定位是无线传感器网络技术和应用的重要基础.基于接收信号强度(received signal strength,简称RSS)的定位方法是实际应用中比较重要的定位方法.考虑到实际应用中不同地点RSS测量信号的方差有所不同这一特点,运用最大概率似然理论,建立...定位是无线传感器网络技术和应用的重要基础.基于接收信号强度(received signal strength,简称RSS)的定位方法是实际应用中比较重要的定位方法.考虑到实际应用中不同地点RSS测量信号的方差有所不同这一特点,运用最大概率似然理论,建立了更加符合实际的基于RSS测量的概率定位模型.对于模型中目标表达式高度非线性不好求解的特点,运用进化计算理论设计出符合传感器通信特征的定位算法(location in probability maximum with evolutionary algorithm,简称PMEA)求解概率可能性最大的位置坐标点,并用随机过程在数学上证明了算法的收敛性.最后,通过对实际公开数据集的实验,证实所提出的概率模型和PMEA算法确实能够提高RSS测距定位的精度.展开更多
We normalize data from 43 Chinese observatories and select data from ten Chinese observatories with most continuous records to assess the secular variations(SVs)and geomagnetic jerks by calculating the deviations betw...We normalize data from 43 Chinese observatories and select data from ten Chinese observatories with most continuous records to assess the secular variations(SVs)and geomagnetic jerks by calculating the deviations between annual observed and CHAOS-6 model monthly means.The variations in the north,east,and vertical eigendirections are studied by using the covariance matrix of the residuals,and we find that the vertical direction is strongly affected by magnetospheric ring currents.To obtain noise-free data,we rely on the covariance matrix of the residuals to remove the noise contributions from the largest eigenvalue or vectors owing to ring currents.Finally,we compare the data from the ten Chinese observatories to seven European observatories.Clearly,the covariance matrix method can simulate the SVs of Dst,the jerk of the northward component in 2014 and that of the eastward component in 2003.5 in China are highly agree with that of Vertically downward component in Europe,compare to CHAOS-6,covariance matrix method can show more details of SVs.展开更多
Background error covariance(BEC)plays an essential role in variational data assimilation.Most variational data assimilation systems still use static BEC.Actually,the characteristics of BEC vary with season,day,and eve...Background error covariance(BEC)plays an essential role in variational data assimilation.Most variational data assimilation systems still use static BEC.Actually,the characteristics of BEC vary with season,day,and even hour of the background.National Meteorological Center-based diurnally varying BECs had been proposed,but the diurnal variation characteristics were gained by climatic samples.Ensemble methods can obtain the background error characteristics that suit the samples in the current moment.Therefore,to gain more reasonable diurnally varying BECs,in this study,ensemble-based diurnally varying BECs are generated and the diurnal variation characteristics are discussed.Their impacts are then evaluated by cycling data assimilation and forecasting experiments for a week based on the operational China Meteorological Administration-Beijing system.Clear diurnal variation in the standard deviation of ensemble forecasts and ensemble-based BECs can be identified,consistent with the diurnal variation characteristics of the atmosphere.The results of one-week cycling data assimilation and forecasting show that the application of diurnally varying BECs reduces the RMSEs in the analysis and 6-h forecast.Detailed analysis of a convective rainfall case shows that the distribution of the accumulated precipitation forecast using the diurnally varying BECs is closer to the observation than using the static BEC.Besides,the cycle-averaged precipitation scores in all magnitudes are improved,especially for the heavy precipitation,indicating the potential of using diurnally varying BEC in operational applications.展开更多
文摘定位是无线传感器网络技术和应用的重要基础.基于接收信号强度(received signal strength,简称RSS)的定位方法是实际应用中比较重要的定位方法.考虑到实际应用中不同地点RSS测量信号的方差有所不同这一特点,运用最大概率似然理论,建立了更加符合实际的基于RSS测量的概率定位模型.对于模型中目标表达式高度非线性不好求解的特点,运用进化计算理论设计出符合传感器通信特征的定位算法(location in probability maximum with evolutionary algorithm,简称PMEA)求解概率可能性最大的位置坐标点,并用随机过程在数学上证明了算法的收敛性.最后,通过对实际公开数据集的实验,证实所提出的概率模型和PMEA算法确实能够提高RSS测距定位的精度.
基金supported by the National Natural Science Foundation of China(Grant No.41404053)Special Project for Meteo-Scientifi c Research in the Public Interest(No.GYHY201306073)
文摘We normalize data from 43 Chinese observatories and select data from ten Chinese observatories with most continuous records to assess the secular variations(SVs)and geomagnetic jerks by calculating the deviations between annual observed and CHAOS-6 model monthly means.The variations in the north,east,and vertical eigendirections are studied by using the covariance matrix of the residuals,and we find that the vertical direction is strongly affected by magnetospheric ring currents.To obtain noise-free data,we rely on the covariance matrix of the residuals to remove the noise contributions from the largest eigenvalue or vectors owing to ring currents.Finally,we compare the data from the ten Chinese observatories to seven European observatories.Clearly,the covariance matrix method can simulate the SVs of Dst,the jerk of the northward component in 2014 and that of the eastward component in 2003.5 in China are highly agree with that of Vertically downward component in Europe,compare to CHAOS-6,covariance matrix method can show more details of SVs.
基金This work was jointly sponsored by the National Natural Science Foundation of China[grant number 42075148]the Outreach Projects of the State Key Laboratory of Severe Weather[grant number 2021LASWA08]+1 种基金the Outreach Projects of the Key Laboratory of Meteorological Disaster[grant number KLME202209]supported by the High-Performance Computing Center of Nanjing University of Information Science and Technology(NUIST).
文摘Background error covariance(BEC)plays an essential role in variational data assimilation.Most variational data assimilation systems still use static BEC.Actually,the characteristics of BEC vary with season,day,and even hour of the background.National Meteorological Center-based diurnally varying BECs had been proposed,but the diurnal variation characteristics were gained by climatic samples.Ensemble methods can obtain the background error characteristics that suit the samples in the current moment.Therefore,to gain more reasonable diurnally varying BECs,in this study,ensemble-based diurnally varying BECs are generated and the diurnal variation characteristics are discussed.Their impacts are then evaluated by cycling data assimilation and forecasting experiments for a week based on the operational China Meteorological Administration-Beijing system.Clear diurnal variation in the standard deviation of ensemble forecasts and ensemble-based BECs can be identified,consistent with the diurnal variation characteristics of the atmosphere.The results of one-week cycling data assimilation and forecasting show that the application of diurnally varying BECs reduces the RMSEs in the analysis and 6-h forecast.Detailed analysis of a convective rainfall case shows that the distribution of the accumulated precipitation forecast using the diurnally varying BECs is closer to the observation than using the static BEC.Besides,the cycle-averaged precipitation scores in all magnitudes are improved,especially for the heavy precipitation,indicating the potential of using diurnally varying BEC in operational applications.
