GriddingMachine, a database and software for Earth system modeling at global and regional scales

Creators: Wang, Yujie; Köhler, Philipp; Braghiere, Renato K.; Longo, Marcos; Doughty, Russell; Bloom, A. Anthony; Frankenberg, Christian

Abstract

Land and Earth system modeling is moving towards more explicit biophysical representations, requiring increasing variety of datasets for initialization and benchmarking. However, researchers often have difficulties in identifying and integrating non-standardized datasets from various sources. We aim towards a standardized database and one-stop distribution method of global datasets. Here, we present the GriddingMachine as (1) a database of global-scale datasets commonly used to parameterize or benchmark the models, from plant traits to vegetation indices and geophysical information and (2) a cross-platform open source software to download and request a subset of datasets with only a few lines of code. The GriddingMachine datasets can be accessed either manually through traditional HTTP, or automatically using modern programming languages including Julia, Matlab, Octave, Python, and R. The GriddingMachine collections can be used for any land and Earth modeling framework and ecological research at the regional and global scales, and the number of datasets will continue to grow to meet the increasing needs of research communities.

Additional Information

© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 09 March 2022; Accepted 04 May 2022; Published 01 June 2022. We gratefully acknowledge the generous support of Eric and Wendy Schmidt (by recommendation of the Schmidt Futures) and the Heising-Simons Foundation. This research has been supported by the National Aeronautics and Space Administration (NASA) Earth Sciences Division grant NNX15AH95G and Carbon Cycle Science grant 80NSSC21K1712 awarded to Christian Frankenberg. Marcos Longo was supported by the NASA Postdoctoral Program, administered by Universities Space Research Association under contract with NASA. We acknowledge NASA for the publicly available datasets. The distributed datasets include modified Copernicus Climate Change Service Information [2020]. Neither the European Commission nor the European Centre for Medium-Range Weather Forecasts (ECMWF) are responsible for any use that may be made of the Copernicus information or data in this publication. We thank the data owners for generously sharing the datasets. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. California Institute of Technology. Government sponsorship acknowledged. Data availability: The compressed datasets can be downloaded from CaltechDATA. Code availability: The code can be found at https://github.com/CliMA/GriddingMachine.jl under the Apache 2.0 License. The exact version of the package used to produce the results presented in this paper is also archived on CaltechDATA along with the datasets. Contributions: Y.W. designed the code structure, wrote the code, and led the writing. All authors identified suitable datasets and contributed to the writing. The authors declare no competing interests.

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