Technical Note: A novel approach to estimation of time-variable surface sources and sinks of carbon dioxide using empirical orthogonal functions and the Kalman filter

R.Zhuravlev; B.Khattatov; B.Kiryushov; S.Maksyutov

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Technical Note: A novel approach to estimation of time-variable surface sources and sinks of carbon dioxide using empirical orthogonal functions and the Kalman filter

机译：技术说明：一种使用经验正交函数和卡尔曼滤波器估算二氧化碳的时变表面源和汇的新颖方法

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In this work we propose an approach to solving a source estimation problembased on representation of carbon dioxide surface emissions as a linearcombination of a finite number of pre-computed empirical orthogonalfunctions (EOFs). We used National Institute for Environmental Studies(NIES) transport model for computing response functions and Kalman filterfor estimating carbon dioxide emissions. Our approach produces resultssimilar to these of other models participating in the TransCom3 experiment.Using the EOFs we can estimate surface fluxes at higher spatial resolution,while keeping the dimensionality of the problem comparable with that in theregions approach. This also allows us to avoid potentially artificial sharpgradients in the fluxes in between pre-defined regions. EOF resultsgenerally match observations more closely given the same error structure asthe traditional method.Additionally, the proposed approach does not require additional effort ofdefining independent self-contained emission regions.

机译：在这项工作中，我们提出了一种基于二氧化碳表面排放量表示形式的解决源估计问题的方法，该方法是将有限数量的预先计算的经验正交函数（EOF）进行线性组合。我们使用美国国家环境研究所（NIES）的运输模型来计算响应函数，并使用卡尔曼滤波器来估算二氧化碳排放量。我们的方法产生的结果与参与TransCom3实验的其他模型的结果相似。使用EOF，我们可以在更高的空间分辨率下估算表面通量，同时使问题的维度与区域方法可比。这也使我们能够避免在预定义区域之间的通量中潜在的人为锐利梯度。在给出与传统方法相同的误差结构的情况下，EOF结果通常与观测值更接近。此外，该方法不需要定义独立的独立发射区域的额外工作。

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《Atmospheric chemistry and physics》 |2011年第20期|共11页
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R.Zhuravlev; B.Khattatov; B.Kiryushov; S.Maksyutov;
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中图分类大气科学（气象学）;
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1. Technical Note: A novel approach to estimation of time-variable surface sources and sinks of carbon dioxide using empirical orthogonal functions and the Kalman filter [J] . Zhuravlev R., Khattatov B., Kiryushov B., Atmospheric chemistry and physics . 2011,第20期

机译：技术说明：一种使用经验正交函数和卡尔曼滤波器估算二氧化碳的时变表面源和汇的新颖方法
2. Technical Note: A novel approach to estimation of time-variable surface sources and sinks of carbon dioxide using empirical orthogonal functions and the Kalman filter [J] . Zhuravlev R., Khattatov B., Kiryushov B., Atmospheric Chemistry and Physics Discussions . 2011,第20期

机译：技术说明：一种使用经验正交函数和卡尔曼滤波器估算二氧化碳的时变表面源和汇的新颖方法
3. Distributed Kalman filtering for Time-Space Gaussian Processes * * This work is supported by Progetto di Ateneo CPDA147754/14-New statistical learning approach for multi-agents adaptive estimation and coverage control. [J] . M. Todescato, A. Dalla Libera, R. Carli, IFAC PapersOnLine . 2017,第1期

机译：时空高斯过程的分布式卡尔曼滤波 * * Progetto di Ateneo CPDA147754 / 14支持这项工作-新的统计学习多主体自适应估计和覆盖控制的方法。
4. Unscented Kalman filter for urban link travel time estimation with mid-link sinks and sources [C] . Hage R.-M., Betaille D., Peyret F., 2012 15th International IEEE Conference on Intelligent Transportation Systems. . 2012

机译：Unscented Kalman滤波器用于估计带有中间链路汇和源的城市链路旅行时间
5. A Kalman filtering approach to wideband scattering function estimation and updating. [D] . Roan, Michael James. 1999

机译：宽带散射函数估计和更新的卡尔曼滤波方法。
6. A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems [O] . Muhammad Adeel Akram, Peilin Liu, Muhammad Owais Tahir, 2019

机译：基于卡尔曼滤波和鲁棒估计理论的高度非线性系统多源信息融合状态优化模型
7. Technical Note: A novel approach to estimation of time-variable surface sources and sinks of carbon dioxide using empirical orthogonal functions and the Kalman filter [O] . R. Zhuravlev, B. Khattatov, B. Kiryushov, 2011

机译：技术说明：一种使用经验正交函数和卡尔曼滤波器估算二氧化碳的时变表面源和汇的新颖方法
8. The Energy Budget at the Earth'S Surface: Assessing Sources and Sinks of Carbon Dioxide in a Corn Crop Using a Momentum Balance Approach [R] . Lemon, E. R., Wright, J. L. 1967

机译：地球表面的能源预算：使用动量平衡法评估玉米作物中二氧化碳的来源和汇集

Technical Note: A novel approach to estimation of time-variable surface sources and sinks of carbon dioxide using empirical orthogonal functions and the Kalman filter

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