Variational methods for predicting climate-environmental processes with assimilation of observational data

机译：通过观测数据同化预测气候-环境过程的变分方法

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Prospective issues of the organization of modeling technology for studying climatic and environmental processes and solving practical problems are discussed. We study the problems of predictability analysis and uncertainty estimates. To this goal, the combination of process models and observational data is carried out within the framework of the variational principle with weak constraints. This makes it possible to obtain the direct non-iterative algorithms for estimating the state and uncertainty functions.

机译：讨论了用于研究气候和环境过程并解决实际问题的建模技术组织的预期问题。我们研究可预测性分析和不确定性估计的问题。为此，在具有弱约束的变分原理框架内将过程模型与观测数据结合起来。这使得获得用于估计状态和不确定性函数的直接非迭代算法成为可能。

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《International symposium on atmospheric and ocean optics: atmospheric physics》|2017年|104665Q.1-104665Q.5|共5页
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Vladimir V. Penenko;
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mathematical modeling; data assimilation; forecasting; uncertainty assessment; predictability; variational approach;

机译：数学建模;数据同化;预测;不确定性评估;可预测性变分法;

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Variational methods for predicting climate-environmental processes with assimilation of observational data

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