AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surfa...AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.展开更多
A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU w...A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU was developed for Pakistani crudes from Zamzama and Kunnar fields.A hybrid methodology based on the integration of Taguchi method and genetic algorithm(GA)was employed to estimate the optimal cut point temperature for various sets of process variables.Optimised datasets were utilised to develop an artificial neural networks(ANN)model for the prediction of optimum values of cut points.The ANN model was then used to replace the hybrid framework of the Taguchi method and the GA.The integration of the ANN and FP model makes it a grey-box(GB)model.For the case of Zamama crude,the GB model helped in the decrease of up to 38.93%in energy required per kilo barrel of diesel and an 8.2%increase in diesel production compared to the stand-alone FP model under uncertainty.Similarly,for Kunnar crude,up to 18.87%decrease in energy required per kilo barrel of diesel and a 33.96%increase in diesel production was observed in comparison to the stand-alone FP model.展开更多
The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature （Tb） sim- ulation. How to reduce the uncertainty in the Tb simu...The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature （Tb） sim- ulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging （BMA） ensemble approach. The simulations by the community microwave emission model （CMEM） cou- pled with the community land model version 4.5 （CLM4.5） over China's Mainland are con- ducted by the 24 configurations from four vegetation opacity parameterizations （VOPs）, three soil dielectric constant parameterizations （SDCPs）, and two soil roughness param- eterizations （SRPs）. Compared with the simple arithmetical averaging （SAA） method, the BMA reconstructions have a higher spatial correlation coefficient （larger than 0.99） than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system （AMSR-E） at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference （RMSD） of 4 K and a temporal correlation coefficient of 0.64.展开更多
In developing countries,solar energy is the largest source of energy,accounting for 35%–45%of the total energy supply.This energy resource plays a vital role in meeting the energy needs of the world,especially in Vie...In developing countries,solar energy is the largest source of energy,accounting for 35%–45%of the total energy supply.This energy resource plays a vital role in meeting the energy needs of the world,especially in Vietnam.Vietnam has favorable natural conditions for this energy production.Because it is hot and humid,and it has much rainfall and fertile soil,biomass develops very quickly.Therefore,byproducts from agriculture and forestry are abundant and continuously increasing.However,byproducts that are considered natural waste have become the cause of environmental pollution;these include burning forests,straw,and sawdust in the North;and rice husks dumped into rivers and canals in the Mekong Delta region.Biomass energy is provided in a short cycle,is environmentally safe to use and is encouraged by organizations that support sustainable development.Taking advantage of this energy source provides energy for economic development and ensures environmental protection.Due to the abovementioned favorable conditions,many biomass energy plants are being built in Vietnam.Like other renewable energy investment projects,the selection of the construction contractor,the selection