Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful ...Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.展开更多
By dint of V-3θ diagram from the Blown-up theory,a continuous heavy rain process in western Sichuan basin from July 14 to 17,2009 is analyzed in this paper.Situation field and precipitation of ECWMF and T213 are veri...By dint of V-3θ diagram from the Blown-up theory,a continuous heavy rain process in western Sichuan basin from July 14 to 17,2009 is analyzed in this paper.Situation field and precipitation of ECWMF and T213 are verified and discussed.Results show that V-3θ diagram can describe the heavy rain process accurately.Combining with additional conventional weather charts,experience and numerical forecast products,the heavy rain falling area is determined.The forecast accuracy of situation field of EC is significantly higher than that of T213 and the forecast accuracy of T213 for heavy rain forecast is relatively low.展开更多
In this study, we examine the impacts that EVs （electric vehicles） have on vehicle usage patterns and environmental improvements, using our integrated travel demand forecasting model, which can simulate an individua...In this study, we examine the impacts that EVs （electric vehicles） have on vehicle usage patterns and environmental improvements, using our integrated travel demand forecasting model, which can simulate an individual activity-travel behavior in each time period, as well as consider an induced demand by decreasing travel cost. In order to examine the effects that charging/discharging have on the demand in electricity, we analyze scenarios based on the simulation results of the EVs＇ parking location, parking duration and the battery state of charge. From the simulation, result under the ownership rate of EVs in the Nagoya metropolitan area in 2020 is about 6%, which turns out that the total CO2 emissions have decreased by 4% although the situation of urban transport is not changed. After calculating the electricity demand in each zone using architectural area and basic units of hourly power consumption, we evaluate the effect to decrease the peak load by V2G （vehicle-to-grid）. According to the results, if EV drivers charge at home during the night and discharge at work during the day, the electricity demand in Nagoya city increases by approximately 1%, although changes in each individual zone range from -7% to ＋8%, depending on its characteristics.展开更多
The Yunnan–Guizhou quasi-stationary front(YGQSF)is a critical weather system in Southwest China during the cold season(November to April),frequently introducing low-temperature disasters.Accurate forecasting of YGQSF...The Yunnan–Guizhou quasi-stationary front(YGQSF)is a critical weather system in Southwest China during the cold season(November to April),frequently introducing low-temperature disasters.Accurate forecasting of YGQSF poses significant challenges for operational models.This study evaluates the performance of ECMWF Integrated Forecasting System(IFS)in forecasting YGQSFs by utilizing hourly observational data and a linear fitting method for frontal line identification.From 2020 to 2023,a total of 392 and 261 YGQSFs were identified in observational data and the IFS forecast,respectively.From east to west,the frontal lines gradually change from northwest–southeast-oriented to quasi-north-south-oriented.The northern segments of the frontal lines are concentrated east of the Hengduan Mountains,while the southern segments are dispersed across four high-occurrence regions on the Yungui Plateau.These high-occurrence regions are located on local highlands or their eastern slopes,highlighting the influence of topographic blocking effects.These spatial characteristics of the YGQSFs are well captured by the IFS,especially for the high-occurrence region between 103.5°E and 105°E,which is related to the pronounced spatial temperature gradients enhanced by the large elevation difference.Further analysis focuses on the impact of spatial temperature gradients on the YGQSF forecasting,particularly examining Hits,Misses,and False alarms.The model demonstrates superior performance in forecasting YGQSFs characterized by large temperature gradients and substantial cold air accumulation.Conversely,reduced temperature gradients,stemming from weaker cold air to the east or weaker warm air to the west,increase the risk of Misses.Temporally,the missed YGQSFs are uniformly distributed,while spatially they are scattered.False alarms,however,peak between February and March and are concentrated between 103.5°E and 107.25°E.Overall,forecasting YGQSFs with weak intensity,short frontal lines,or meridional orientation remains particularly challenging.The biases revealed in this study provide valuable insights for enhancing operational forecasting accuracy.展开更多
基金financially supported by the Health and Family Planning Commission of Hubei Province(No.WJ2017F047)the Health and Family Planning Commission of Wuhan(No.WG17D05)
文摘Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.
