Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol applied to Earth system models

Mahadevan, Vijay S.; Guerra, Jorge E.; Jiao, Xiangmin; Kuberry, Paul; Li, Yipeng; Ullrich, Paul; Marsico, David; Jacob, Robert; Bochev, Pavel; Jones, Philip

doi:10.5194/gmd-15-6601-2022

Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol applied to Earth system models

Journal Article · 02 September 2022 · Geoscientific Model Development (Online)

DOI:https://doi.org/10.5194/gmd-15-6601-2022· OSTI ID:1885196

^[1]; ^[2]; Jiao, Xiangmin ^[3]; Kuberry, Paul ^[4]; Li, Yipeng ^[3]; ^[5]; Marsico, David ^[5]; ^[1]; Bochev, Pavel ^[4]; Jones, Philip ^[6]

Argonne National Lab. (ANL), Lemont, IL (United States)
NOAA National Severe Storms Lab., Norman, OK (United States)
Stony Brook Univ., NY (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Univ. of California, Davis, CA (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Strongly coupled nonlinear phenomena such as those described by Earth system models (ESMs) are composed of multiple component models with independent mesh topologies and scalable numerical solvers. A common operation in ESMs is to remap or interpolate component solution fields defined on their computational mesh to another mesh with a different combinatorial structure and decomposition, e.g., from the atmosphere to the ocean, during the temporal integration of the coupled system. Several remapping schemes are currently in use or available for ESMs. However, a unified approach to compare the properties of these different schemes has not been attempted previously. We present a rigorous methodology for the evaluation and intercomparison of remapping methods through an independently implemented suite of metrics that measure the ability of a method to adhere to constraints such as grid independence, monotonicity, global conservation, and local extrema or feature preservation. A comprehensive set of numerical evaluations is conducted based on a progression of scalar fields from idealized and smooth to more general climate data with strong discontinuities and strict bounds. We examine four remapping algorithms with distinct design approaches, namely ESMF Regrid (Hill et al., 2004), TempestRemap (Ullrich and Taylor, 2015), generalized moving least squares (GMLS) (Trask and Kuberry, 2020) with post-processing filters, and WLS-ENOR (Li et al., 2020). By repeated iterative application of the high-order remapping methods to the test fields, we verify the accuracy of each scheme in terms of their observed convergence order for smooth data and determine the bounded error propagation using challenging, realistic field data on both uniform and regionally refined mesh cases. In addition to retaining high-order accuracy under idealized conditions, the methods also demonstrate robust remapping performance when dealing with non-smooth data. There is a failure to maintain monotonicity in the traditional L2-minimization approaches used in ESMF and TempestRemap, in contrast to stable recovery through nonlinear filters used in both meshless GMLS and hybrid mesh-based WLS-ENOR schemes. Local feature preservation analysis indicates that high-order methods perform better than low-order dissipative schemes for all test cases. The behavior of these remappers remains consistent when applied on regionally refined meshes, indicating mesh-invariant implementations. The MIRA intercomparison protocol proposed in this paper and the detailed comparison of the four algorithms demonstrate that the new schemes, namely GMLS and WLS-ENOR, are competitive compared to standard conservative minimization methods requiring computation of mesh intersections. The work presented in this paper provides a foundation that can be extended to include complex field definitions, realistic mesh topologies, and spectral element discretizations, thereby allowing for a more complete analysis of production-ready remapping packages.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: NA0003525; KP1703020; KJ0403000

OSTI ID:: 1885196

Alternate ID(s):: OSTI ID: 1877121

Report Number(s):: SAND2022-8283J; 707437

Journal Information:: Geoscientific Model Development (Online), Vol. 15, Issue 17; ISSN 1991-9603

Publisher:: Copernicus Publications, EGUCopyright Statement

Country of Publication:: United States

Language:: English

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