of equipment for the installation of the power plant,and the choice of construction site are complex multi-criteria decisions.In this case,decisionmakers must evaluate many qualitative and quantitative factors.These factors interact with each other and it is difficult to use personal experience to choose the optimal solution for such complex decision-making problems,especially in a fuzzy decision-making environment.Therefore,in this study,the authors use a Multi-Criteria Decision-Making(MCDM)model that uses a Fuzzy Analytic Hierarchy Process(FAHP)model and the Combined Compromise Solution(CoCoSo)algorithm to select biomass furnace suppliers utilizing both qualitative and quantitative factors.Furthermore,the results of this work will provide the first look at a hybrid CoCoSo/FAHP method that decision-makers in other fields can use to find the best supplier.展开更多
Industry 4.0 refers to the fourth evolution of technology development,which strives to connect people to various industries in terms of achieving their expected outcomes efficiently.However,resource management in an I...Industry 4.0 refers to the fourth evolution of technology development,which strives to connect people to various industries in terms of achieving their expected outcomes efficiently.However,resource management in an Industry 4.0 network is very complex and challenging.To manage and provide suitable resources to each service,we propose a FogQSYM(Fog—Queuing system)model;it is an analytical model for Fog Applications that helps divide the application into several layers,then enables the sharing of the resources in an effective way according to the availability of memory,bandwidth,and network services.It follows theMarkovian queuing model that helps identify the service rates of the devices,the availability of the system,and the number of jobs in the Industry 4.0 systems,which helps applications process data with a reasonable response time.An experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application,which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response time.After implementing the proposed model with different sizes of fog services in Industry 4.0 applications,FogQSYM provides a lower response time than the existing optimized response time model.It should also be noted that the average response time increases when the arrival rate increases.展开更多
本文利用普林斯顿大学全球大气强迫场资料，驱动公用陆面过程模式（Community Land Model version 4．0，CLM4．O）模拟了中国区域1961-2010年土壤湿度的时空变化。将模拟结果与观测结果、美国国家环境预报中心再分析数据（Naional Cent...本文利用普林斯顿大学全球大气强迫场资料，驱动公用陆面过程模式（Community Land Model version 4．0，CLM4．O）模拟了中国区域1961-2010年土壤湿度的时空变化。将模拟结果与观测结果、美国国家环境预报中心再分析数据（Naional Centers for Environmental Prediction Reanalysis，NCEP）和高级微波扫描辐射计（Advanced Microwave Scanning Radiometer-EOS，AMSR-E）反演的土壤湿度进行了对比分析，结果表明CLM4．0模拟结果可以反映出中国区域观测土壤湿度的空间分布和时空变化特征，但东北、江淮和河套三个地区模拟值相对于观测值在各层次均系统性偏大。模拟与NCEP再分析土壤湿度的空间分布基本一致，与AMSR-E的反演值在35°N以北的分布也基本一致；从1961-2010年土壤湿度模拟结果分析得出，各层土壤湿度空间分布从西北向东南增加。低值区主要分布在新疆、青海、甘肃和内蒙古西部地区。东北平原、江淮地区和长江流域为高值区。土壤湿度数值总体上从浅层向深层增加。不同深度土壤湿度变化趋势基本相同。除新疆西部和东北部分地区外，土壤湿度在35°N以北以减少趋势为主，30°N以南的长江流域、华南及西南地区以增加为主。在全球气候变暖的背景下，CLM4．0模拟的夏季土壤湿度在不同程度上响应了降水的变化。中国典型干旱区和半干旱区土壤湿度减小，湿润区增加。其中湿润区土壤湿度对降水的响应最为显著，其次是半干旱区和干旱区。展开更多
利用1961-2010年普林斯顿大学每3 h一次、1°×1°的大气强迫场数据驱动公用陆面模式CLM4.0(Common Land Model,version4.0)对黄河源区土壤湿度的时空分布进行了模拟试验,合理优化了CLM 4.0中土壤有机质和土壤质地属性参数...利用1961-2010年普林斯顿大学每3 h一次、1°×1°的大气强迫场数据驱动公用陆面模式CLM4.0(Common Land Model,version4.0)对黄河源区土壤湿度的时空分布进行了模拟试验,合理优化了CLM 4.0中土壤有机质和土壤质地属性参数,将模拟结果与荷兰自由大学AM SR-E土壤湿度产品进行了对比分析,并利用玛曲土壤湿度观测站点的观测数据对模拟结果进行了验证。结果表明,CLM4.0模式能较好的模拟黄河源区土壤湿度的空间分布及变化趋势,在优化陆面有机质和土壤质地数据参数后,模拟的土壤湿度空间分布更合理,但CLM4.0模拟的土壤湿度比地面观测值和AMSR-E土壤湿度产品的土壤湿度偏低。展开更多
