Abstract
Atmospheric fronts are associated with precipitation and strong diabatic processes. Therefore, detecting fronts objectively from reanalyses is a prerequisite for the long-term study of their weather impacts. For this purpose, several algorithms exist, e.g., based on the thermal front parameter (TFP) or the F diagnostic that combines relative vorticity and horizontal temperature gradient. It is shown that both methods have problems to identify weak warm fronts since they are characterized by low baroclinicity. To avoid this inaccuracy, a new algorithm is developed that considers fronts as deviation from an adiabatic and steady state. These deviations can be accurately measured using the dynamic state index (DSI). The DSI shows a coherent dipole structure along fronts and is strongly correlated with precipitation sums. Using the DSI, a new front detection algorithm is developed (called DSI method), which allows to clearly identify the global storm track regions. The properties of the identified fronts depend on the applied front detection method, whereby fronts identified with the DSI method have particularly high specific humidity. Using a simple estimate for front speed, it is shown that also the front speed depends on the front detection method and that fronts identified using the DSI method have a higher front speed than fronts identified with the TFP method. This can be attributed to the dipole structure of the DSI and thus demonstrates the potential of the DSI to inherently indicate the movement speed and direction in atmospheric flows.