supported by the National Natural Science Foundation of China(Grant Nos.41405050,91437104&41461164006);the Public Welfare Scientific Research Projects in Meteorology(Grant No.GYHY201406013);the National Basic Research Program of China(Grant No.2014CB441402)
This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of c...
sponsored by the Special Fund for Meteorological Research in the Public Interest from the Ministry of Science and Technology of China(Grant No.GYHY201306004);the National Key Basic Research Program of China(Grant No.2013CB430104);the National Natural Science Foundation of China(Grant Nos.41461164006,41425018 & 41375048)
This study explores for the first time the impact of assimilating radial velocity(Vr)observations from a single or multiple Taiwan's coastal radars on tropical cyclone(TC)forecasting after landfall in the Chinese main...
supported by the Strategic Priority Research Program–Climate Change: Carbon Budget and Relevant Issues (XDA05040404);the National Natural Science Foundation of China (41130528);the National High Technology Research and Development Program of China (2013AA122002);the National Basic Research Program of China (2010CB428501);the Priority Academic Program Development of Jiangsu Higher Education Institutions
Under an Ensemble Kalman Filter(EnKF)framework,Regional Atmospheric Modeling System and Models-3 Community Multi-scale Air Quality(RAMS–CMAQ)modeling system is developed to be a CO2data assimilation system EnKF–CMAQ...
Project supported by the National Basic Research and Development Program of China (973 Program, Grant No.2011CB403306);the Ministry of Water Resources’ Special Funds for Scientific Research on Public Causes (Grant No.200901023);the Central Scientific Institutes Foundation for Public Service (Grant No. HKY-JBYW-2012-5)
In this paper, both state variables and parameters of one-dimensional open channel model are estimated using a framework of the Ensemble Kalman Filter (EnKF). Compared with observation, the predicted accuracy of wat...
the National Natural Science Foundation of China (Grant No. 40705035);the Knowledge Innovation Project of Chinese Academy of Sciences (Grant Nos. KZCX2-YW-217 and KZCX2-YW-126-2);the National Basic Research Program of China (Grant No.2005CB321704)
The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation ...