The purpose of this paper is to demonstrate the ability of a modern data assimilation system to provide long-term diagnostic facilities to monitor the performance of the observational network. Operational data assimilation systems use short-range forecasts to provide the background, or first-guess, field for the analysis. We make a detailed study of the apparent or perceived error of these forecasts when they are verified against radiosondes. On the assumption that the observational error of the radiosondes is horizontally uncorrelated, the perceived forecast error can be partitioned into prediction error, which is horizontally correlated, and observation error, which is not. The calculations show that in areas where there is adequate radiosonde coverage, the 6-hour prediction error is comparable with the observation error. This statement is discussed from a number of viewpoints. We demonstrate in the Northern Hemisphere midlatitudes, for example, that the forecasts account for most of the evolut... Abstract The purpose of this paper is to demonstrate the ability of a modern data assimilation system to provide long-term diagnostic facilities to monitor the performance of the observational network. Operational data assimilation systems use short-range forecasts to provide the background, or first-guess, field for the analysis. We make a detailed study of the apparent or perceived error of these forecasts when they are verified against radiosondes. On the assumption that the observational error of the radiosondes is horizontally uncorrelated, the perceived forecast error can be partitioned into prediction error, which is horizontally correlated, and observation error, which is not. The calculations show that in areas where there is adequate radiosonde coverage, the 6-hour prediction error is comparable with the observation error. This statement is discussed from a number of viewpoints. We demonstrate in the Northern Hemisphere midlatitudes, for example, that the forecasts account for most of the evolut...