Nearly five decades after the third plenary session of the 11th Central Committee of the Communist Party of China(CPC)in 1978,which launched the historic reform and opening up policy that transformed China,the CPC hel...Nearly five decades after the third plenary session of the 11th Central Committee of the Communist Party of China(CPC)in 1978,which launched the historic reform and opening up policy that transformed China,the CPC held another third plenary session that promises to be potentially as consequential.The resolution passed on 18 July during the third plenary session of the 20th Central Committee of the CPC calls for comprehensively deepening reforms,thus paving the way for the country to march towards the final goal of a developed and prosperous country.展开更多
图形用户界面(Graphical User Interface,GUI)领域的检索实践中,较多案例仍然采取使用单一分类号结合关键词或者仅仅使用关键词进行检索,需要不断扩展关键词,针对此情况,文章对比了GUI领域中IPC和CPC的分类特点,指出CPC分类号的分类优势...图形用户界面(Graphical User Interface,GUI)领域的检索实践中,较多案例仍然采取使用单一分类号结合关键词或者仅仅使用关键词进行检索,需要不断扩展关键词,针对此情况,文章对比了GUI领域中IPC和CPC的分类特点,指出CPC分类号的分类优势,结合GUI领域技术方案往往涉及多技术分支的特点以及该领域分类号分布特点,提出一种快速确定该领域技术方案分类号的方式,并结合具体案例,使用上述方式进行检索实践,可以快速准确地找到合适的对比文件。展开更多
A comprehensive assessment of representative satellite-retrieved(Integrated Multi-satellite Retrievals for Global Precipitation Measurement(IMERG)and Tropical Rainfall Measuring Mission Multi-satellite Precipitation A...A comprehensive assessment of representative satellite-retrieved(Integrated Multi-satellite Retrievals for Global Precipitation Measurement(IMERG)and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA)),reanalysis-based(fifth generation of atmospheric reanalysis by the European Centre for Medium Range Weather Forecasts(ERA5)),and gauge-estimated(Climate Prediction Center(CPC))precipitation products was conducted using the data from 807 meteorological stations across China's Mainland from 2001 to 2017.Error statistical metrics,precipitation distribution functions,and extreme precipitation indices were used to evaluate the quality of the four precipitation products in terms of multi-timescale accuracy and extreme precipitation estimation.When the timescale increased from daily to seasonal scales,the accuracy of the four precipitation products first increased and then decreased,and all products performed best on the monthly timescale.Their accuracy ranking in descending order was CPC,IMERG,TMPA,and ERA5 on the daily timescale and IMERG,CPC,TMPA,and ERA5 on the monthly and seasonal timescales.IMERG was generally superior to its predecessor TMPA on the three timescales.ERA5 exhibited large statistical errors.CPC provided stable estimated values.For extreme precipitation estimation,the quality of IMERG was relatively consistent with that of TMPA in terms of precipitation distribution and extreme metrics,and IMERG exhibited a significant advantage in estimating moderate and heavy precipitation.In contrast,ERA5 and CPC exhibited poor performance with large systematic underestimation biases.The findings of this study provide insight into the performance of the latest IMERG product compared with the widely used TMPA,ERA5,and CPC datasets,and points to possible directions for improvement of multi-source precipitation data fusion algorithms in order to better serve hydrological applications.展开更多
文摘Nearly five decades after the third plenary session of the 11th Central Committee of the Communist Party of China(CPC)in 1978,which launched the historic reform and opening up policy that transformed China,the CPC held another third plenary session that promises to be potentially as consequential.The resolution passed on 18 July during the third plenary session of the 20th Central Committee of the CPC calls for comprehensively deepening reforms,thus paving the way for the country to march towards the final goal of a developed and prosperous country.
文摘图形用户界面(Graphical User Interface,GUI)领域的检索实践中,较多案例仍然采取使用单一分类号结合关键词或者仅仅使用关键词进行检索,需要不断扩展关键词,针对此情况,文章对比了GUI领域中IPC和CPC的分类特点,指出CPC分类号的分类优势,结合GUI领域技术方案往往涉及多技术分支的特点以及该领域分类号分布特点,提出一种快速确定该领域技术方案分类号的方式,并结合具体案例,使用上述方式进行检索实践,可以快速准确地找到合适的对比文件。
基金supported by the National Natural Science Foundation of China(Grant No.51979069)the Fundamental Research Funds for the Central Universities(Grant No.B200204029)the National Natural Science Foundation of Jiangsu Province,China(Grant No.BK20211202).
文摘A comprehensive assessment of representative satellite-retrieved(Integrated Multi-satellite Retrievals for Global Precipitation Measurement(IMERG)and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA)),reanalysis-based(fifth generation of atmospheric reanalysis by the European Centre for Medium Range Weather Forecasts(ERA5)),and gauge-estimated(Climate Prediction Center(CPC))precipitation products was conducted using the data from 807 meteorological stations across China's Mainland from 2001 to 2017.Error statistical metrics,precipitation distribution functions,and extreme precipitation indices were used to evaluate the quality of the four precipitation products in terms of multi-timescale accuracy and extreme precipitation estimation.When the timescale increased from daily to seasonal scales,the accuracy of the four precipitation products first increased and then decreased,and all products performed best on the monthly timescale.Their accuracy ranking in descending order was CPC,IMERG,TMPA,and ERA5 on the daily timescale and IMERG,CPC,TMPA,and ERA5 on the monthly and seasonal timescales.IMERG was generally superior to its predecessor TMPA on the three timescales.ERA5 exhibited large statistical errors.CPC provided stable estimated values.For extreme precipitation estimation,the quality of IMERG was relatively consistent with that of TMPA in terms of precipitation distribution and extreme metrics,and IMERG exhibited a significant advantage in estimating moderate and heavy precipitation.In contrast,ERA5 and CPC exhibited poor performance with large systematic underestimation biases.The findings of this study provide insight into the performance of the latest IMERG product compared with the widely used TMPA,ERA5,and CPC datasets,and points to possible directions for improvement of multi-source precipitation data fusion algorithms in order to better serve hydrological applications.
