Synoptic and mesoscale meteorology underwent a revolution in the 1940s and 1950s with the widespread deployment of novel weather observations, such as the radiosonde network and the advent of weather radar. These observations provoked a rapid increase in our understanding of the structure and dynamics of the atmosphere by pioneering analysts such as Fred Sanders. I will argue that we may be approaching an analogous revolution in our ability to study the structure and dynamics of atmospheric phenomena with the advent of probabilistic objective analyses. These probabilistic analyses provide not only best-estimates of the state of the atmosphere (e.g., the expected value) and the uncertainty about this state (e.g., the variance), but also the relationships between all locations and all variables at that instant in time. Up until now, these relationships have been determined by sampling methods (e.g., case studies and composites) and time-series analysis.

Probabilistic analyses may soon be provided by ensemble-based state-estimation methods such as the ensemble Kalman filter (EnKF). Currently, we have the odd situation where ensembles provide probabilistic forecasts based on deterministic analyses. The EnKF offers the possibility to relax this restriction and move toward fully probabilistic state estimation and forecasting. Ensemble synoptic analysis may then be realized and used to study atmospheric phenomena and their relationships. For example, one could diagnose the relationship between surface fronts and tropopause disturbances, or tropical sea-surface temperature anomalies and extratropical planetary waves. After a brief overview of a research-based EnKF, illustrative examples of ensemble synoptic analysis will be given, including statistically determined operators for potential-vorticity inversion.