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.

数据同化过程通常用于估计系统的最佳初始状态，以提高未来状态预测的准确性。这项功能强大的技术已广泛应用于大气，海洋和陆地表面的研究。在这项工作中，我们将处理高斯卷积运算，这是数据同化方法的核心步骤，也是一些数据分析程序中的重要步骤。特别地，我们考虑使用递归滤波器来近似高斯卷积。在[1]中，我们提出了一种加速的一阶递归滤波器，以一种非常快速的方式来计算高斯卷积核。我们介绍了理论和结果，并提供了一种基于三阶递归滤波器的新的GPU并行实现。要观察性能方面的好处，