Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models
Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models
复制标题
检查观测系统实验（OSE）和伴随模型产生的观测影响
DOI：
10.1111/j.1600-0870.2008.00388.x
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Yanqiu Zhu
中科院分区：
文献类型：
--
作者：
R. Gelaro;Yanqiu Zhu
摘要利用资料同化系统的伴随，可以准确、有效地估计任一或全部同化观测对预报技术指标的影响。该方法允许在单个数据类型、通道或位置方面聚合结果，所有这些都同时计算。在这项研究中，伴随的观测影响的估计进行了比较，从标准观测系统实验（OSE）使用美国航天局GEOS-5大气数据同化系统的前向和伴随版本的结果。尽管重要的基本差异的方式观测的影响，在两种方法进行测量，结果表明，他们提供了一致的估计的总体影响，大多数主要的观测系统，在减少干总能量度量的24小时预测误差在地球仪和热带外，在较小的程度上，在热带地区。然而，同样重要的是，有人认为，这两种方法提供了独特的，但互补的，关于观测数值天气预报的影响的信息。此外，当一起使用时，它们揭示了观测系统影响之间的冗余性和依赖性，因为观测被添加或从数据同化系统中删除。了解这些依赖关系似乎构成了一个重要的挑战，使最佳利用全球观测系统的数值天气预报。
Abstract With the adjoint of a data assimilation system, the impact of any or all assimilated observations on measures of forecast skill can be estimated accurately and efficiently. The approach allows aggregation of results in terms of individual data types, channels or locations, all computed simultaneously. In this study, adjoint-based estimates of observation impact are compared with results from standard observing system experiments (OSEs) using forward and adjoint versions of the NASA GEOS-5 atmospheric data assimilation system. Despite important underlying differences in the way observation impacts are measured in the two approaches, the results show that they provide consistent estimates of the overall impact of most of the major observing systems in reducing a dry total-energy metric of 24-h forecast error over the globe and extratropics and, to a lesser extent, over the tropics. Just as importantly, however, it is argued that the two approaches provide unique, but complementary, information about the impact of observations on numerical weather forecasts. Moreover, when used together, they reveal both redundancies and dependencies between observing system impacts as observations are added or removed from the data assimilation system. Understanding these dependencies appears to pose an important challenge in making optimal use of the global observing system for numerical weather prediction.