predicting the quality of water and air is a particular challenge for forecasting systems that support them. in order to represent the small-scale phenomena, a high-resolution model needs accurate capture of air and sea circulations, significant for forecasting environmental pollution. data assimilation is one of the state of the art methods to be used for this purpose. due to the importance of...

In this paper, we propose Deep Data Assimilation (DDA), an integration of (DA) with Machine Learning (ML). DA is the Bayesian approximation true state some physical system at a given time by combining time-distributed observations dynamic model in optimal way. We use ML order to learn assimilation process. particular, recurrent neural network, trained dynamical and results process, applied for ...

Data Assimilation aims at estimating the posterior conditional probability density functions based on error statistics of noisy observations and dynamical system. State art methods are sub-optimal due to common use Gaussian linearization non-linear dynamics. To achieve a good performance, these often require case-by-case fine-tuning by using explicit regularization techniques such as inflation ...

Data assimilation refers to the statistical techniques used to combine numerical and statistical models with observations to give an improved estimate of the state of a system or process. Typically a data assimilation problem has a sequential aspect where data as it becomes available over time is used to update the state or parameters of a dynamical system. Data assimilation is usually distingu...

Abstract Increasing model resolution can improve the performance of a data assimilation system because it reduces error, more optimally use high-resolution observations, and with an ensemble method forecast error covariances are improved. However, increasing scales cubical increase computational costs. A that effectively is introduced here. The novel approach called “Super-resolution assimilati...