Based on the extreme forecast index (EFI) of ECMWF, the “observed” EFI (OEFI) of observation is defined and the EFI is calibrated. Then the EFI equivalent percentile (EFIEP) and EFI equivalent quantile (EFIEQ) are d...Based on the extreme forecast index (EFI) of ECMWF, the “observed” EFI (OEFI) of observation is defined and the EFI is calibrated. Then the EFI equivalent percentile (EFIEP) and EFI equivalent quantile (EFIEQ) are designed to forecast the daily extreme precipitation quantitatively. The formulation indicates that the EFIEP is correlated not only to the EFI but also to the proportion of no precipitation. This characteristic is prominent as two areas with nearly same EFIs but different proportions of no precipitation. Cases study shows that the EFIEP can forecast reliable percentile of daily precipitation and 100% percentiles are forecasted for over max extreme events. The EFIEQ is a considerable tool for quantitative precipitation forecast (QPF). Compared to the probabilistic forecast of ensemble prediction system (EPS), it is quantitative and synthesizes the advantage of extreme precipitation location forecast of EPS. Using the observations of 2311 stations of China in 2016 to verify the EFIEP and EFIEQ, the results show that the forecast biases are around 1. The threat scores (TS) for 20 years return period events are about 0.21 and 0.07 for 36 and 180 hours lead times respectively. The equivalent threat scores (ETS) are all larger than 0 and nearly equal to the TS. The TS for heavy rainfall are 0.23 and 0.07 for 36 and 180 lead times respectively. The scores are better than those of high resolution deterministic model (HRDet) and show significant forecast skills for quantitative forecast of extreme daily precipitation.展开更多
Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a mo...Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.展开更多
Based on the domestic and foreign related research methods, the life meteorological index forecasting system of Wuhu City was compiled using database and network as well as computer language. The system realized the a...Based on the domestic and foreign related research methods, the life meteorological index forecasting system of Wuhu City was compiled using database and network as well as computer language. The system realized the automation process for the generation of life index forecasting products from local situation of Wuhu City and forecasting data, which could get the latest service products dispensing with manual intervention. The development of the system not only made the operation process of the life meteorological index of Wuhu City more time-saving and efficient, but also made the results more scientific and rigorous.展开更多
Use of artificial neural networks has become a significant and an emerging research method due to its capability of capturing nonlinear behavior instead of conventional time series methods. Among them, feed forward ba...Use of artificial neural networks has become a significant and an emerging research method due to its capability of capturing nonlinear behavior instead of conventional time series methods. Among them, feed forward back propagation neural network (BPNN) is the widely used network topology for forecasting stock prices indices. In this study, we attempted to find the best network topology for one step ahead forecasting of All Share Price Index (ASPI), Colombo Stock Exchange (CSE) by employing feed forward BPNN. The daily data including ASPI, All Share Total Return Index (ASTRI), Market Price Earnings Ratio (PER), and Market Price to Book Value (PBV) were collected from CSE over the period from January 2nd 2012 to March 20th 2014. The experiment is implemented by prioritizing the number of inputs, learning rate, number of hidden layer neurons, and the number of training sessions. Eight models were selected on basis of input data and the number of training sessions. Then the best model was used for forecasting next trading day ASPI value. Empirical result reveals that the proposed model can be used as an approximation method to obtain next day value. In addition, it showed that the number of inputs, number of hidden layer neurons and the training times are significant factors that can be affected to the accuracy of forecast value.展开更多
研究山西极端暴雨发生规律对开展预报预警、灾害防御具有重要意义。本文利用常规观测资料和欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)第五代大气再分析资料(ERA5),采用标准化距平作为异常度,运...研究山西极端暴雨发生规律对开展预报预警、灾害防御具有重要意义。本文利用常规观测资料和欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)第五代大气再分析资料(ERA5),采用标准化距平作为异常度,运用环流分析和物理量诊断等方法,研究1981—2018年6—9月山西17次极端暴雨的气候特征、环流影响系统和水汽异常特征。结果表明:山西极端暴雨主要出现在7—8月,暴雨区主要位于中南部,2010年以来极端暴雨明显多发;影响系统主要是700 h Pa低涡和台风系统,有偏南和偏东两支水汽通道。极端暴雨过程中,低层水汽含量明显偏高,从暴雨区平均比湿的过程最大值看,大部分过程850 h Pa超过14.2 g·kg^(-1),700 h Pa则可超过9.8 g·kg^(-1)、对应暴雨区平均异常度达1.6以上;水汽的极端性在低层水汽通量辐合中心表现突出,17次极端暴雨700、850 h Pa暴雨区水汽通量辐合中心过程最大值的异常度均值分别达-8、-6,其中台风减弱低压影响下的极端暴雨850 h Pa水汽通量辐合中心最大异常度达-12。根据以上环流和水汽特征建立极端暴雨概念模型,并给出极端暴雨低层水汽含量和水汽通量辐合强度预报参考指标。展开更多
By using the fog data from 1995 to 2004 of four selected observation stations,the weather features of foggy days in Liaoxi area have been studied in this paper.The favorable surface and upper circulation for fog and i...By using the fog data from 1995 to 2004 of four selected observation stations,the weather features of foggy days in Liaoxi area have been studied in this paper.The favorable surface and upper circulation for fog and its frequency have also been concluded from the statistic.In this paper,the forecasting index of fog,proposed on the basis of the condition and mechanism of the fog occurrence,has been tested by the 10-year analysis.Another test conducted by using the data of 1st July-31st December,2004 also gives a good result which has a vacancy rate of 22.2% and a miss rate of 5.1%.展开更多
