Recursive filter based GPU algorithms in a Data Assimilation scenario

Data Assimilation process is generally used to estimate the best initial state of a system in order to improve accuracy of future states prediction. This powerful technique has been widely applied in investigations of the atmosphere, ocean, and land surface. In this work, we deal with the Gaussian convolution operation which is a central step of the Data Assimilation approach, as well as in several data analysis procedures. In particular, we consider the use of recursive filters to approximate the Gaussian convolution. In [1] we presented an accelerated first-order recursive filter to compute the Gaussian convolution kernel, in a very fast way. We present theory and results, and we provide a new GPU-parallel implementation which is based on the third order recursive filter. To observe the benefits in terms of performance, tests and experiments complete our work.

Automated summary

Data assimilation is commonly used to improve prediction accuracy by estimating the best initial state of a system. This study focuses on the Gaussian convolution operation within the data assimilation approach, presenting an accelerated recursive filter and a new GPU-parallel implementation based on third-order recursive filter. Tests and experiments are conducted to evaluate the performance benefits.