Ensemble data assimilation with the NCEP Global Forecast System
Ensemble data assimilation with the NCEP Global Forecast System
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DOI：
10.1175/2007mwr2018.1
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发表时间：
2008-02-01
影响因子：
3.2
通讯作者：
Toth, Zoltan
中科院分区：
文献类型：
--
作者：
Whitaker, Jeffrey S.;Hamill, Thomas M.;Toth, Zoltan
利用NCEP全球预报系统模式进行了集合同化系统的实际资料试验，并与NCEP全球资料同化系统（GDAS）进行了比较。除卫星辐射率外，对2004年1月1日至2月10日期间业务数据流中的所有观测数据都进行了同化。由于计算资源的限制，比较是在较低的分辨率（三角形截断波数62，28级）比GDAS实时NCEP业务运行（三角形截断波数254，64级）。集合数据同化系统的性能优于NCEP三维变分数据同化系统（3DVAR）的降低分辨率版本，在数据稀疏区域的改善最大。相对于NCEP 3DVAR系统（集合数据同化系统的48小时预报与3DVAR系统的24小时预报一样准确），集合数据同化分析在南半球热带外的预报技巧方面取得了24小时的改进。数据丰富的北方半球的改善虽然在统计上仍然显著，但更为温和。在同化卫星辐射时，南半球的改善是否会得到保留，还有待观察。三种不同的参数化的数据同化系统中未考虑的背景误差（包括模式误差）进行了测试。将NCEP-NCAR再分析的相邻6小时分析之间的缩放随机差异添加到每个集合成员（添加剂膨胀）的表现略好于其他两种方法（乘法膨胀和松弛先验）。
Real-data experiments with an ensemble data assimilation system using the NCEP Global Forecast System model were performed and compared with the NCEP Global Data Assimilation System (GDAS). All observations in the operational data stream were assimilated for the period 1 January-10 February 2004, except satellite radiances. Because of computational resource limitations, the comparison was done at lower resolution ( triangular truncation at wavenumber 62 with 28 levels) than the GDAS real-time NCEP operational runs ( triangular truncation at wavenumber 254 with 64 levels). The ensemble data assimilation system outperformed the reduced-resolution version of the NCEP three-dimensional variational data assimilation system ( 3DVAR), with the biggest improvement in data-sparse regions. Ensemble data assimilation analyses yielded a 24-h improvement in forecast skill in the Southern Hemisphere extratropics relative to the NCEP 3DVAR system ( the 48-h forecast from the ensemble data assimilation system was as accurate as the 24-h forecast from the 3DVAR system). Improvements in the data-rich Northern Hemisphere, while still statistically significant, were more modest. It remains to be seen whether the improvements seen in the Southern Hemisphere will be retained when satellite radiances are assimilated. Three different parameterizations of background errors unaccounted for in the data assimilation system ( including model error) were tested. Adding scaled random differences between adjacent 6-hourly analyses from the NCEP-NCAR reanalysis to each ensemble member ( additive inflation) performed slightly better than the other two methods ( multiplicative inflation and relaxation-to-prior).