To make a forecast we need to know the current state of the atmosphere and the Earth's surface (land and oceans). The weather forecasts produced at ECMWF use data assimilation to estimate initial conditions for the forecast model from meteorological observations.
ECMWF is a world leader in data assimilation research and development. The quality of our forecasts depends on how well we use information received in real-time from the global observing system, which consists of numerous satellite instruments, weather stations, ships, buoys, and other components.
The purpose of data assimilation is to determine a best possible atmospheric state using observations and short range forecasts. Data assimilation is typically a sequential time-stepping procedure, in which a previous model forecast is compared with newly received observations, the model state is then updated to reflect the observations, a new forecast is initiated, and so on. The update step in this process is usually referred to as the analysis; the short model forecast used to produce the analysis is called the background.
We also use data assimilation to monitor climate change based on past observations – this is called reanalysis.
The atmosphere is chaotic, meaning that even small differences in its state can lead to very different weather patterns occurring several days later – this is sometimes referred to as the butterfly effect. To account for the chaotic nature of the atmosphere and the associated uncertainty in prediction, we run an ensemble of 51 forecasts simultaneously; the forecast using the best possible initial state plus 50 other forecasts with slight variations to the initial state. Our ensembles provide a probabilistic forecast which is an estimate of how predictable a particular weather situation is.
Land data assimilation
The ECMWF data assimilation system also includes a specific Land Data Assimilation System to initialise the land surface model variables.