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
  • [1] A Python']Python package for particle physics analyses
    Bevan, Adrian
    Charman, Thomas
    Hays, Jonathan
    23RD INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS (CHEP 2018), 2019, 214
  • [2] The qspec Python']Python package: A physics toolbox for laser spectroscopy
    Mueller, P.
    Noertershaeuser, W.
    COMPUTER PHYSICS COMMUNICATIONS, 2025, 311
  • [3] PyLCP: A Python']Python package for computing laser cooling physics
    Eckel, Stephen
    Barker, Daniel S.
    Norrgard, Eric B.
    Scherschligt, Julia
    COMPUTER PHYSICS COMMUNICATIONS, 2022, 270
  • [4] A cross-validation package driving Netica with python']python
    Fienen, Michael N.
    Plant, Nathaniel G.
    ENVIRONMENTAL MODELLING & SOFTWARE, 2015, 63 : 14 - 23
  • [5] GeospaceLAB: Python']Python package for managing and visualizing data in space physics
    Cai, Lei
    Aikio, Anita
    Kullen, Anita
    Deng, Yue
    Zhang, Yongliang
    Zhang, Shun-Rong
    Virtanen, Ilkka
    Vanhamaki, Heikki
    FRONTIERS IN ASTRONOMY AND SPACE SCIENCES, 2022, 9
  • [6] BARMPy: Bayesian additive regression models Python']Python package
    Van Boxel, Danielle
    COMPUTATIONAL STATISTICS, 2024,
  • [7] TrustML: A Python']Python package for computing the trustworthiness of ML models
    Manzano, Marti
    Ayala, Claudia
    Gomez, Cristina
    SOFTWAREX, 2024, 26
  • [8] pyrichlet: A Python']Python Package for Density Estimation and Clustering Using Gaussian Mixture Models
    Selva, Fidel
    Fuentes-Garcia, Ruth
    Gil-Leyva, Maria Fernanda
    JOURNAL OF STATISTICAL SOFTWARE, 2025, 112 (08): : 1 - 39
  • [9] THE FOAM PYTHON']PYTHON PACKAGE AND APPLICATIONS TO OCEAN SALINITY MISSION ARCHITECTURE STUDIES
    Akins, Alex B.
    Brown, Shannon T.
    Misra, Sidharth
    Lee, Tong
    Yueh, Simon
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 6753 - 6756
  • [10] yggdrasil: a Python']Python package for integrating computational models across languages and scales
    Lang, Meagan
    IN SILICO PLANTS, 2019, 1 (01):