为解决无法获取先验分布模式的“贫信息、小样本”航线随机客流量预测问题,提取这类航线客流量时间序列的上、下界信息,并在中间增加一个偏好值,形成包含左界点、中间点和右界点的三元区间数结构的航线客流量表达形式,将三元区间数数据...为解决无法获取先验分布模式的“贫信息、小样本”航线随机客流量预测问题,提取这类航线客流量时间序列的上、下界信息,并在中间增加一个偏好值,形成包含左界点、中间点和右界点的三元区间数结构的航线客流量表达形式,将三元区间数数据结构转换为左半径、中心及右半径3个独立的时间序列,再利用灰色系统理论建立航线客流量预测模型,并利用周期外延模型对上述模型得出的残差序列进行修正。采用2004—2019年民航客运量数据进行验证分析。结果发现,ARIMA(autoregressive integrated moving average model)模型预测检验的平均绝对百分比误差为6.77%,灰色周期外延模型的平均绝对百分比误差为1.66%,因此后者在短期预测上有较大优势。展开更多
In this paper,time extension methods,originally designed for clear-sky land surface conditions,are used to estimate high-spatial resolution surface daily longwave(LW)radiation from the instantaneous Global LAnd Surfac...In this paper,time extension methods,originally designed for clear-sky land surface conditions,are used to estimate high-spatial resolution surface daily longwave(LW)radiation from the instantaneous Global LAnd Surface Satellite(GLASS)longwave radiation product.The performance of four time methods were first tested by using ground based flux measurements that were collected from 141 global sites.Combined with the accuracy of daily LW radiation estimated from the instantaneous GLASS LW radiation,the linear sine interpolation method performs better than the other methods and was employed to estimate the daily LW radiation as follows:The bias/Root Mean Square Error(RMSE)of the linear sine interpolation method were−6.30/15.10 W/m^(2)for the daily longwave upward radiation(LWUP),−1.65/27.63 W/m2 for the daily longwave downward radiation(LWDN),and 4.69/26.42 W/m^(2)for the daily net longwave radiation(LWNR).We found that the lengths of the diurnal cycle of LW radiation are longer than the durations between sunrise and sunset and we proposed increasing the day length by 1.5 h.The accuracies of daily LW radiation were improved after adjusting the day length.The bias/RMSE were−4.15/13.74 W/m2 for the daily LWUP,−1.3/27.52 W/m^(2)for the daily LWDN,and 2.85/25.91 W/m^(2)for the daily LWNR.We are producing long-term surface daily LW radiation values from the GLASS LW radiation product.展开更多
文摘为解决无法获取先验分布模式的“贫信息、小样本”航线随机客流量预测问题,提取这类航线客流量时间序列的上、下界信息,并在中间增加一个偏好值,形成包含左界点、中间点和右界点的三元区间数结构的航线客流量表达形式,将三元区间数数据结构转换为左半径、中心及右半径3个独立的时间序列,再利用灰色系统理论建立航线客流量预测模型,并利用周期外延模型对上述模型得出的残差序列进行修正。采用2004—2019年民航客运量数据进行验证分析。结果发现,ARIMA(autoregressive integrated moving average model)模型预测检验的平均绝对百分比误差为6.77%,灰色周期外延模型的平均绝对百分比误差为1.66%,因此后者在短期预测上有较大优势。
基金supported by the National Key Research and Development Program of China under Grant 2016YFA0600101National Natural Science Foundation of China via grants 42090011,41771365 and 42071308the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)under Grant 2019QZKK0206.
文摘In this paper,time extension methods,originally designed for clear-sky land surface conditions,are used to estimate high-spatial resolution surface daily longwave(LW)radiation from the instantaneous Global LAnd Surface Satellite(GLASS)longwave radiation product.The performance of four time methods were first tested by using ground based flux measurements that were collected from 141 global sites.Combined with the accuracy of daily LW radiation estimated from the instantaneous GLASS LW radiation,the linear sine interpolation method performs better than the other methods and was employed to estimate the daily LW radiation as follows:The bias/Root Mean Square Error(RMSE)of the linear sine interpolation method were−6.30/15.10 W/m^(2)for the daily longwave upward radiation(LWUP),−1.65/27.63 W/m2 for the daily longwave downward radiation(LWDN),and 4.69/26.42 W/m^(2)for the daily net longwave radiation(LWNR).We found that the lengths of the diurnal cycle of LW radiation are longer than the durations between sunrise and sunset and we proposed increasing the day length by 1.5 h.The accuracies of daily LW radiation were improved after adjusting the day length.The bias/RMSE were−4.15/13.74 W/m2 for the daily LWUP,−1.3/27.52 W/m^(2)for the daily LWDN,and 2.85/25.91 W/m^(2)for the daily LWNR.We are producing long-term surface daily LW radiation values from the GLASS LW radiation product.
