What is the concept of data assimilation?

Data assimilation is a strong technique which has been widely applied in researches of the atmosphere, ocean, and land surface. Data assimilation is a way to integrate the data from variety of sources with different resolutions and accuracies with model prediction to improve deterministic model accuracy (McLaughlin et al., 2005). Data assimilation has been applied to chaotic dynamical systems that are too difficult to predict using simple extrapolation methods. The cause of this difficulty is that little changes in initial conditions can cause to huge changes in prediction accuracy. In other words, data assimilation is used to not only update the hydrological model states that optimally combine model outputs with observation, but also to quantify observational and different model errors.

Data assimilation is a procedure developed to optimally merge information from model simulations and independent observations with appropriate modelling (Liu et al., 2012). Data assimilation systems interpolate and extrapolate the remote sensing observations and provide perfect estimates at the scales required by the application, in time dimension and as well in the spatial dimension. Data assimilation systems thereby organize the useful and redundant observational information into physically consistent estimates of the variables of relevance to data users (Reichle, 2008).

Data assimilation combines, in an objective way, information from observations with information from a model of the evolving system (e.g. atmosphere, land), taking account of errors in the observations and model (Nichols 2009). It is the cornerstone of Numerical Weather Prediction (NWP) where it has contributed to improved models, improved use of observations and significantly improved forecasts (Simmons and Hollingsworth 2002).

Data assimilation techniques have become more popular over the last decade in modelling and forecasting large systems due to the ever increasing computational resources. The aim of data assimilation is to characterize 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, and a new forecast is initiated, and so on.

Reference In Data assimilation

1- Liu, Y., et al., 2012. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrology and Earth System Sciences, 16 (10), 3863-3887. Doi: 10.5194/hess-16-3863-2012

2- McLaughlin, D.; O'Neill, A.; Derber, J.; Kamachi, M. Opportunities for enhanced collaboration within the data assimilation community. Quarterly Journal of the Royal Meteorological Society 2005, 131, 3683-3693.

3- Nichols, N.K., 2009. Mathematical concepts of data assimilation. In Data Assimilation: Making sense of observations, Eds. W.A. Lahoz, B. Khattatov and R. Menard, Springer, due April 2009.

4- Reichle, R. H., 2008. Data assimilation methods in the Earth sciences. Advances in Water Resources 31 (2008) 1411-1418

5- Simmons, A.J. and Hollingsworth, A., 2002: Some aspects of the improvement in skill in numerical weather prediction. QJRMS, 128, 547-677.