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
  • [41] pyCOFBuilder: A Python']Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach
    Oliveira, Felipe L.
    Esteves, Pierre M.
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2024, 64 (08) : 3278 - 3289
  • [42] NLMpy: a PYTHON']PYTHON software package for the creation of neutral landscape models within a general numerical framework
    Etherington, Thomas R.
    Holland, E. Penelope
    O'Sullivan, David
    METHODS IN ECOLOGY AND EVOLUTION, 2015, 6 (02): : 164 - 168
  • [43] How to Interpret Statistical Models Using marginaleffects for R and Python']Python
    Arel-Bundock, Vincent
    Greifer, Noah
    Heiss, Andrew
    JOURNAL OF STATISTICAL SOFTWARE, 2024, 111 (09): : 1 - 32
  • [44] Suggesting Comment Completions for Python']Python using Neural Language Models
    Ciurumelea, Adelina
    Proksch, Sebastian
    Gall, Harald C.
    PROCEEDINGS OF THE 2020 IEEE 27TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER '20), 2020, : 456 - 467
  • [45] reval: A Python']Python package to determine best clustering solutions with stability-based relative clustering validation
    Landi, Isotta
    Mandelli, Veronica
    Lombardo, Michael, V
    PATTERNS, 2021, 2 (04):
  • [46] AUTOMATIC GENERATION OF DIGITAL ELEVATION MODELS USING PYTHON']PYTHON SCRIPTS
    Dobesova, Zdena
    11TH INTERNATIONAL MULTIDISCIPLINARY SCIENTIFIC GEOCONFERENCE (SGEM 2011), VOL II, 2011, : 599 - 604
  • [47] MicrographCleaner: A python']python package for cryo-EM micrograph cleaning using deep learning
    Sanchez-Garcia, Ruben
    Segura, Joan
    Maluenda, David
    Sorzano, C. O. S.
    Carazo, J. M.
    JOURNAL OF STRUCTURAL BIOLOGY, 2020, 210 (03)
  • [48] SAIPy: A Python']Python package for single-station earthquake monitoring using deep learning
    Li, Wei
    Chakraborty, Megha
    Cartaya, Claudia Quinteros
    Koehler, Jonas
    Faber, Johannes
    Meier, Men-Andrin
    Ruempker, Georg
    Srivastava, Nishtha
    COMPUTERS & GEOSCIENCES, 2024, 192
  • [49] MSNoise, a Python']Python Package for Monitoring Seismic Velocity Changes Using Ambient Seismic Noise
    Lecocq, Thomas
    Caudroni, Corentin
    Brenguier, Florent
    SEISMOLOGICAL RESEARCH LETTERS, 2014, 85 (03) : 715 - 726
  • [50] Physcraper: a Python']Python package for continually updated phylogenetic trees using the Open Tree of Life
    Sanchez-Reyes, Luna L.
    Kandziora, Martha
    McTavish, Emily Jane
    BMC BIOINFORMATICS, 2021, 22 (01) : 355