff amount predicted for the Liudaogou watershed by the SCS-CN model, using the modified Ia/S value, increased gradually with increasing rainfall when rainfall values were lower than 50 mm, whereas the predicted amount increased rapidly when the rainfall exceeded 50 mm. These findings may be helpful in solving the problem of serious soil and water loss on the Loess Plateau of China.

关键词： curve number initial abstraction ratio model performance rainfall soil and water loss

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Characteristics of greenhouse gas emissions from rice paddy fields in South Korea under climate change scenario RCP-8.5 using the DNDC model

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Pedosphere 2021年第2期31卷 332-341页

作者： Wonjae HWANG Chanyang KIM Kijong CHO Seunghun HYUN Department of Environmental Science and Ecological Engineering Korea UniversitySeoul 02841(South Korea)

Understanding the greenhouse gas(GHG)emission from rice paddy fields is essential to come up with appropriate countermeasure in response to global ***,GHG emissions from paddy fields in South Korea are not well *** objectives of this study were to estimate the carbon dioxide(CO_(2))and methane(CH_(4))emissions from rice paddy fields in South Korea,under the Representative Concentration Pathway 8.5(RCP-8.5)climate change scenario using the DNDC(i.e.,DeNitrification-DeComposition)model at 1-km^(2)*** performance of the model was verified with field data collected using a closed chamber,which supports the application of the model to South *** the model predictions and field measurements showed that most(>95%)GHG emissions occur in the cropping period,between April and *** a baseline(assuming no climate change),the national sums of the CO_(2)and CH_(4)emissions for the 2020 s and 2090 s were estimated to be 5.8×10^(6)and 6.0×10^(6)t CO_(2)-equivalents(CO_(2)-eq)year^(-1)for CO_(2)and 6.4×10^(6)and 6.6×10^(6)t CO_(2)-eq year^(-1)for CH_(4),respectively,indicating no significant changes over 80 *** RCP-8.5,in the 2090 s,CH_(4)emissions were predicted to increase by 10.7×10^(6)and 14.9×10^(6)t CO_(2)-eq year^(-1),for a 10-or 30-cm tillage depth,***,the CO_(2)emissions gradually decreased with rising temperatures,due to reduced root *** tillage increased the emissions of both GHGs,with a more pronounced effect for CH_(4)than CO_(2).Intermittent drainage in the middle of the cropping season can attenuate the CH_(4)emissions from paddy *** findings of this work will aid in developing nationwide policies on agricultural land management in the face of climate change.

关键词： CH_(4) CO_(2) model performance paddy soil rising temperature tillage depth

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Detection of COVID-19 and Pneumonia Using Deep Convolutional Neural Network

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Computer Systems Science & Engineering 2023年第1期44卷 519-534页

作者： Md.Saiful Islam Shuvo Jyoti Das Md.Riajul Alam Khan Sifat Momen Nabeel Mohammed Department of Electrical and Computer Engineering North South UniversityPlot 15Block BBashundharaDhaka1229Bangladesh

COVID-19 has created a panic all around the *** is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over the *** healthcare sector of the world is facing great challenges tackling COVID *** of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia *** this article,we propose a deep Convo-lutional Neural Network(CNN)based approach to detect COVID+(i.e.,patients with COVID-19),pneumonia and normal cases,from the chest X-ray ***-19 detection from chest X-ray is suitable considering all aspects in compar-ison to Reverse Transcription Polymerase Chain Reaction(RT-PCR)and Computed Tomography(CT)*** deep CNN models including VGG16,InceptionV3,DenseNet121,DenseNet201 and InceptionResNetV2 have been adopted in this pro-posed *** have been trained individually to make particular *** results demonstrate that DenseNet201 provides overall better performance with accuracy,recall,F1-score and precision of 94.75%,96%,95%and 95%*** careful comparison with results available in the literature,we have found to develop models with a higher *** the studies were carried out using a publicly available chest X-ray(CXR)image data-set.

关键词： COVID-19 convolutional neural network deep learning DenseNet201 model performance

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