Met Office MOGREPS‐G initialisation using an ensemble of hybrid four‐dimensional ensemble variational (En‐4DEnVar) data assimilations
Met Office MOGREPS‐G initialisation using an ensemble of hybrid four‐dimensional ensemble variational (En‐4DEnVar) data assimilations
复制标题
使用混合四维系综变分 (En-4DEnVar) 数据同化系综进行气象局 MOGREPS-G 初始化
DOI：
10.1002/qj.4431
复制
发表时间：
2023
影响因子：
8.9
通讯作者：
M. Wlasak
中科院分区：
文献类型：
--
作者：
G. Inverarity;W. Tennant;L. Anton;N. Bowler;A. Clayton;M. Jardak;A. Lorenc;F. Rawlins;S. Thompson;M. Thurlow;D. Walters;M. Wlasak
英国气象局全球和区域包围圈预测系统（MOGREPS-G）使用集合变换卡尔曼滤波器（ETKF）从2008年9月到2019年12月的运行实施中扰动其初始条件。2019年，MOGREPS-G成为第一个将混合四维集合变分数据同化（En-4DEnVar）应用于44个扰动集合成员中的每一个的业务大气集合。还增加了其他增强功能，包括用于改善整体传播的通货膨胀。这些变化对集合预报的综合影响是非常积极的，但最初对确定性预报更为中性，确定性预报也使用集合来表示混合数据同化更新中的流量依赖预报误差。后一个结果并不令人意外，因为确定性预报的混合数据同化最初对建模的平稳协方差分量进行了更强的加权，并且没有进行优化以充分利用升级后的集合。2020年12月的后续操作升级除了滞后之外还引入了转移，以通过包括来自上一个周期的集合成员以及来自相邻预报提前期的集合成员来更好地利用集合，从而在确定性预报的混合数据同化中更好地利用集合，以增强集合，而无需运行额外的预报。此后，在2022年5月确定性预报的混合数据同化中，对集合给予了更多的权重。在MOGREPS-G中采用混合4DEnVar的一个关键动机是通过共享大部分确定性预报系统的数据同化代码来减少维护开销。这也使集合同化确定性预报所使用的几乎所有观测类型。更新后的系统还更好地利用了并行性，以便足够快地用于业务使用，尽管比气象局的ETKF吸收了更多的观测数据，并且计算成本更高。
The Met Office Global and Regional Ensemble Prediction System–Global (MOGREPS‐G) used an ensemble transform Kalman filter (ETKF) to perturb its initial conditions from its operational implementation in September 2008 until December 2019. In 2019, MOGREPS‐G became the first operational atmospheric ensemble to apply hybrid four‐dimensional ensemble variational data assimilation (En‐4DEnVar) to each of the 44 perturbed ensemble members. Other enhancements have also been added, including to the inflation used to improve ensemble spread. The combined impact of these changes on ensemble forecasts is overwhelmingly positive but initially more neutral for deterministic forecasts, which also use the ensemble to represent flow‐dependent forecast errors in their hybrid data assimilation updates. The latter result is not a surprise, because the deterministic forecast's hybrid data assimilation was initially weighted more strongly to the modelled stationary covariance component and not optimised to take full advantage of the upgraded ensemble. A subsequent operational upgrade in December 2020 has introduced shifting in addition to lagging to exploit the ensemble better in the deterministic forecast's hybrid data assimilation by including ensemble members from a previous cycle and also from adjacent forecast lead times to augment the ensemble without having to run additional forecasts. More weight has since been given to the ensemble in the deterministic forecast's hybrid data assimilation in May 2022. A key motive for adopting hybrid 4DEnVar in MOGREPS‐G is to reduce maintenance overheads by virtue of sharing much of the deterministic forecast system's data assimilation code. This also enables the ensemble to assimilate almost all observation types used by the deterministic forecast. The updated system also exploits parallelism better so as to be fast enough for operational use, despite assimilating more observations and being more computationally expensive than the Met Office's ETKF.
影响因子：
2.2
作者：
Baker L
通讯作者：
Baker L
DOI：
10.5194/gmd-2016-194-supplement
发表时间：
2017
期刊：
--
影响因子：
--
作者：
D. Walters;I. Boutle;M. Brooks;T. Melvin;R. Stratton;S. Vosper;H. Wells;K. Williams;N. Wood
通讯作者：
D. Walters;I. Boutle;M. Brooks;T. Melvin;R. Stratton;S. Vosper;H. Wells;K. Williams;N. Wood
用于同化湿度和云敏感观测的湿度增量算子：公式和初步结果
影响因子：
8.9
作者：
Migliorini, Stefano;Lorenc, Andrew C.;Bell, William
通讯作者：
Bell, William