利用第二次全国土壤调查土壤质地数据（SNSS）和中国区域陆地覆盖资料（CLCV）将陆面过程模式CLM3.5（Community Land Model version 3.5）中基于联合国粮食农业组织发展的土壤质地数据（FAO）和MODIS卫星反演的陆地覆盖数据（MODIS）...利用第二次全国土壤调查土壤质地数据（SNSS）和中国区域陆地覆盖资料（CLCV）将陆面过程模式CLM3.5（Community Land Model version 3.5）中基于联合国粮食农业组织发展的土壤质地数据（FAO）和MODIS卫星反演的陆地覆盖数据（MODIS）进行了替换,使用中国气象局陆面数据同化系统（CMA Land Data Assimilation System,CLDAS）大气强迫场资料,分别驱动基于同时改进土壤质地和陆地覆盖数据的CLM3.5（CLM-new）、基于只改进陆地覆盖数据的CLM3.5（CLM-clcv）、基于只改进土壤质地数据的CLM3.5（CLM-snss）和基于原始下垫面数据的CLM3.5（CLM-ctl）,对内蒙古地区2011~2013年土壤湿度的时空变化进行模拟试验,研究下垫面改进对CLM3.5模拟土壤湿度的影响。将四组模拟结果与46个土壤水分站点观测数据进行对比分析,结果表明：相对于控制试验,CLM-clcv、CLM-snss和CLM-new都能不同程度地改进土壤湿度模拟,其中CLM-clcv主要在呼伦贝尔改进明显,CLM-snss则在除呼伦贝尔以外的大部地区改进显著,CLM-ctl模拟的土壤湿度在各层上均系统性偏大,而CLM-new模拟土壤湿度最好地反映出内蒙古地区观测的土壤湿度的时空变化特征,显著改善了土壤湿度的模拟,体现在与观测值有着更高的相关系数和更小的平均偏差与均方根误差。展开更多
陆面模式CLM(Community Land Model)是目前国际上发展较为完善并被广泛应用的陆面过程模式。本文使用中国科学院寒区旱区环境与工程研究所位于青藏高原东部的若尔盖高原湿地生态系统研究站的观测资料,对CLM3.0版本及CLM4.0版本在上述地...陆面模式CLM(Community Land Model)是目前国际上发展较为完善并被广泛应用的陆面过程模式。本文使用中国科学院寒区旱区环境与工程研究所位于青藏高原东部的若尔盖高原湿地生态系统研究站的观测资料,对CLM3.0版本及CLM4.0版本在上述地区的模拟性能进行了检验与对比。通过比较观测值与模拟值,验证了模式在高原季节性冻土地区的适用性,发现CLM4.0较CLM3.0在模拟结果上有了一定提高。CLM4.0加入了未冻水参数化方案,使模式可以模拟到冬季土壤冻结后存留的未冻水,显著增加了冻融期间土壤含水量的模拟,同时减小了土壤含冰量的模拟值。并因此增大了模拟的冻土热容量,减小了热导率,使冻融期间土壤温度的模拟也有了一定改善。但是模拟中也发现对于较深层土壤,温度模拟值在冻融期间较观测显著偏低。另外,在消融(冻结)过程阶段CLM4.0模拟的土壤含水量骤增(骤降)的时间均较观测提前。消融过程、冻结过程阶段模拟时间偏短,而完全冻结、完全消融阶段模拟时间偏长。因此CLM对于高原冻土地区的模拟仍是其需要重点改进的地方之一。展开更多
利用NOAH(The Community Noah Land Surface Model)、SHAW(Simultaneous Heat and Water)和CLM(Community Land Model)3个不同的陆面过程模式及兰州大学(Semi-Arid Climate Observatory and Laboratory,SACOL)2007年的观测资料,对黄土...利用NOAH(The Community Noah Land Surface Model)、SHAW(Simultaneous Heat and Water)和CLM(Community Land Model)3个不同的陆面过程模式及兰州大学(Semi-Arid Climate Observatory and Laboratory,SACOL)2007年的观测资料,对黄土高原半干旱区的陆面过程进行了模拟研究。通过与观测值间的对比,考察不同陆面过程模式在半干旱区的适用性。研究结果表明:3个模式在半干旱区的模拟性能有较大差异。其中,CLM模式模拟的20 cm以上的浅层土壤温度最优,SHAW模式模拟的深层土壤温度最优;SHAW模式模拟的土壤含水量与观测值最为接近,而NOAH和CLM模式模拟值有较大偏差;3个模式均能较好地模拟地表反射辐射,其中SHAW模式模拟值与观测值的偏差最小;对地表长波辐射的模拟,CLM模式的模拟最优;3个模式均能较好地反映感热、潜热通量的变化趋势,其中CLM模式对感热的模拟性能优于其他两个模式,在有降水发生后的湿润条件下,CLM模式对潜热的模拟性能最优,而无降水的干燥条件下,CLM模式的模拟偏差最大,NOAH模式对冬季潜热的模拟最优。总体而言,CLM模式能够更好地再现半干旱区地气之间的相互作用,但模式对土壤含水量及干燥条件下的潜热通量的模拟较差,模式对半干旱区陆气间的水文过程还有待进一步的研究和改进。展开更多
利用CLM(Common Land Model)模式对我国内蒙古奈曼旗农牧交错带沙漠和农田两种不同典型下垫面的陆面过程进行了数值模拟试验,并与外场试验观测结果进行了对比分析。结果表明:无论是沙漠还是农田试验,CLM都能够较好地模拟其辐射通量和土...利用CLM(Common Land Model)模式对我国内蒙古奈曼旗农牧交错带沙漠和农田两种不同典型下垫面的陆面过程进行了数值模拟试验,并与外场试验观测结果进行了对比分析。结果表明:无论是沙漠还是农田试验,CLM都能够较好地模拟其辐射通量和土壤中的热传导特征,CLM的模拟结果能够真实地再现试验期间土壤热传导过程对天气过程的响应。相比而言,模式对沙漠地区长波辐射通量和干燥时期短波辐射通量的模拟结果好于农田,其原因可能是因为农田下垫面植被及土壤特征较沙漠复杂,有着很大的不确定性,造成了农田地表反照率和温度模拟的偏差。而对农田热传导的模拟结果好于沙漠,反映了CLM对含水量较大、持水力较强的农田下垫面的热传导模拟能力较好,而对含水量较小、持水力较弱的沙漠下垫面的热传导模拟能力相对较差。展开更多
基金Supported by National Natural Science Foundation of China(No.82160195,No.82460203)Degree and Postgraduate Education Teaching Reform Project of Jiangxi Province(No.JXYJG-2020-026).
文摘AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.
基金Higher Education Commission,Pakistan,under the National Research Program for Universities Project,Grant/Award Number:NBU-FPEJ-2024-1243-02。
文摘A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU was developed for Pakistani crudes from Zamzama and Kunnar fields.A hybrid methodology based on the integration of Taguchi method and genetic algorithm(GA)was employed to estimate the optimal cut point temperature for various sets of process variables.Optimised datasets were utilised to develop an artificial neural networks(ANN)model for the prediction of optimum values of cut points.The ANN model was then used to replace the hybrid framework of the Taguchi method and the GA.The integration of the ANN and FP model makes it a grey-box(GB)model.For the case of Zamama crude,the GB model helped in the decrease of up to 38.93%in energy required per kilo barrel of diesel and an 8.2%increase in diesel production compared to the stand-alone FP model under uncertainty.Similarly,for Kunnar crude,up to 18.87%decrease in energy required per kilo barrel of diesel and a 33.96%increase in diesel production was observed in comparison to the stand-alone FP model.
