edu.ucar.opensky::technotes:574 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-25T15:42:41.132572 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d75x2cxd Parallel Implementation of the Multi-resolution Approximation for Large-scale Spatial Gaussian Models in Python 2018-12-27T00:00:00Z publication https://n2t.org/ark:/85065/d75x2cxd https://doi.org/10.26024/c04h-fd33 Jurek, Marcin author Hammerling, Dorit author UCAR/NCAR - Library publisher Gaussian processes have become a standard tool for modeling large atmospheric data sets. Traditional methods used in this context, such as kriging, are computationally infeasible for many data sets encountered in practice due to their size. One solution to overcome this limitation is to employ the multi-resolution approximation (MRA), developed by. In this technical report, we describe a Python package containing a parallel implementation of the MRA algorithm for estimating the true value of the process from noisy data. We describe the algorithm and its implementation, apply it to several simulated data sets and report run times. We also include code examples that cover typical use cases of the algorithm as well as some diagnostic tools to verify its results. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright Author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation