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1. Introduction Consensus forecasts for meteorological events were operationally used in the pioneering studies of Toth and Kalnay (1993 1997 ), Molteni et al. (1996) , Houtekamer et al. (1996) , and Goerss (2000) . Krishnamurti et al. (1999) introduced the notion of a multimodel superensemble (MMSE) to combine multimodel forecast datasets using a linear multiple regression approach that utilized the mean-square error reduction principle. Studies reported on the efficiency of this
1. Introduction Consensus forecasts for meteorological events were operationally used in the pioneering studies of Toth and Kalnay (1993 1997 ), Molteni et al. (1996) , Houtekamer et al. (1996) , and Goerss (2000) . Krishnamurti et al. (1999) introduced the notion of a multimodel superensemble (MMSE) to combine multimodel forecast datasets using a linear multiple regression approach that utilized the mean-square error reduction principle. Studies reported on the efficiency of this
described in this paper to develop a statistically significant database of actual error specifications derived from laboratory testing. Because laboratory testing is not available at this time, it is impossible to differentiate between pure instrument error and atmospheric variability. Here we seek only to determine the overall variability of the coupled system as a first step toward full analysis of instrument sensitivity and measurement uncertainty. As indicated above, the hundreds of instruments
described in this paper to develop a statistically significant database of actual error specifications derived from laboratory testing. Because laboratory testing is not available at this time, it is impossible to differentiate between pure instrument error and atmospheric variability. Here we seek only to determine the overall variability of the coupled system as a first step toward full analysis of instrument sensitivity and measurement uncertainty. As indicated above, the hundreds of instruments
