Aerosol optical depth(AOD) is the most basic parameter that describes the optical properties of atmospheric aerosols,and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Feng...Aerosol optical depth(AOD) is the most basic parameter that describes the optical properties of atmospheric aerosols,and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3 A(FY-3 A) and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a24-h Weather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3 A/MERSI(Medium-resolution Spectral Imager) AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3 A satellite aerosol products can be effectively applied to numerical models and dust weather analysis.展开更多
With available high-resolution ocean surface wind vectors retrieved from the U.S.Naval Research Laboratory's WindSat on Coriolis,the impact of these data on genesis and forecasting of tropical storm Henri is exami...With available high-resolution ocean surface wind vectors retrieved from the U.S.Naval Research Laboratory's WindSat on Coriolis,the impact of these data on genesis and forecasting of tropical storm Henri is examined using the non-hydrostatic,fifth-generation mesoscale model(MM5) of Pennsylvania State University-National Center for Atmospheric Research plus its newly released three-dimensional variational data assimilation(3DVAR) system.It is shown that the assimilation of the WindSat-retrieved ocean surface wind vectors in the 3DVAR system improves the model initialization fields by introducing a stronger vortex in the lower troposphere.As a result,the model reproduces the storm formation and track reasonably close to the observations.Compared to the experiment without the WindSat surface winds,the WindSat assimilation reduced an error between the model simulated track and observations of more than 80 km and also improved the storm intensity by nearly 2 hPa.It suggests that these data could provide early detection and prediction of tropical storms or hurricanes.展开更多
为了提高流数据聚类效率,文中基于经典流聚类算法Clu Stream的思想和Storm的计算架构,设计了一种分布式实时流聚类算法(distributed real time clustering algorithm for stream data,DRClu Stream)。该算法运用滑动时间窗口机制实现多...为了提高流数据聚类效率,文中基于经典流聚类算法Clu Stream的思想和Storm的计算架构,设计了一种分布式实时流聚类算法(distributed real time clustering algorithm for stream data,DRClu Stream)。该算法运用滑动时间窗口机制实现多粒度的数据存储;将流数据的在线微聚类部分拆分成局部和全局两个部分做分布式计算,第一部分由多个线程并行进行微簇的局部增量更新,第二部分合并微簇的局部增量结果来更新全局微簇。还设计了DRClu Stream算法基于Storm的实现方案,通过使用消息中间件Kafka和合理部署Storm的拓扑对DRClu Stream算法进行实现。性能分析及实验结果表明:DRClu Stream算法的聚类精度与K-Means相近,且随着local节点(local bolt线程)的增加聚类精度保持稳定,而计算效率呈近线性提升。展开更多
基金supported by the National Key Research and Development Program of China (Grant Nos.2017YFC1502100 and 2016YFA0602302)the Natural Science Foundation of Jiangsu Province (Grant Nos.BK20160954 and BK20170940)+3 种基金the Beijige Funding from Jiangsu Research Institute of Meteorological Science (Grant Nos.BJG201510 and BJG201604)the Startup Foundation for Introducing Talent of NUIST (Grant Nos.2016r27,2016r043 and 2017r058)a project for data application of Fengyun3 meteorological satellite [FY-3(02)UDS-1.1.2]the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Aerosol optical depth(AOD) is the most basic parameter that describes the optical properties of atmospheric aerosols,and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3 A(FY-3 A) and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a24-h Weather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3 A/MERSI(Medium-resolution Spectral Imager) AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3 A satellite aerosol products can be effectively applied to numerical models and dust weather analysis.
文摘With available high-resolution ocean surface wind vectors retrieved from the U.S.Naval Research Laboratory's WindSat on Coriolis,the impact of these data on genesis and forecasting of tropical storm Henri is examined using the non-hydrostatic,fifth-generation mesoscale model(MM5) of Pennsylvania State University-National Center for Atmospheric Research plus its newly released three-dimensional variational data assimilation(3DVAR) system.It is shown that the assimilation of the WindSat-retrieved ocean surface wind vectors in the 3DVAR system improves the model initialization fields by introducing a stronger vortex in the lower troposphere.As a result,the model reproduces the storm formation and track reasonably close to the observations.Compared to the experiment without the WindSat surface winds,the WindSat assimilation reduced an error between the model simulated track and observations of more than 80 km and also improved the storm intensity by nearly 2 hPa.It suggests that these data could provide early detection and prediction of tropical storms or hurricanes.
文摘为了提高流数据聚类效率,文中基于经典流聚类算法Clu Stream的思想和Storm的计算架构,设计了一种分布式实时流聚类算法(distributed real time clustering algorithm for stream data,DRClu Stream)。该算法运用滑动时间窗口机制实现多粒度的数据存储;将流数据的在线微聚类部分拆分成局部和全局两个部分做分布式计算,第一部分由多个线程并行进行微簇的局部增量更新,第二部分合并微簇的局部增量结果来更新全局微簇。还设计了DRClu Stream算法基于Storm的实现方案,通过使用消息中间件Kafka和合理部署Storm的拓扑对DRClu Stream算法进行实现。性能分析及实验结果表明:DRClu Stream算法的聚类精度与K-Means相近,且随着local节点(local bolt线程)的增加聚类精度保持稳定,而计算效率呈近线性提升。
