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Neural Network as Transformation Function in Data Assimilation

EasyChair Preprint no. 13742

8 pagesDate: July 2, 2024


Variational Data Assimilation (DA) is a technique aimed at mitigating the error in simulated states by integrating observations. Variational DA is widely employed in weather forecasting and hydrological modeling as an optimization technique for refining dynamic simulation states. However, when constructing the cost function in variational DA, it is necessary to establish a transformation function from simulated states to observations. When observations come from ground sensors or from remote sensing, representing such a transformation function with explicit expressions can sometimes be challenging or even impossible. Therefore, considering the strong mapping capabilities of Neural Network (NN)s in representing the relationship from simulated states to observations, this paper proposes a method utilizing a NN as the transformation function. We evaluated our method on a real dataset of river discharge in the UK and achieved a 13% enhancement in prediction accuracy, measured by Mean Square Error (MSE), compared to the results obtained without DA.

Keyphrases: Mapping, neural network, Variational data assimilation

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Kun Wang and Matthew D. Piggott and Yanghua Wang and Rossella Arcucci},
  title = {Neural Network as Transformation Function in Data Assimilation},
  howpublished = {EasyChair Preprint no. 13742},

  year = {EasyChair, 2024}}
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