基金Project supported by the China Special Fund for Meteorological Research in the Public Interest(No.GYHY201306045)the National Natural Science Foundation of China(Nos.41305066 and41575096)
文摘The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature （Tb） sim- ulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging （BMA） ensemble approach. The simulations by the community microwave emission model （CMEM） cou- pled with the community land model version 4.5 （CLM4.5） over China's Mainland are con- ducted by the 24 configurations from four vegetation opacity parameterizations （VOPs）, three soil dielectric constant parameterizations （SDCPs）, and two soil roughness param- eterizations （SRPs）. Compared with the simple arithmetical averaging （SAA） method, the BMA reconstructions have a higher spatial correlation coefficient （larger than 0.99） than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system （AMSR-E） at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference （RMSD） of 4 K and a temporal correlation coefficient of 0.64.
文摘In developing countries,solar energy is the largest source of energy,accounting for 35%–45%of the total energy supply.This energy resource plays a vital role in meeting the energy needs of the world,especially in Vietnam.Vietnam has favorable natural conditions for this energy production.Because it is hot and humid,and it has much rainfall and fertile soil,biomass develops very quickly.Therefore,byproducts from agriculture and forestry are abundant and continuously increasing.However,byproducts that are considered natural waste have become the cause of environmental pollution;these include burning forests,straw,and sawdust in the North;and rice husks dumped into rivers and canals in the Mekong Delta region.Biomass energy is provided in a short cycle,is environmentally safe to use and is encouraged by organizations that support sustainable development.Taking advantage of this energy source provides energy for economic development and ensures environmental protection.Due to the abovementioned favorable conditions,many biomass energy plants are being built in Vietnam.Like other renewable energy investment projects,the selection of the construction contractor,the selection of equipment for the installation of the power plant,and the choice of construction site are complex multi-criteria decisions.In this case,decisionmakers must evaluate many qualitative and quantitative factors.These factors interact with each other and it is difficult to use personal experience to choose the optimal solution for such complex decision-making problems,especially in a fuzzy decision-making environment.Therefore,in this study,the authors use a Multi-Criteria Decision-Making(MCDM)model that uses a Fuzzy Analytic Hierarchy Process(FAHP)model and the Combined Compromise Solution(CoCoSo)algorithm to select biomass furnace suppliers utilizing both qualitative and quantitative factors.Furthermore,the results of this work will provide the first look at a hybrid CoCoSo/FAHP method that decision-makers in other fields can use to find the best supplier.
