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Assimilating the LAI Data to the VEGAS Model Using the Local Ensemble Transform Kalman Filter: An Observing System Simulation Experiment 
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下载免费PDF全文 Information on the spatial and temporal pat- terns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents the construction and implementation of a terrestrial carbon cycle data assimilation system based on a dynamic vegetation and terrestrial carbon model Vegetation-Global-Atmosphere-Soil （VEGAS） with an advanced assimilation algorithm, the local ensemble transform Kalman filter （LETKF, hereafter LETKF-VEGAS）. An observing system simulation experiment （OSSE） framework was designed to evaluate the reliability of this system, and numerical experiments conducted by the OSSE using leaf area index （LAI） observations suggest that the LETKF -VEGAS can improve the estimations of leaf carbon pool and LAI significantly, with reduced root mean square errors and increased correlation coefficients with true values, as compared to a control run without assimilation. Furthermore, the LETKF-VEGAS has the potential to provide more accurate estimations of the net primary productivity （NPP） and carbon flux to atmosphere （CFta）. 相似文献
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集合变换卡尔曼滤波(ensemble transform Kalman filter, ETKF)是一种有效的集合预报初始扰动构造方案。但是,有限的集合样本、相同的集合成员设置以及预报模式误差等可能会使两个距离较远的状态变量产生虚假相关,从而影响ETKF集合扰动的质量。为了有效解决远距离虚假相关问题,将局地化思想引入ETKF方案。本文针对GRAPES区域集合预报系统(GRAPES REPS),对ETKF初值扰动局地化方案的效果进行了试验分析,为进一步改善和优化局地化方案(LETKF方案)提供依据。通过一周的连续试验,从暴雨个例、集合预报多种评分检验等方面分析了LETKF初始扰动方案所产生的集合预报质量。结果表明,区域集合预报中集合变换卡尔曼滤波初始扰动的局地化方案能够更加合理地捕捉到快速增长的分析误差的物理结构,更准确地再现数值模式预报误差的线性与非线性传播和演变特征。该局地化方案可以较好地改进预报质量,提高降水预报的准确率,尤其是针对小雨、中雨、暴雨量级的预报。相对于现有区域集合预报的业务系统GRAPES REPS,基于局地化ETKF初始扰动方案的区域集合预报具有较明显的优势。总体来看,LETKF初始扰动方案可更好地改善区域集合预报的质量。 相似文献
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在Liu and Kalnay (2008) 的研究基础上, 将基于集合的观测资料影响性评价方法(简称LK08法)运用到一个简单的大气环流模式中, 对模拟探空资料的预报影响性进行了综合评价, 考察了LK08法在真实大气环流模式上的适用性。研究结果表明, 应用基于集合的评价方法可以一次性计算出同化系统中每个观测的影响性, 然后按观测手段、观测区域等进行影响性数值的简单累加, 以此可以比较不同类型观测的相对影响性。比较结果显示, 不同半球的模拟探空观测对预报的总影响性相差不大, 但由于南半球资料个数要远远少于北半球, 因此, 南半球单个观测的影响性要大于北半球的单个观测。不同观测类型对预报的总影响性也不相同。有效性验证分析表明, 按LK08法计算得到的总体观测影响性能解释实际影响性的70%～80%, 且很好地抓住了其变化和走势。 相似文献
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Improved Analyses and Forecasts With AIRS Temperature Retrievals Using the Local Ensemble Transform Kalman Filter 总被引：1，自引：1，他引：0
In this paper we investigate the impact of the Atmospheric Infra-Red Sounder (AIRS) temperature retrievals on data assimilation and the resulting forecasts using the four-dimensional Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme and a reduced resolution version of the NCEP Global Forecast System (GFS). Our results indicate that the AIRS temperature retrievals have a significant and consistent positive impact in the Southern Hemispheric extratropics on both analyses and forecasts, which is found not only in the temperature field but also in other variables. In tropics and the Northern Hemispheric extratropics these impacts are smaller, but are still generally positive or neutral. 相似文献
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The ensemble based forecast sensitivity to observation method by Liu and Kalnay is applied to the SPEEDY-LETKF system to estimate the observation impact of three types of simulated observations. The estimation results show that all types of observations have positive impact on short-range forecast. The largest impact in Northern Hemisphere is produced by rawinsondes, followed by satellite retrieved profiles and cloud drift wind data, which in Southern Hemisphere is produced by satellite retrieved profiles, rawinsondes and cloud drift wind data. Satellite retrieved profiles influence more on the Southern Hemisphere than on the Northern Hemisphere due to few observations from rawinsondes in the Southern Hemisphere. At the level of 200 to 300 hPa, the largest impact is attributed to wind observations from rawinsondes and cloud drift wind. 相似文献
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