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首页> 外文期刊>Nonlinear processes in geophysics >Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction
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Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction

机译：全球确定性天气预报的四维整体变化数据同化

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The goal of this study is to evaluate a version of the ensemble-variational data assimilation approach (EnVar) for possible replacement of 4D-Var at Environment Canada for global deterministic weather prediction. This implementation of EnVar relies on 4-D ensemble covariances, obtained from an ensemble Kalman filter, that are combined in a vertically dependent weighted average with simple static covariances. Verification results are presented from a set of data assimilation experiments over two separate 6-week periods that used assimilated observations and model configuration very similar to the currently operational system. To help interpret the comparison of EnVar versus 4D-Var, additional experiments using 3D-Var and a version of EnVar with only 3-D ensemble covariances are also evaluated. To improve the rate of convergence for all approaches evaluated (including EnVar), an estimate of the cost function Hessian generated by the quasi-Newton minimization algorithm is cycled from one analysis to the next. Analyses from EnVar (with 4-D ensemble covariances) nearly always produce improved, and never degraded, forecasts when compared with 3D-Var. Comparisons with 4D-Var show that forecasts from EnVar analyses have either similar or better scores in the troposphere of the tropics and the winter extra-tropical region. However, in the summer extra-tropical region the medium-range forecasts from EnVar have either similar or worse scores than 4D-Var in the troposphere. In contrast, the 6 h forecasts from EnVar are significantly better than 4D-Var relative to radiosonde observations for both periods and in all regions. The use of 4-D versus 3-D ensemble covariances only results in small improvements in forecast quality. By contrast, the improvements from using 4D-Var versus 3D-Var are much larger. Measurement of the fit of the background and analyzed states to the observations suggests that EnVar and 4D-Var can both make better use of observations distributed over time than 3D-Var. In summary, the results from this study suggest that the EnVar approach is a viable alternative to 4D-Var, especially when the simplicity and computational efficiency of EnVar are considered. Additional research is required to understand the seasonal dependence of the difference in forecast quality between EnVar and 4D-Var in the extra-tropics.
机译：这项研究的目的是评估集成变分数据同化方法（EnVar）的一种版本，以可能替代加拿大环境部的4D-Var进行全球确定性天气预报。 EnVar的此实现依赖于从整体Kalman滤波器获得的4-D整体协方差，这些4D整体协方差与垂直相关的加权平均值和简单的静态协方差组合在一起。验证结果来自两个单独的6周周期内的数据同化实验集，这些实验使用的同化观测值和模型配置与当前操作系统非常相似。为了帮助解释EnVar与4D-Var的比较，还评估了使用3D-Var和仅具有3-D集合协方差的EnVar版本的其他实验。为了提高所有评估方法（包括EnVar）的收敛速度，将准牛顿最小化算法生成的成本函数Hessian的估计从一个分析循环到另一个分析。 与3D-Var相比，EnVar（具有4D集合协方差）的分析几乎总能产生改进的预测，而不会降低其预测。与4D-Var的比较表明，EnVar分析得出的预报在热带的对流层和冬季热带外地区的得分相似或更好。但是，在夏季热带地区，EnVar的中程预报在对流层中的得分与4D-Var相似或差。相比之下，EnVar的6小时预报相对于两个时期和所有地区的探空仪观测结果都明显好于4D-Var。使用4-D与3-D集成协方差只会导致预测质量的小幅提高。相比之下，使用4D-Var和3D-Var所带来的改进要大得多。测量背景和分析状态与观测值的契合度表明，与3D-Var相比，EnVar和4D-Var都可以更好地利用随时间分布的观测。总而言之，这项研究的结果表明，EnVar方法是4D-Var的可行替代方案，尤其是在考虑了EnVar的简单性和计算效率的情况下。需要额外的研究来了解温带地区EnVar和4D-Var之间预测质量差异的季节依赖性。

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