Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have becom...Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.展开更多
Mesoscale convective systems (MCSs) are severe disaster-producing weather systems. Previous attempts of MCS census are made by examining infrared satellite imageries artificially, with subjectivity involved in the pro...Mesoscale convective systems (MCSs) are severe disaster-producing weather systems. Previous attempts of MCS census are made by examining infrared satellite imageries artificially, with subjectivity involved in the process unavoidably. This method is also inefficient and time-consuming. The disadvantages make it impossible to do MCS census over Asia and western Pacific region (AWPR) with an extended span of time, which is not favorable for gaining a deeper insight into these systems. In this paper, a fire-new automatic MCS identification (AMI) method is used to capture four categories of MCSs with different sizes and shapes from numerical satellite infrared data. 47,468 MCSs are identified over Asia and western Pacific region during the warm season (May to October) from 1995 to 2008. Based on this database, MCS characteristics such as shape, size, duration, velocity, geographical distribution, intermonthly variation, and lifecycle are studied. Results indicate that the number of linear MCSs is 2.5 times that of circular MCSs. The former is of a larger size while the latter is of a longer duration. The 500 hPa steering flow plays an important role in the MCS movement. MCSs tend to move faster after they reach the maximum extent. Four categories of MCS have similar characteristics of geographical distribution and intermonthly variation. Basically, MCSs are zonally distributed, with three zones weakening from south to north. The intermonthly variation of MCSs is related to the seasonal adjustment of the large-scale circulation. As to the MCSs over China, they have different lifecycle characteristics over different areas. MCSs over plateaus and hill areas, with only one peak in their lifecycle curves, tend to form in the afternoon, mature at nightfall, and dissipate at night. On the other hand, MCSs over plains, which have several peaks in their lifecycle curves, may form either in the afternoon or at night, whereas MCSs over the oceans tend to form at midnight. Affected by the sea-land breeze circulation, MCSs over coastal areas of Guangdong and Guangxi always come into being at about 1500 or 1600 (local time), while MCSs over the Sichuan Basin, affected by the mountain-valley breeze circulation, generally initiate nocturnally.展开更多
As one of the major methods for the simulation of option pricing,Monte Carlo method assumes random fluctuations in the distribution of asset prices.Under certain uncertainties process,different evolution paths could b...As one of the major methods for the simulation of option pricing,Monte Carlo method assumes random fluctuations in the distribution of asset prices.Under certain uncertainties process,different evolution paths could be simulated so as to finally yield the expectation value of the asset price,which requires a lot of simulations to ensure the accuracy based on huge and expensive calculations.In order to solve the above computational problem,quantum Monte Carlo(QMC)has been established and applied in the relevant systems such as European call options.In this work,both MC and QM methods are adopted to simulate European call options.Based on the preparation of quantum states in QMC algorithm and the construction of quantum circuits by simulating a quantum hardware environment on a traditional computer,the amplitude estimation(AE)algorithm is found to play a secondary role in accelerating the pricing of European options.More importantly,the payoff function and the time required for the simulation in QMC method show some improvements than those in MC method.展开更多
Mesoscale convective systems （MCSs） are severe disaster-producing weather systems. Radar data and infrared satellite image are useful tools in MCS surveillance. The previous method of MCS census is to look through t...Mesoscale convective systems （MCSs） are severe disaster-producing weather systems. Radar data and infrared satellite image are useful tools in MCS surveillance. The previous method of MCS census is to look through the printed infrared imagery manually. This method is not only subjective and inaccurate, but also inefficient. Different from previous studies, a new automatic MCS identification （AMI） method, which overcomes the above disadvantages, is used in the present study. The AMI method takes three steps： searching potential MCS profiles, tracking the MCS, and assessing the MCS, so as to capture MCSs from infrared satellite images. Finally, 47468 MCSs are identified over Asia and the western Pacific region during the warm seasons （May-October） from 1995 to 2008. From this database, the geographical distribution and diurnal variation of MCSs are analyzed. The results show that different types of MCSs have similar geographical distributions. Latitude is the main control factor for MCS distribution. MCSs are most frequent over the central Tibetan Plateau; meanwhile, this area also has the highest hail frequency according to previous studies. ~rther, it is found that the diurnal variation of MCSs has little to do with MCSs＇ size or shape; MCSs in different areas have their own particular diurnal variation patterns. Based on the diurnal variation characteristics, MCSs are classified into four categories： the whole-day occurring MCSs in low latitude, the whole-day occurring MCSs in high latitude, the nocturnal MCSs, and the postmeridian MCSs. MCSs over most places of China's Mainland are postmeridian; but MCSs over the Sichuan basin and its vicinity are nocturnal. This conclusion is coincidental with the hail climatology of China.展开更多
基金Under the auspices of the Natural Science Foundation of China(No.32371875,32001249)。
文摘Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.