基金This work was supported by the National Research Foundation of Korea under Grant 2019R1A2C1085388.
文摘Industry 4.0 refers to the fourth evolution of technology development,which strives to connect people to various industries in terms of achieving their expected outcomes efficiently.However,resource management in an Industry 4.0 network is very complex and challenging.To manage and provide suitable resources to each service,we propose a FogQSYM(Fog—Queuing system)model;it is an analytical model for Fog Applications that helps divide the application into several layers,then enables the sharing of the resources in an effective way according to the availability of memory,bandwidth,and network services.It follows theMarkovian queuing model that helps identify the service rates of the devices,the availability of the system,and the number of jobs in the Industry 4.0 systems,which helps applications process data with a reasonable response time.An experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application,which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response time.After implementing the proposed model with different sizes of fog services in Industry 4.0 applications,FogQSYM provides a lower response time than the existing optimized response time model.It should also be noted that the average response time increases when the arrival rate increases.
文摘本文利用普林斯顿大学全球大气强迫场资料，驱动公用陆面过程模式（Community Land Model version 4．0，CLM4．O）模拟了中国区域1961-2010年土壤湿度的时空变化。将模拟结果与观测结果、美国国家环境预报中心再分析数据（Naional Centers for Environmental Prediction Reanalysis，NCEP）和高级微波扫描辐射计（Advanced Microwave Scanning Radiometer-EOS，AMSR-E）反演的土壤湿度进行了对比分析，结果表明CLM4．0模拟结果可以反映出中国区域观测土壤湿度的空间分布和时空变化特征，但东北、江淮和河套三个地区模拟值相对于观测值在各层次均系统性偏大。模拟与NCEP再分析土壤湿度的空间分布基本一致，与AMSR-E的反演值在35°N以北的分布也基本一致；从1961-2010年土壤湿度模拟结果分析得出，各层土壤湿度空间分布从西北向东南增加。低值区主要分布在新疆、青海、甘肃和内蒙古西部地区。东北平原、江淮地区和长江流域为高值区。土壤湿度数值总体上从浅层向深层增加。不同深度土壤湿度变化趋势基本相同。除新疆西部和东北部分地区外，土壤湿度在35°N以北以减少趋势为主，30°N以南的长江流域、华南及西南地区以增加为主。在全球气候变暖的背景下，CLM4．0模拟的夏季土壤湿度在不同程度上响应了降水的变化。中国典型干旱区和半干旱区土壤湿度减小，湿润区增加。其中湿润区土壤湿度对降水的响应最为显著，其次是半干旱区和干旱区。
