Clean Energy Research:data assimilation
data assimilation
data assimilation |
Data assimilation means integration of data from different sources. The purpose of data assimilation is to determine a best possible atmospheric state using observations and short range forecasts. Data is received from different sources, such as satellite instruments, weather stations, ships, buoys, and other components. Data is combined in order to have more reliable 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. Data assimilation is used also to monitor climate change based on past observations.
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.Erikieliset vastineet
data-assimilaatio | suomi (suomi) |
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Tieteen termipankki 15.11.2024: Clean Energy Research:data assimilation. (Tarkka osoite: https://tieteentermipankki.fi/wiki/Clean Energy Research:data assimilation.)