Open AccessA Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean
Author(s) -
Aldebert Clement,
Koenig Guillaume,
Baklouti Melika,
Fraunié Philippe,
Deve JeanLuc
Publication year - 2021
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2020ms002245
Subject(s) - identification (biology) , mixing (physics) , computer science , geophysics , geophysical fluid dynamics , statistical physics , mathematical optimization , geology , mathematics , physics , meteorology , botany , quantum mechanics , biology
Geophysical models make predictions relying on parameter values to be estimated from data. However, existing methods are costly because they require either many runs of the complex geophysical model or to implement an adjoint model. Here, we propose an alternative approach based on optimal control theory which is the simultaneous perturbations stochastic approximation (SPSA). This gradient‐descent method is generic and easy to implement, and its computational cost does not increase with the number of parameters to optimize. This study aims at highlighting the potential of SPSA for parameter identification in geophysical models. Through the example of vertical turbulent mixing in the upper ocean, we show with twin experiments that the method could successfully identify parameter values that minimize model‐data discrepancy. The efficient and easy‐to‐get results provided by SPSA in this study should pave the way for a broader use of parameter identification in the complex and embedded models commonly used in geophysical sciences.