陆表自然和人文要素相互作用——“全球变化及应对”重点专项研究进展

在国家重点研发计划支持下,项目提出了陆表不均一性检测和订正的新方法,解决了渐变型不均一性检测和订正的难题,构建了中国地表太阳辐射、气温、地温、风速和降水等参数均一化站点和格点数据集,修订了关于中国地表风速变化趋势、增温格局及其形成机制的结论。融合多源数据,构建并验证了千米级、流域级或县域级的电厂、人口、生物质能、取水量、氮排放、二氧化碳排放等影响自然系统的关键人文要素历史和未来预估数据集。构建了未来关键人文要素情景,研制了碳中和目标下甲烷和氧化亚氮排放情景和用于驱动全球模式的未来情景,预估了中国碳中和战略的实施对全球变暖的减缓作用,发现中国碳中和对远期和中期全球变暖的减缓作用显著。给出了中国各省份水体氮排放安全阈值及超越时间,阐明了中国粮食产量与氮施肥的关系,提出了在保障粮食安全的前提下减少水体氮排放的有效途径,指出重构城乡养分循环体系是同时保障粮食安全和恢复水质的必要途径。发现全球饱和水汽压差的年际变化与大气二氧化碳浓度上升速率的年际变化显著相关,阐明了饱和水汽压差变化在调控生态系统生产力中的重要角色以及多因素耦合作用在生态系统生产力变化中的复杂影响。建议更全面细致地评估中国各种碳中...     

Using analysis state to construct a forecast error covariance matrix in ensemble Kalman filter assimilation

Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme. If it is not correctly estimated, the assimilated states could be far from the true states. A popular method to address this problem is error covariance matrix inflation. That is, to multiply the forecast error covariance matrix by an appropriate factor. In this paper, analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed. The proposed assimilation scheme was tested on the Lorenz-96 model and 2D Shallow Water Equation model, both of which are associated with spatially correlated observational systems. The experiments showed that by introducing the proposed structure of the forecast error covariance matrix and applying its adaptive estimation procedure, the assimilation results were further improved.