文摘利用第二次全国土壤调查土壤质地数据（SNSS）和中国区域陆地覆盖资料（CLCV）将陆面过程模式CLM3.5（Community Land Model version 3.5）中基于联合国粮食农业组织发展的土壤质地数据（FAO）和MODIS卫星反演的陆地覆盖数据（MODIS）进行了替换,使用中国气象局陆面数据同化系统（CMA Land Data Assimilation System,CLDAS）大气强迫场资料,分别驱动基于同时改进土壤质地和陆地覆盖数据的CLM3.5（CLM-new）、基于只改进陆地覆盖数据的CLM3.5（CLM-clcv）、基于只改进土壤质地数据的CLM3.5（CLM-snss）和基于原始下垫面数据的CLM3.5（CLM-ctl）,对内蒙古地区2011~2013年土壤湿度的时空变化进行模拟试验,研究下垫面改进对CLM3.5模拟土壤湿度的影响。将四组模拟结果与46个土壤水分站点观测数据进行对比分析,结果表明：相对于控制试验,CLM-clcv、CLM-snss和CLM-new都能不同程度地改进土壤湿度模拟,其中CLM-clcv主要在呼伦贝尔改进明显,CLM-snss则在除呼伦贝尔以外的大部地区改进显著,CLM-ctl模拟的土壤湿度在各层上均系统性偏大,而CLM-new模拟土壤湿度最好地反映出内蒙古地区观测的土壤湿度的时空变化特征,显著改善了土壤湿度的模拟,体现在与观测值有着更高的相关系数和更小的平均偏差与均方根误差。
文摘陆面模式CLM(Community Land Model)是目前国际上发展较为完善并被广泛应用的陆面过程模式。本文使用中国科学院寒区旱区环境与工程研究所位于青藏高原东部的若尔盖高原湿地生态系统研究站的观测资料,对CLM3.0版本及CLM4.0版本在上述地区的模拟性能进行了检验与对比。通过比较观测值与模拟值,验证了模式在高原季节性冻土地区的适用性,发现CLM4.0较CLM3.0在模拟结果上有了一定提高。CLM4.0加入了未冻水参数化方案,使模式可以模拟到冬季土壤冻结后存留的未冻水,显著增加了冻融期间土壤含水量的模拟,同时减小了土壤含冰量的模拟值。并因此增大了模拟的冻土热容量,减小了热导率,使冻融期间土壤温度的模拟也有了一定改善。但是模拟中也发现对于较深层土壤,温度模拟值在冻融期间较观测显著偏低。另外,在消融(冻结)过程阶段CLM4.0模拟的土壤含水量骤增(骤降)的时间均较观测提前。消融过程、冻结过程阶段模拟时间偏短,而完全冻结、完全消融阶段模拟时间偏长。因此CLM对于高原冻土地区的模拟仍是其需要重点改进的地方之一。
文摘利用NOAH(The Community Noah Land Surface Model)、SHAW(Simultaneous Heat and Water)和CLM(Community Land Model)3个不同的陆面过程模式及兰州大学(Semi-Arid Climate Observatory and Laboratory,SACOL)2007年的观测资料,对黄土高原半干旱区的陆面过程进行了模拟研究。通过与观测值间的对比,考察不同陆面过程模式在半干旱区的适用性。研究结果表明:3个模式在半干旱区的模拟性能有较大差异。其中,CLM模式模拟的20 cm以上的浅层土壤温度最优,SHAW模式模拟的深层土壤温度最优;SHAW模式模拟的土壤含水量与观测值最为接近,而NOAH和CLM模式模拟值有较大偏差;3个模式均能较好地模拟地表反射辐射,其中SHAW模式模拟值与观测值的偏差最小;对地表长波辐射的模拟,CLM模式的模拟最优;3个模式均能较好地反映感热、潜热通量的变化趋势,其中CLM模式对感热的模拟性能优于其他两个模式,在有降水发生后的湿润条件下,CLM模式对潜热的模拟性能最优,而无降水的干燥条件下,CLM模式的模拟偏差最大,NOAH模式对冬季潜热的模拟最优。总体而言,CLM模式能够更好地再现半干旱区地气之间的相互作用,但模式对土壤含水量及干燥条件下的潜热通量的模拟较差,模式对半干旱区陆气间的水文过程还有待进一步的研究和改进。
文摘利用CLM(Common Land Model)模式对我国内蒙古奈曼旗农牧交错带沙漠和农田两种不同典型下垫面的陆面过程进行了数值模拟试验,并与外场试验观测结果进行了对比分析。结果表明:无论是沙漠还是农田试验,CLM都能够较好地模拟其辐射通量和土壤中的热传导特征,CLM的模拟结果能够真实地再现试验期间土壤热传导过程对天气过程的响应。相比而言,模式对沙漠地区长波辐射通量和干燥时期短波辐射通量的模拟结果好于农田,其原因可能是因为农田下垫面植被及土壤特征较沙漠复杂,有着很大的不确定性,造成了农田地表反照率和温度模拟的偏差。而对农田热传导的模拟结果好于沙漠,反映了CLM对含水量较大、持水力较强的农田下垫面的热传导模拟能力较好,而对含水量较小、持水力较弱的沙漠下垫面的热传导模拟能力相对较差。
