| description abstract | A collection of eight operational global analyses over a 27-month period have been processed to common data structures to facilitate comparisons among the analyses and global observational datasets. The present study evaluated the global precipitation, outgoing longwave radiation (OLR) at the top of the atmosphere, and basin-scale precipitation over the United States. In addition, a multimodel ensemble was created from a linear average of the available data, as close to the analysis time as each system permitted. The results show that the monthly global precipitation and OLR from the multimodel ensemble compares generally better to the observations than any single analysis. Likewise, the daily precipitation from the ensemble exhibits better statistical comparison (in space and time) to gauge observations over the Mississippi River basin. However, the comparisons have seasonality, when the members of the ensemble exhibit generally more skill, during winter. There is notably higher skill of the summertime basin precipitation by the ensemble. Using the global precipitation and OLR, the sensitivity was tested to selectively choose the members with the best statistical comparisons to the reference data. Only small improvements in the statistics were found when comparing a selective ensemble to the full ensemble. Additionally, terms of the global energy budget were compared among the ensemble and to other estimates. The ensemble data and the variance of the ensemble should make a useful point of comparison for the development of model and assimilation components of global analyses. | |
