Improving the joint estimation of CO2 and surface carbon fluxes using a Constrained Ensemble Kalman Filter in COLA (v1.0)
Improving the joint estimation of CO2 and surface carbon fluxes using a Constrained Ensemble Kalman Filter in COLA (v1.0)
复制标题
使用 COLA (v1.0) 中的约束集成卡尔曼滤波器改进 CO2 和表面碳通量的联合估计
DOI：
10.5194/gmd-2021-375
复制
发表时间：
2021
影响因子：
5.1
通讯作者：
P. Han
中科院分区：
文献类型：
--
作者：
Zhiqiang Liu;N. Zeng;Yun Liu;E. Kalnay;G. Asrar;Bo Wu;Qixiang Cai;Di Liu;P. Han
抽象。在过去的二十年里，大气二氧化碳（CO2）反演测量以了解碳源和汇取得了很大的进展。然而，大多数的研究，包括四维变分（4D-Var），Enhancement卡尔曼滤波（EnKF），贝叶斯合成方法，直接获得的通量，而CO2浓度与前向模型推导出作为后分析。Kang等人（2012年）使用局部集合变换卡尔曼滤波器（LETKF）同时更新CO2、表面碳通量（SCF）和气象场。沿着这条轨道，开发了一个具有短同化窗口和长观测窗口的系统（Liu等人，2019年）。然而，这一系统面临着维持全球碳质量的挑战。为了克服这一缺点，在这里，我们引入了一个约束包围卡尔曼滤波（CEnKF）的方法，以确保全球CO2质量的守恒。在一个标准的LETKF程序之后，一个额外的同化过程被应用于调整CO2在每个模型网格点，并确保全球CO2质量的分析和第一次猜测之间的一致性。在观测系统模拟实验（OSSEs）的背景下，我们通过比较有无CEnKF的实验结果表明，CEnKF可以将全球SCF的年偏差从约0.20亿吨显著降低到0.06亿吨以下，而且在大多数大陆区域的年偏差也得到了降低.在季节尺度上，改进后的系统减少了48- 90%，根据大陆地区的通量均方根误差从先验分析。此外，2015-2016年厄尔尼诺的影响被很好地捕捉到，异常主要发生在热带地区。
Abstract. Atmospheric inversion of carbon dioxide (CO2) measurements to understand carbon sources and sinks has made great progress over the last two decades. However, most of the studies, including four-dimension variational (4D-Var), Ensemble Kalman filter (EnKF), and Bayesian synthesis approaches, obtains directly only fluxes while CO2 concentration is derived with the forward model as post-analysis. Kang et al. (2012) used the Local Ensemble Transform Kalman Filter (LETKF) that updates the CO2, surface carbon fluxes (SCF), and meteorology field simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this system faces the challenge of maintaining global carbon mass. To overcome this shortcoming, here we introduce a Constrained Ensemble Kalman Filter (CEnKF) approach to ensure the conservation of global CO2 mass. After a standard LETKF procedure, an additional assimilation process is applied to adjust CO2 at each model grid point and to ensure the consistency between the analysis and the first guess of global CO2 mass. In the context of observing system simulation experiments (OSSEs), we show that the CEnKF can significantly reduce the annual global SCF bias from ~0.2 gigaton to less than 0.06 gigaton by comparing between experiments with and without it. Moreover, the annual bias over most continental regions is also reduced. At the seasonal scale, the improved system reduced the flux root-mean-square error from priori to analysis by 48–90 %, depending on the continental region. Moreover, the 2015–2016 El Nino impact is well captured with anomalies mainly in the tropics.
Ensembleâtype Kalman 滤波器算法守恒质量、总能量和熵
影响因子：
8.9
作者：
T. Janjic;Y. Ruckstuhl;M. Verlaan
通讯作者：
M. Verlaan
DOI：
10.5194/acpd-8-19917-2008
发表时间：
2009
期刊：
--
影响因子：
--
作者：
L. Feng;P. Palmer;H. Bösch;S. Dance
通讯作者：
L. Feng;P. Palmer;H. Bösch;S. Dance