F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM...F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.展开更多
文摘Based on the extreme forecast index (EFI) of ECMWF, the “observed” EFI (OEFI) of observation is defined and the EFI is calibrated. Then the EFI equivalent percentile (EFIEP) and EFI equivalent quantile (EFIEQ) are designed to forecast the daily extreme precipitation quantitatively. The formulation indicates that the EFIEP is correlated not only to the EFI but also to the proportion of no precipitation. This characteristic is prominent as two areas with nearly same EFIs but different proportions of no precipitation. Cases study shows that the EFIEP can forecast reliable percentile of daily precipitation and 100% percentiles are forecasted for over max extreme events. The EFIEQ is a considerable tool for quantitative precipitation forecast (QPF). Compared to the probabilistic forecast of ensemble prediction system (EPS), it is quantitative and synthesizes the advantage of extreme precipitation location forecast of EPS. Using the observations of 2311 stations of China in 2016 to verify the EFIEP and EFIEQ, the results show that the forecast biases are around 1. The threat scores (TS) for 20 years return period events are about 0.21 and 0.07 for 36 and 180 hours lead times respectively. The equivalent threat scores (ETS) are all larger than 0 and nearly equal to the TS. The TS for heavy rainfall are 0.23 and 0.07 for 36 and 180 lead times respectively. The scores are better than those of high resolution deterministic model (HRDet) and show significant forecast skills for quantitative forecast of extreme daily precipitation.
基金support from National Natural Science Foundation of China(Nos.71774051,72243003)National Social Science Fund of China(No.22AZD128)the seminar participants in Center for Resource and Environmental Management,Hunan University,China.
文摘Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.
文摘Based on the domestic and foreign related research methods, the life meteorological index forecasting system of Wuhu City was compiled using database and network as well as computer language. The system realized the automation process for the generation of life index forecasting products from local situation of Wuhu City and forecasting data, which could get the latest service products dispensing with manual intervention. The development of the system not only made the operation process of the life meteorological index of Wuhu City more time-saving and efficient, but also made the results more scientific and rigorous.
文摘Use of artificial neural networks has become a significant and an emerging research method due to its capability of capturing nonlinear behavior instead of conventional time series methods. Among them, feed forward back propagation neural network (BPNN) is the widely used network topology for forecasting stock prices indices. In this study, we attempted to find the best network topology for one step ahead forecasting of All Share Price Index (ASPI), Colombo Stock Exchange (CSE) by employing feed forward BPNN. The daily data including ASPI, All Share Total Return Index (ASTRI), Market Price Earnings Ratio (PER), and Market Price to Book Value (PBV) were collected from CSE over the period from January 2nd 2012 to March 20th 2014. The experiment is implemented by prioritizing the number of inputs, learning rate, number of hidden layer neurons, and the number of training sessions. Eight models were selected on basis of input data and the number of training sessions. Then the best model was used for forecasting next trading day ASPI value. Empirical result reveals that the proposed model can be used as an approximation method to obtain next day value. In addition, it showed that the number of inputs, number of hidden layer neurons and the training times are significant factors that can be affected to the accuracy of forecast value.
文摘研究山西极端暴雨发生规律对开展预报预警、灾害防御具有重要意义。本文利用常规观测资料和欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)第五代大气再分析资料(ERA5),采用标准化距平作为异常度,运用环流分析和物理量诊断等方法,研究1981—2018年6—9月山西17次极端暴雨的气候特征、环流影响系统和水汽异常特征。结果表明:山西极端暴雨主要出现在7—8月,暴雨区主要位于中南部,2010年以来极端暴雨明显多发;影响系统主要是700 h Pa低涡和台风系统,有偏南和偏东两支水汽通道。极端暴雨过程中,低层水汽含量明显偏高,从暴雨区平均比湿的过程最大值看,大部分过程850 h Pa超过14.2 g·kg^(-1),700 h Pa则可超过9.8 g·kg^(-1)、对应暴雨区平均异常度达1.6以上;水汽的极端性在低层水汽通量辐合中心表现突出,17次极端暴雨700、850 h Pa暴雨区水汽通量辐合中心过程最大值的异常度均值分别达-8、-6,其中台风减弱低压影响下的极端暴雨850 h Pa水汽通量辐合中心最大异常度达-12。根据以上环流和水汽特征建立极端暴雨概念模型,并给出极端暴雨低层水汽含量和水汽通量辐合强度预报参考指标。
文摘By using the fog data from 1995 to 2004 of four selected observation stations,the weather features of foggy days in Liaoxi area have been studied in this paper.The favorable surface and upper circulation for fog and its frequency have also been concluded from the statistic.In this paper,the forecasting index of fog,proposed on the basis of the condition and mechanism of the fog occurrence,has been tested by the 10-year analysis.Another test conducted by using the data of 1st July-31st December,2004 also gives a good result which has a vacancy rate of 22.2% and a miss rate of 5.1%.
文摘F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.
