  This paper aims to introduce an application to Kalman Filtering Theory, which
is rather unconventional. Recent experiments have shown that many natural
phenomena, especially from ecology or meteorology, could be monitored and
predicted more accurately when accounting their evolution over some
geographical area. Thus, the signals they provide are gathered together into a
collection of distributed time series. Despite the common sense, such time
series are more or less correlated each other. Instead of processing each time
series independently, their collection can constitute the set of measurable
states provided by some open system. Modeling and predicting the system states
can take benefit from the family of Kalman filtering algorithms. The article
describes an adaptation of basic Kalman filter to the context of distributed
signals collections and completes with an application coming from Meteorology.
