Data Assimilation

Data assimilation is the process by which observations are incorporated into a computer model of a real system to provide an analysis of a current scenario and forecast future scenarios. Applications of data assimilation arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology.

Data assimilation proceeds by analysis cycles. Considering weather prediction as an example, in each analysis cycle, observations of the current (and possibly past) state of a system are combined with the results from a numerical weather prediction (NWP) model (the forecast) to produce an analysis, which is considered as 'the best' estimate of the current state of the system. This is called the analysis step. Essentially, the analysis step tries to balance the uncertainty in the data and in the forecast. The model is then advanced in time and its result becomes the forecast in the next analysis cycle.

Weather forecasting applications

Data assimilation is used for combining observations of variables like temperature and atmospheric pressure into numerical models to predict weather.

In weather forecasting there are 2 main types of data assimilation: 3 dimensional (3DDA) and 4 dimensional (4DDA). In 3DDA only those observations available at the time of analysis are used. In 4DDA the future observations are included (thus, time dimension added).

Future Development in NWP

The rapid development of the various data assimilation methods for NWP models is connected with the two main points in the field of numerical weather prediction:

1.      Utilizing the observations currently seems to be the most promising chance to improve the quality of the forecasts at the different spatial scales (from the planetary scale to the local city, or even street scale) and time scales.

2.      The number of different kinds of available observations (sodars, radars, satellite) is rapidly growing.

Other applications of Data Assimilation

Data assimilation methods are currently also used in other environmental forecasting problems, e.g. in hydrological forecasting.

Given the abundance of spacecraft data for other planets in the Solar System, data assimilation is now also applied beyond the Earth to obtain re-analyses of the atmospheric state of extraterrestrial planets. Mars is the first extraterrestrial planet which data assimilation has been applied to, so far.