Using the COAsT Python']Python package to develop a standardised validation workflow for ocean physics models

被引:0
|
作者
Byrne, David [1 ]
Polton, Jeff [1 ]
O'Dea, Enda [2 ]
Williams, Joanne [1 ]
机构
[1] Natl Oceanog Ctr, Liverpool, England
[2] Met Off, Exeter, England
基金
英国自然环境研究理事会;
关键词
CONFIGURATION;
D O I
10.5194/gmd-16-3749-2023
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Validation is one of the most important stages of a model's development. By comparing outputs to observations, we can estimate how well the model isable to simulate reality, which is the ultimate aim of many models. During development, validation may be iterated upon to improve the modelsimulation and compare it to similar existing models or perhaps previous versions of the same configuration. As models become more complex, datastorage requirements increase and analyses improve, scientific communities must be able to develop standardised validation workflows for efficientand accurate analyses with an ultimate goal of a complete, automated validation. We describe how the Coastal Ocean Assessment Toolbox (COAsT) Python package has been used to develop a standardised and partially automated validation system. This is discussedalongside five principles which are fundamental for our system: system scaleability, independence from data source, reproducible workflows, expandablecode base and objective scoring. We also describe the current version of our own validation workflow and discuss how it adheres to the aboveprinciples. COAsT provides a set of standardised oceanographic data objects ideal for representing both modelled and observed data. We use the packageto compare two model configurations of the Northwest European Shelf to observations from tide gauge and profiles.
引用
收藏
页码:3749 / 3764
页数:16
相关论文
共 50 条
  • [31] Modelly: An open source all in one python']python package for developing machine learning models
    Sarkar, Tushar
    Shah, Disha
    SOFTWARE IMPACTS, 2022, 14
  • [32] ECMpy 2.0: A Python']Python package for automated construction and analysis of enzyme-constrained models
    Mao, Zhitao
    Niu, Jinhui
    Zhao, Jianxiao
    Huang, Yuanyuan
    Wu, Ke
    Yun, Liyuan
    Guan, Jirun
    Yuan, Qianqian
    Liao, Xiaoping
    Wang, Zhiwen
    Ma, Hongwu
    SYNTHETIC AND SYSTEMS BIOTECHNOLOGY, 2024, 9 (03) : 494 - 502
  • [33] GMES: A Python']Python package for solving Maxwell's equations using the FDTD method
    Chun, Kyungwon
    Kim, Huioon
    Kim, Hyounggyu
    Jung, Kil Su
    Chung, Youngjoo
    COMPUTER PHYSICS COMMUNICATIONS, 2013, 184 (04) : 1272 - 1279
  • [34] Computer Programs Physics pyMCD: Python']Python package for searching transition states via the multicoordinate driven method
    Lee, Kyunghoon
    Kim, Jun Hyeong
    Kim, Woo Youn
    COMPUTER PHYSICS COMMUNICATIONS, 2023, 291
  • [35] Grammar-aware phrase dataset generated using a novel python']python package
    Gemechu, Ebisa A.
    Kanagachidambaresan, G. R.
    DATA IN BRIEF, 2023, 48
  • [36] orsum: a Python']Python package for filtering and comparing enrichment analyses using a simple principle
    Ozisik, Ozan
    Terezol, Morgane
    Baudot, Anais
    BMC BIOINFORMATICS, 2022, 23 (01)
  • [37] MSIFinder: a python']python package for detecting MSI status using random forest classifier
    Zhou, Tao
    Chen, Libin
    Guo, Jing
    Zhang, Mengmeng
    Zhang, Yanrui
    Cao, Shanbo
    Lou, Feng
    Wang, Haijun
    BMC BIOINFORMATICS, 2021, 22 (01)
  • [38] Latte: Cross-framework Python']Python package for evaluation of latent-based generative models
    Watcharasupat, Karn N.
    Lee, Junyoung
    Lerch, Alexander
    SOFTWARE IMPACTS, 2022, 11
  • [39] LLM-IE: a python']python package for biomedical generative information extraction with large language models
    Hsu, Enshuo
    Roberts, Kirk
    JAMIA OPEN, 2025, 8 (02)
  • [40] PsychRNN: An Accessible and Flexible Python']Python Package for Training Recurrent Neural Network Models on Cognitive Tasks
    Ehrlich, Daniel B.
    Stone, Jasmine T.
    Brandfonbrener, David
    Atanasov, Alexander
    Murray, John D.
    ENEURO, 2021, 8 (01) : 1 - 11