4.4 Article

Assessing a New Coupled Data Assimilation System Based on the Met Office Coupled Atmosphere-Land-Ocean-Sea Ice Model

期刊

MONTHLY WEATHER REVIEW
卷 143, 期 11, 页码 4678-4694

出版社

AMER METEOROLOGICAL SOC
DOI: 10.1175/MWR-D-15-0174.1

关键词

Models and modeling; Coupled models; Data assimilation; Model evaluation; performance; Ocean models

资金

  1. European Commission MyOcean2 project
  2. U.K. Royal Navy

向作者/读者索取更多资源

A new coupled data assimilation (DA) system developed with the aim of improving the initialization of coupled forecasts for various time ranges from short range out to seasonal is introduced. The implementation here is based on a weakly coupled data assimilation approach whereby the coupled model is used to provide background information for separate ocean-sea ice and atmosphere-land analyses. The increments generated from these separate analyses are then added back into the coupled model. This is different from the existing Met Office system for initializing coupled forecasts, which uses ocean and atmosphere analyses that have been generated independently using the FOAM ocean data assimilation system and NWP atmosphere assimilation systems, respectively. A set of trials has been run to investigate the impact of the weakly coupled data assimilation on the analysis, and on the coupled forecast skill out to 5-10 days. The analyses and forecasts have been assessed by comparing them to observations and by examining differences in the model fields. Encouragingly for this new system, both ocean and atmospheric assessments show the analyses and coupled forecasts produced using coupled DA to be very similar to those produced using separate ocean-atmosphere data assimilation. This work has the benefit of highlighting some aspects on which to focus to improve the coupled DA results. In particular, improving the modeling and data assimilation of the diurnal SST variation and the river runoff should be examined.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据