基金National Natural Science Founds of China (40875028)Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Mesoscale convective systems (MCSs) are severe disaster-producing weather systems. Previous attempts of MCS census are made by examining infrared satellite imageries artificially, with subjectivity involved in the process unavoidably. This method is also inefficient and time-consuming. The disadvantages make it impossible to do MCS census over Asia and western Pacific region (AWPR) with an extended span of time, which is not favorable for gaining a deeper insight into these systems. In this paper, a fire-new automatic MCS identification (AMI) method is used to capture four categories of MCSs with different sizes and shapes from numerical satellite infrared data. 47,468 MCSs are identified over Asia and western Pacific region during the warm season (May to October) from 1995 to 2008. Based on this database, MCS characteristics such as shape, size, duration, velocity, geographical distribution, intermonthly variation, and lifecycle are studied. Results indicate that the number of linear MCSs is 2.5 times that of circular MCSs. The former is of a larger size while the latter is of a longer duration. The 500 hPa steering flow plays an important role in the MCS movement. MCSs tend to move faster after they reach the maximum extent. Four categories of MCS have similar characteristics of geographical distribution and intermonthly variation. Basically, MCSs are zonally distributed, with three zones weakening from south to north. The intermonthly variation of MCSs is related to the seasonal adjustment of the large-scale circulation. As to the MCSs over China, they have different lifecycle characteristics over different areas. MCSs over plateaus and hill areas, with only one peak in their lifecycle curves, tend to form in the afternoon, mature at nightfall, and dissipate at night. On the other hand, MCSs over plains, which have several peaks in their lifecycle curves, may form either in the afternoon or at night, whereas MCSs over the oceans tend to form at midnight. Affected by the sea-land breeze circulation, MCSs over coastal areas of Guangdong and Guangxi always come into being at about 1500 or 1600 (local time), while MCSs over the Sichuan Basin, affected by the mountain-valley breeze circulation, generally initiate nocturnally.
基金This work was financially supported by the National Natural Science Foundation of China Granted No.11764028。
文摘As one of the major methods for the simulation of option pricing,Monte Carlo method assumes random fluctuations in the distribution of asset prices.Under certain uncertainties process,different evolution paths could be simulated so as to finally yield the expectation value of the asset price,which requires a lot of simulations to ensure the accuracy based on huge and expensive calculations.In order to solve the above computational problem,quantum Monte Carlo(QMC)has been established and applied in the relevant systems such as European call options.In this work,both MC and QM methods are adopted to simulate European call options.Based on the preparation of quantum states in QMC algorithm and the construction of quantum circuits by simulating a quantum hardware environment on a traditional computer,the amplitude estimation(AE)algorithm is found to play a secondary role in accelerating the pricing of European options.More importantly,the payoff function and the time required for the simulation in QMC method show some improvements than those in MC method.
基金Supported by the National(Key)Basic Research and Development(973)Program of China(2013CB430103)National Natural Science Foundation of China(40875028)Forecaster Special Project Funded by the Jiangsu Meteorological Bureau(2012-8)
文摘Mesoscale convective systems （MCSs） are severe disaster-producing weather systems. Radar data and infrared satellite image are useful tools in MCS surveillance. The previous method of MCS census is to look through the printed infrared imagery manually. This method is not only subjective and inaccurate, but also inefficient. Different from previous studies, a new automatic MCS identification （AMI） method, which overcomes the above disadvantages, is used in the present study. The AMI method takes three steps： searching potential MCS profiles, tracking the MCS, and assessing the MCS, so as to capture MCSs from infrared satellite images. Finally, 47468 MCSs are identified over Asia and the western Pacific region during the warm seasons （May-October） from 1995 to 2008. From this database, the geographical distribution and diurnal variation of MCSs are analyzed. The results show that different types of MCSs have similar geographical distributions. Latitude is the main control factor for MCS distribution. MCSs are most frequent over the central Tibetan Plateau; meanwhile, this area also has the highest hail frequency according to previous studies. ~rther, it is found that the diurnal variation of MCSs has little to do with MCSs＇ size or shape; MCSs in different areas have their own particular diurnal variation patterns. Based on the diurnal variation characteristics, MCSs are classified into four categories： the whole-day occurring MCSs in low latitude, the whole-day occurring MCSs in high latitude, the nocturnal MCSs, and the postmeridian MCSs. MCSs over most places of China's Mainland are postmeridian; but MCSs over the Sichuan basin and its vicinity are nocturnal. This conclusion is coincidental with the hail climatology of China.