基金Supported by Civil Aviation Flight University of China Natural Science Fund Program(J2008-66)~~
文摘By dint of V-3θ diagram from the Blown-up theory,a continuous heavy rain process in western Sichuan basin from July 14 to 17,2009 is analyzed in this paper.Situation field and precipitation of ECWMF and T213 are verified and discussed.Results show that V-3θ diagram can describe the heavy rain process accurately.Combining with additional conventional weather charts,experience and numerical forecast products,the heavy rain falling area is determined.The forecast accuracy of situation field of EC is significantly higher than that of T213 and the forecast accuracy of T213 for heavy rain forecast is relatively low.
文摘In this study, we examine the impacts that EVs （electric vehicles） have on vehicle usage patterns and environmental improvements, using our integrated travel demand forecasting model, which can simulate an individual activity-travel behavior in each time period, as well as consider an induced demand by decreasing travel cost. In order to examine the effects that charging/discharging have on the demand in electricity, we analyze scenarios based on the simulation results of the EVs＇ parking location, parking duration and the battery state of charge. From the simulation, result under the ownership rate of EVs in the Nagoya metropolitan area in 2020 is about 6%, which turns out that the total CO2 emissions have decreased by 4% although the situation of urban transport is not changed. After calculating the electricity demand in each zone using architectural area and basic units of hourly power consumption, we evaluate the effect to decrease the peak load by V2G （vehicle-to-grid）. According to the results, if EV drivers charge at home during the night and discharge at work during the day, the electricity demand in Nagoya city increases by approximately 1%, although changes in each individual zone range from -7% to ＋8%, depending on its characteristics.
基金Supported by the National Natural Science Foundation of China(42225505 and U2142204)S&T Development Fund of Chinese Academy of Meteorological Sciences(2023KJ033 and 2022KJ007)+1 种基金Jiangsu Collaborative Innovation Center for Climate ChangeKey Research and Development Program of Yunnan Province(202403AC100040)。
文摘The Yunnan–Guizhou quasi-stationary front(YGQSF)is a critical weather system in Southwest China during the cold season(November to April),frequently introducing low-temperature disasters.Accurate forecasting of YGQSF poses significant challenges for operational models.This study evaluates the performance of ECMWF Integrated Forecasting System(IFS)in forecasting YGQSFs by utilizing hourly observational data and a linear fitting method for frontal line identification.From 2020 to 2023,a total of 392 and 261 YGQSFs were identified in observational data and the IFS forecast,respectively.From east to west,the frontal lines gradually change from northwest–southeast-oriented to quasi-north-south-oriented.The northern segments of the frontal lines are concentrated east of the Hengduan Mountains,while the southern segments are dispersed across four high-occurrence regions on the Yungui Plateau.These high-occurrence regions are located on local highlands or their eastern slopes,highlighting the influence of topographic blocking effects.These spatial characteristics of the YGQSFs are well captured by the IFS,especially for the high-occurrence region between 103.5°E and 105°E,which is related to the pronounced spatial temperature gradients enhanced by the large elevation difference.Further analysis focuses on the impact of spatial temperature gradients on the YGQSF forecasting,particularly examining Hits,Misses,and False alarms.The model demonstrates superior performance in forecasting YGQSFs characterized by large temperature gradients and substantial cold air accumulation.Conversely,reduced temperature gradients,stemming from weaker cold air to the east or weaker warm air to the west,increase the risk of Misses.Temporally,the missed YGQSFs are uniformly distributed,while spatially they are scattered.False alarms,however,peak between February and March and are concentrated between 103.5°E and 107.25°E.Overall,forecasting YGQSFs with weak intensity,short frontal lines,or meridional orientation remains particularly challenging.The biases revealed in this study provide valuable insights for enhancing operational forecasting accuracy.
