Toward reliable ensemble Kalman filter estimates of CO2 fluxes
Toward reliable ensemble Kalman filter estimates of CO2 fluxes
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实现可靠的集成卡尔曼滤波器估计 CO2 通量
DOI：
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发表时间：
2012
期刊：
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通讯作者：
V. Yadav
中科院分区：
文献类型：
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作者：
A. Chatterjee;A. Michalak;Jeffrey L. Anderson;Kim L. Mueller;V. Yadav
[1]使用集合过滤器估计二氧化碳(CO2)的源和汇正变得越来越普遍，因为它们为同化二氧化碳的高密度观测提供了一个计算效率相对较高的框架。然而，它们在高分辨率下估计通量的适用性以及它们的估计与更传统的“批处理”反演方法的等价性尚未得到证明。在这项研究中，我们介绍了一个地质统计集成平方根过滤器(GEnSRF)作为一个原型过滤器，并使用北美地区的一个高空间(1��1�)和时间(3小时)分辨率的合成数据研究来检验它的性能。在估计和相关不确定性方面的集合性能以批量逆建模设置为基准，以便隔离和量化由于集合过滤器中的数值近似和参数选择而导致的估计中的降级。经过检验的案例研究表明，采用最先进的协方差膨胀和局部化方案是确保良好滤波性能的必要条件，但不是充分条件，其定义是其在一系列分辨率上产生可靠的通量估计和不确定性的能力。观测密度被发现是稳定集合性能的另一个关键因素，这归因于缺乏在同化时间之间演化集合的动力学模式。这一结果和其他结果指出了集合方法在碳循环科学中的适用性与其在最初开发这些工具的气象应用中的使用之间的关键差异。
[1] The use of ensemble filters for estimating sources and sinks of carbon dioxide (CO2) is becoming increasingly common, because they provide a relatively computationally efficient framework for assimilating high-density observations of CO2. Their applicability for estimating fluxes at high-resolutions and the equivalence of their estimates to those from more traditional “batch” inversion methods have not been demonstrated, however. In this study, we introduce a Geostatistical Ensemble Square Root Filter (GEnSRF) as a prototypical filter and examine its performance using a synthetic data study over North America at a high spatial (1 � � 1 � ) and temporal (3-hourly) resolution. The ensemble performance, both in terms of estimates and associated uncertainties, is benchmarked against a batch inverse modeling setup in order to isolate and quantify the degradation in the estimates due to the numerical approximations and parameter choices in the ensemble filter. The examined case studies demonstrate that adopting state-of-the-art covariance inflation and localization schemes is a necessary but not sufficient condition for ensuring good filter performance, as defined by its ability to yield reliable flux estimates and uncertainties across a range of resolutions. Observational density is found to be another critical factor for stabilizing the ensemble performance, which is attributed to the lack of a dynamical model for evolving the ensemble between assimilation times. This and other results point to key differences between the applicability of ensemble approaches to carbon cycle science relative to its use in meteorological applications where these tools were originally developed.